From e920885676c6e7019fdd2891b2173aa630d54c6b Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 2 Sep 2017 12:46:01 -0500
Subject: [PATCH 001/213] Fix pickle during train

---
 spacy/cli/train.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index ddec2c069..b2c87d2b5 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -80,6 +80,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
     n_train_words = corpus.count_train()
 
     optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
+    nlp._optimizer = None
 
     print("Itn.\tLoss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
     try:

From 33fa91feb7fa81a3346b8d34ac927c1578bc719b Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 4 Sep 2017 21:19:30 +0200
Subject: [PATCH 002/213] Restore correctness of parser model

---
 spacy/syntax/nn_parser.pyx | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 34e504da9..2aaae4f05 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -393,8 +393,7 @@ cdef class Parser:
 
         tokvecs = self.model[0].ops.flatten(tokvecses)
         if USE_FINE_TUNE:
-            # TODO: This is incorrect! Unhack when training next model
-            tokvecs += self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
+            tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
 
         nr_state = len(docs)
         nr_class = self.moves.n_moves
@@ -532,8 +531,8 @@ cdef class Parser:
             docs = [docs]
             golds = [golds]
         if USE_FINE_TUNE:
-            my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
-            tokvecs += self.model[0].ops.flatten(my_tokvecs)
+            tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
+            tokvecs = self.model[0].ops.flatten(tokvecs)
 
         cuda_stream = get_cuda_stream()
 
@@ -606,8 +605,8 @@ cdef class Parser:
         assert min(lengths) >= 1
         tokvecs = self.model[0].ops.flatten(tokvecs)
         if USE_FINE_TUNE:
-            my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
-            tokvecs += self.model[0].ops.flatten(my_tokvecs)
+            tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
+            tokvecs = self.model[0].ops.flatten(tokvecs)
 
         states = self.moves.init_batch(docs)
         for gold in golds:

From 5384fff5ceaa694109b6a6efa790877aabd5be7e Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 6 Sep 2017 18:40:18 +0200
Subject: [PATCH 003/213] Add test for 1305: Incorrect lemmatization of VBZ for
 English

---
 spacy/tests/regression/test_issue1305.py | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 spacy/tests/regression/test_issue1305.py

diff --git a/spacy/tests/regression/test_issue1305.py b/spacy/tests/regression/test_issue1305.py
new file mode 100644
index 000000000..e123ce0ba
--- /dev/null
+++ b/spacy/tests/regression/test_issue1305.py
@@ -0,0 +1,8 @@
+import pytest
+
+@pytest.mark.models('en')
+def test_issue1305(EN):
+    '''Test lemmatization of English VBZ'''
+    assert EN.vocab.morphology.lemmatizer('works', 'verb') == set(['work'])
+    doc = EN(u'This app works well')
+    assert doc[2].lemma_ == 'work'

From 497a9308a8775b51c79621bfe5f03aab1bf6696c Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 6 Sep 2017 18:41:22 +0200
Subject: [PATCH 004/213] Xfail new lemmatizer test

---
 spacy/tests/regression/test_issue1305.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/spacy/tests/regression/test_issue1305.py b/spacy/tests/regression/test_issue1305.py
index e123ce0ba..c75b42434 100644
--- a/spacy/tests/regression/test_issue1305.py
+++ b/spacy/tests/regression/test_issue1305.py
@@ -1,5 +1,6 @@
 import pytest
 
+@pytest.mark.xfail
 @pytest.mark.models('en')
 def test_issue1305(EN):
     '''Test lemmatization of English VBZ'''

From dd9cab0fafe0c9df863a5f539303b1bcfa4f1feb Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 6 Sep 2017 19:03:05 +0200
Subject: [PATCH 005/213] Fix type-check for int/long

---
 spacy/syntax/transition_system.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/syntax/transition_system.pyx b/spacy/syntax/transition_system.pyx
index 9cf82e0c7..055129c8b 100644
--- a/spacy/syntax/transition_system.pyx
+++ b/spacy/syntax/transition_system.pyx
@@ -148,7 +148,7 @@ cdef class TransitionSystem:
 
     def add_action(self, int action, label_name):
         cdef attr_t label_id
-        if not isinstance(label_name, int):
+        if not isinstance(label_name, (int, long)):
             label_id = self.strings.add(label_name)
         else:
             label_id = label_name

From 5c3ff069242804da4aba48ec6d09777eb91f74b9 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 6 Sep 2017 19:13:24 +0200
Subject: [PATCH 006/213] Fix lemmatizer rules

---
 spacy/lemmatizer.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/spacy/lemmatizer.py b/spacy/lemmatizer.py
index 4d534b50f..3a04a471d 100644
--- a/spacy/lemmatizer.py
+++ b/spacy/lemmatizer.py
@@ -25,6 +25,7 @@ class Lemmatizer(object):
         elif univ_pos == PUNCT:
             univ_pos = 'punct'
         # See Issue #435 for example of where this logic is requied.
+        print("Check base form", string)
         if self.is_base_form(univ_pos, morphology):
             return set([string.lower()])
         lemmas = lemmatize(string, self.index.get(univ_pos, {}),
@@ -38,7 +39,8 @@ class Lemmatizer(object):
         avoid lemmatization entirely.
         """
         morphology = {} if morphology is None else morphology
-        others = [key for key in morphology if key not in (POS, 'number', 'pos', 'verbform')]
+        others = [key for key in morphology
+                  if key not in (POS, 'Number', 'POS', 'VerbForm', 'Tense')]
         true_morph_key = morphology.get('morph', 0)
         if univ_pos == 'noun' and morphology.get('Number') == 'sing':
             return True
@@ -47,7 +49,9 @@ class Lemmatizer(object):
         # This maps 'VBP' to base form -- probably just need 'IS_BASE'
         # morphology
         elif univ_pos == 'verb' and (morphology.get('VerbForm') == 'fin' and \
-                                     morphology.get('Tense') == 'pres'):
+                                     morphology.get('Tense') == 'pres' and \
+                                     morphology.get('Number') is None and \
+                                     not others):
             return True
         elif univ_pos == 'adj' and morphology.get('Degree') == 'pos':
             return True

From 99e44fbdbbb34f33f3a02890cec03bea2b0a4b74 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 6 Sep 2017 19:13:51 +0200
Subject: [PATCH 007/213] Update regression test

---
 spacy/tests/regression/test_issue429.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/spacy/tests/regression/test_issue429.py b/spacy/tests/regression/test_issue429.py
index 1baa9a1db..74f12bd9f 100644
--- a/spacy/tests/regression/test_issue429.py
+++ b/spacy/tests/regression/test_issue429.py
@@ -9,11 +9,14 @@ import pytest
 @pytest.mark.models('en')
 def test_issue429(EN):
     def merge_phrases(matcher, doc, i, matches):
-      if i != len(matches) - 1:
-        return None
-      spans = [(ent_id, ent_id, doc[start:end]) for ent_id, start, end in matches]
-      for ent_id, label, span in spans:
-        span.merge('NNP' if label else span.root.tag_, span.text, EN.vocab.strings[label])
+        if i != len(matches) - 1:
+            return None
+        spans = [(ent_id, ent_id, doc[start:end]) for ent_id, start, end in matches]
+        for ent_id, label, span in spans:
+            span.merge(
+                tag=('NNP' if label else span.root.tag_),
+                lemma=span.text,
+                label='PERSON')
 
     doc = EN('a')
     matcher = Matcher(EN.vocab)

From 456bb8a74c384bcef58d562525c5ce914b0bed76 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 6 Sep 2017 19:14:17 +0200
Subject: [PATCH 008/213] Unxfail and close #1305

---
 spacy/tests/regression/test_issue1305.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/spacy/tests/regression/test_issue1305.py b/spacy/tests/regression/test_issue1305.py
index c75b42434..e123ce0ba 100644
--- a/spacy/tests/regression/test_issue1305.py
+++ b/spacy/tests/regression/test_issue1305.py
@@ -1,6 +1,5 @@
 import pytest
 
-@pytest.mark.xfail
 @pytest.mark.models('en')
 def test_issue1305(EN):
     '''Test lemmatization of English VBZ'''

From b8e81daccfd0ccf1388a7538ffcd9e6489e8d9ec Mon Sep 17 00:00:00 2001
From: Ines Montani <ines.montani@gmail.com>
Date: Thu, 14 Sep 2017 12:49:59 +0200
Subject: [PATCH 009/213] Fix typo (closes #1312)

---
 website/docs/usage/customizing-tokenizer.jade | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/usage/customizing-tokenizer.jade b/website/docs/usage/customizing-tokenizer.jade
index 7e0b4b479..0bc81771d 100644
--- a/website/docs/usage/customizing-tokenizer.jade
+++ b/website/docs/usage/customizing-tokenizer.jade
@@ -282,7 +282,7 @@ p
         def __call__(self, text):
             words = text.split(' ')
             # All tokens 'own' a subsequent space character in this tokenizer
-            spaces = [True] * len(word)
+            spaces = [True] * len(words)
             return Doc(self.vocab, words=words, spaces=spaces)
 
 p

From ba23d63c35bf9187f093804f93af4fd345cfa1e3 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 14 Sep 2017 13:37:41 +0200
Subject: [PATCH 010/213] Fix minibatch function, for fixed batch size

---
 spacy/gold.pyx | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/spacy/gold.pyx b/spacy/gold.pyx
index f00d04109..fc8d6622b 100644
--- a/spacy/gold.pyx
+++ b/spacy/gold.pyx
@@ -7,6 +7,7 @@ import re
 import ujson
 import random
 import cytoolz
+import itertools
 
 from .syntax import nonproj
 from .util import ensure_path
@@ -146,9 +147,13 @@ def minibatch(items, size=8):
     '''Iterate over batches of items. `size` may be an iterator,
     so that batch-size can vary on each step.
     '''
+    if isinstance(size, int):
+        size_ = itertools.repeat(8)
+    else:
+        size_ = size
     items = iter(items)
     while True:
-        batch_size = next(size) #if hasattr(size, '__next__') else size
+        batch_size = next(size_)
         batch = list(cytoolz.take(int(batch_size), items))
         if len(batch) == 0:
             break

From 9cb2aef5877b342ef44cd77386328ee91039088e Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 14 Sep 2017 13:38:28 +0200
Subject: [PATCH 011/213] Remove print statement

---
 spacy/lemmatizer.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/spacy/lemmatizer.py b/spacy/lemmatizer.py
index 3a04a471d..312c8db72 100644
--- a/spacy/lemmatizer.py
+++ b/spacy/lemmatizer.py
@@ -25,7 +25,6 @@ class Lemmatizer(object):
         elif univ_pos == PUNCT:
             univ_pos = 'punct'
         # See Issue #435 for example of where this logic is requied.
-        print("Check base form", string)
         if self.is_base_form(univ_pos, morphology):
             return set([string.lower()])
         lemmas = lemmatize(string, self.index.get(univ_pos, {}),

From 683d81bb49096867f5ad8d3dde23217ea54d6790 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 14 Sep 2017 16:15:59 +0200
Subject: [PATCH 012/213] Update example for adding entity type

---
 examples/training/train_new_entity_type.py | 87 ++++++++++------------
 1 file changed, 40 insertions(+), 47 deletions(-)

diff --git a/examples/training/train_new_entity_type.py b/examples/training/train_new_entity_type.py
index 4eae11c75..ab69285a6 100644
--- a/examples/training/train_new_entity_type.py
+++ b/examples/training/train_new_entity_type.py
@@ -25,7 +25,7 @@ For more details, see the documentation:
 * Saving and loading models: https://spacy.io/docs/usage/saving-loading
 
 Developed for: spaCy 1.7.6
-Last tested for: spaCy 1.7.6
+Last updated for: spaCy 2.0.0a13
 """
 from __future__ import unicode_literals, print_function
 
@@ -34,55 +34,41 @@ from pathlib import Path
 import random
 
 import spacy
-from spacy.gold import GoldParse
-from spacy.tagger import Tagger
+from spacy.gold import GoldParse, minibatch
+from spacy.pipeline import NeuralEntityRecognizer
+from spacy.pipeline import TokenVectorEncoder
 
 
+def get_gold_parses(tokenizer, train_data):
+    '''Shuffle and create GoldParse objects'''
+    random.shuffle(train_data)
+    for raw_text, entity_offsets in train_data:
+        doc = tokenizer(raw_text)
+        gold = GoldParse(doc, entities=entity_offsets)
+        yield doc, gold
+
+ 
 def train_ner(nlp, train_data, output_dir):
-    # Add new words to vocab
-    for raw_text, _ in train_data:
-        doc = nlp.make_doc(raw_text)
-        for word in doc:
-            _ = nlp.vocab[word.orth]
     random.seed(0)
-    # You may need to change the learning rate. It's generally difficult to
-    # guess what rate you should set, especially when you have limited data.
-    nlp.entity.model.learn_rate = 0.001
-    for itn in range(1000):
-        random.shuffle(train_data)
-        loss = 0.
-        for raw_text, entity_offsets in train_data:
-            gold = GoldParse(doc, entities=entity_offsets)
-            # By default, the GoldParse class assumes that the entities
-            # described by offset are complete, and all other words should
-            # have the tag 'O'. You can tell it to make no assumptions
-            # about the tag of a word by giving it the tag '-'.
-            # However, this allows a trivial solution to the current
-            # learning problem: if words are either 'any tag' or 'ANIMAL',
-            # the model can learn that all words can be tagged 'ANIMAL'.
-            #for i in range(len(gold.ner)):
-                #if not gold.ner[i].endswith('ANIMAL'):
-                #    gold.ner[i] = '-'
-            doc = nlp.make_doc(raw_text)
-            nlp.tagger(doc)
-            # As of 1.9, spaCy's parser now lets you supply a dropout probability
-            # This might help the model generalize better from only a few
-            # examples.
-            loss += nlp.entity.update(doc, gold, drop=0.9)
-        if loss == 0:
-            break
-    # This step averages the model's weights. This may or may not be good for
-    # your situation --- it's empirical.
-    nlp.end_training()
-    if output_dir:
-        if not output_dir.exists():
-            output_dir.mkdir()
-        nlp.save_to_directory(output_dir)
+    optimizer = nlp.begin_training(lambda: [])
+    nlp.meta['name'] = 'en_ent_animal'
+    for itn in range(50):
+        losses = {}
+        for batch in minibatch(get_gold_parses(nlp.make_doc, train_data), size=3):
+            docs, golds = zip(*batch)
+            nlp.update(docs, golds, losses=losses, sgd=optimizer, update_shared=True,
+                       drop=0.35)
+        print(losses)
+    if not output_dir:
+        return
+    elif not output_dir.exists():
+        output_dir.mkdir()
+    nlp.to_disk(output_dir)
 
 
 def main(model_name, output_directory=None):
-    print("Loading initial model", model_name)
-    nlp = spacy.load(model_name)
+    print("Creating initial model", model_name)
+    nlp = spacy.blank(model_name)
     if output_directory is not None:
         output_directory = Path(output_directory)
 
@@ -91,6 +77,11 @@ def main(model_name, output_directory=None):
             "Horses are too tall and they pretend to care about your feelings",
             [(0, 6, 'ANIMAL')],
         ),
+        (
+            "Do they bite?", 
+            [],
+        ),
+ 
         (
             "horses are too tall and they pretend to care about your feelings",
             [(0, 6, 'ANIMAL')]
@@ -109,18 +100,20 @@ def main(model_name, output_directory=None):
         )
 
     ]
-    nlp.entity.add_label('ANIMAL')
+    nlp.pipeline.append(TokenVectorEncoder(nlp.vocab))
+    nlp.pipeline.append(NeuralEntityRecognizer(nlp.vocab))
+    nlp.pipeline[-1].add_label('ANIMAL')
     train_ner(nlp, train_data, output_directory)
 
     # Test that the entity is recognized
-    doc = nlp('Do you like horses?')
+    text = 'Do you like horses?'
     print("Ents in 'Do you like horses?':")
+    doc = nlp(text)
     for ent in doc.ents:
         print(ent.label_, ent.text)
     if output_directory:
         print("Loading from", output_directory)
-        nlp2 = spacy.load('en', path=output_directory)
-        nlp2.entity.add_label('ANIMAL')
+        nlp2 = spacy.load(output_directory)
         doc2 = nlp2('Do you like horses?')
         for ent in doc2.ents:
             print(ent.label_, ent.text)

From daf869ab3b02a6e3ab36fe6b2bf5e4c7c0a72049 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 14 Sep 2017 16:16:41 +0200
Subject: [PATCH 013/213] Fix add_action for NER, so labelled 'O' actions
 aren't added

---
 spacy/syntax/ner.pyx | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/spacy/syntax/ner.pyx b/spacy/syntax/ner.pyx
index 2f5cd4e48..11b429aa2 100644
--- a/spacy/syntax/ner.pyx
+++ b/spacy/syntax/ner.pyx
@@ -220,6 +220,31 @@ cdef class BiluoPushDown(TransitionSystem):
             raise Exception(move)
         return t
 
+    def add_action(self, int action, label_name):
+        cdef attr_t label_id
+        if not isinstance(label_name, (int, long)):
+            label_id = self.strings.add(label_name)
+        else:
+            label_id = label_name
+        if action == OUT and label_id != 0:
+            return
+        if action == MISSING or action == ISNT:
+            return
+        # Check we're not creating a move we already have, so that this is
+        # idempotent
+        for trans in self.c[:self.n_moves]:
+            if trans.move == action and trans.label == label_id:
+                return 0
+        if self.n_moves >= self._size:
+            self._size *= 2
+            self.c = <Transition*>self.mem.realloc(self.c, self._size * sizeof(self.c[0]))
+        self.c[self.n_moves] = self.init_transition(self.n_moves, action, label_id)
+        assert self.c[self.n_moves].label == label_id
+        self.n_moves += 1
+        return 1
+
+
+
     cdef int initialize_state(self, StateC* st) nogil:
         # This is especially necessary when we use limited training data.
         for i in range(st.length):

From c6395b057a6cd65fe931f5b9b8aece35e94f16d7 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 14 Sep 2017 16:18:02 +0200
Subject: [PATCH 014/213] Improve parser feature extraction, for missing values

---
 spacy/syntax/_state.pxd    | 13 +++++++++----
 spacy/syntax/nn_parser.pyx | 16 +++++++++++++++-
 2 files changed, 24 insertions(+), 5 deletions(-)

diff --git a/spacy/syntax/_state.pxd b/spacy/syntax/_state.pxd
index 3da9e5d4c..9a08691de 100644
--- a/spacy/syntax/_state.pxd
+++ b/spacy/syntax/_state.pxd
@@ -101,9 +101,10 @@ cdef cppclass StateC:
         elif n == 6:
             if this.B(0) >= 0:
                 ids[0] = this.B(0)
+                ids[1] = this.B(0)-1
             else:
                 ids[0] = -1
-            ids[1] = this.B(0)
+                ids[1] = -1
             ids[2] = this.B(1)
             ids[3] = this.E(0)
             if ids[3] >= 1:
@@ -118,8 +119,12 @@ cdef cppclass StateC:
             # TODO error =/
             pass
         for i in range(n):
+            # Token vectors should be padded, so that there's a vector for
+            # missing values at the start.
             if ids[i] >= 0:
-                ids[i] += this.offset
+                ids[i] += this.offset + 1
+            else:
+                ids[i] = 0
 
     int S(int i) nogil const:
         if i >= this._s_i:
@@ -162,9 +167,9 @@ cdef cppclass StateC:
 
     int E(int i) nogil const:
         if this._e_i <= 0 or this._e_i >= this.length:
-            return 0
+            return -1
         if i < 0 or i >= this._e_i:
-            return 0
+            return -1
         return this._ents[this._e_i - (i+1)].start
 
     int L(int i, int idx) nogil const:
diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 552ea4f8f..ad6ed280e 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -394,7 +394,7 @@ cdef class Parser:
         tokvecs = self.model[0].ops.flatten(tokvecses)
         if USE_FINE_TUNE:
             tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
-
+        tokvecs = self._pad_tokvecs(tokvecs)
         nr_state = len(docs)
         nr_class = self.moves.n_moves
         nr_dim = tokvecs.shape[1]
@@ -454,6 +454,7 @@ cdef class Parser:
         tokvecs = self.model[0].ops.flatten(tokvecses)
         if USE_FINE_TUNE:
             tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
+        tokvecs = self._pad_tokvecs(tokvecs)
         cuda_stream = get_cuda_stream()
         state2vec, vec2scores = self.get_batch_model(len(docs), tokvecs,
                                                      cuda_stream, 0.0)
@@ -534,6 +535,8 @@ cdef class Parser:
             tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
             tokvecs = self.model[0].ops.flatten(tokvecs)
 
+        tokvecs = self._pad_tokvecs(tokvecs)
+
         cuda_stream = get_cuda_stream()
 
         states, golds, max_steps = self._init_gold_batch(docs, golds)
@@ -583,6 +586,7 @@ cdef class Parser:
                 break
         self._make_updates(d_tokvecs,
             backprops, sgd, cuda_stream)
+        d_tokvecs = self._unpad_tokvecs(d_tokvecs)
         d_tokvecs = self.model[0].ops.unflatten(d_tokvecs, [len(d) for d in docs])
         if USE_FINE_TUNE:
             d_tokvecs = bp_my_tokvecs(d_tokvecs, sgd=sgd)
@@ -639,10 +643,20 @@ cdef class Parser:
         d_tokvecs = self.model[0].ops.allocate(tokvecs.shape)
         self._make_updates(d_tokvecs, backprop_lower, sgd, cuda_stream)
         d_tokvecs = self.model[0].ops.unflatten(d_tokvecs, lengths)
+        d_tokvecs = self._unpad_tokvecs(d_tokvecs)
         if USE_FINE_TUNE:
             d_tokvecs = bp_my_tokvecs(d_tokvecs, sgd=sgd)
         return d_tokvecs
 
+    def _pad_tokvecs(self, tokvecs):
+        # Add a vector for missing values at the start of tokvecs
+        xp = get_array_module(tokvecs)
+        pad = xp.zeros((1, tokvecs.shape[1]), dtype=tokvecs.dtype)
+        return xp.vstack((pad, tokvecs))
+
+    def _unpad_tokvecs(self, d_tokvecs):
+        return d_tokvecs[1:]
+
     def _init_gold_batch(self, whole_docs, whole_golds):
         """Make a square batch, of length equal to the shortest doc. A long
         doc will get multiple states. Let's say we have a doc of length 2*N,

From 70da88a3a74e17b0c15fd9224c025a5c556625aa Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 14 Sep 2017 16:18:30 +0200
Subject: [PATCH 015/213] Update comment on Language.begin_training

---
 spacy/language.py | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/spacy/language.py b/spacy/language.py
index 66b42ff94..e6a5304dd 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -347,15 +347,9 @@ class Language(object):
         """Allocate models, pre-process training data and acquire a trainer and
         optimizer. Used as a contextmanager.
 
-        gold_tuples (iterable): Gold-standard training data.
+        get_gold_tuples (function): Function returning gold data
         **cfg: Config parameters.
-        YIELDS (tuple): A trainer and an optimizer.
-
-        EXAMPLE:
-            >>> with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
-            >>>    for epoch in trainer.epochs(gold):
-            >>>        for docs, golds in epoch:
-            >>>            state = nlp.update(docs, golds, sgd=optimizer)
+        returns: An optimizer
         """
         if self.parser:
             self.pipeline.append(NeuralLabeller(self.vocab))

From d1518027a980f57d6ee88d6d99e161267ab9ad25 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 14 Sep 2017 16:18:46 +0200
Subject: [PATCH 016/213] Increment version

---
 spacy/about.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/about.py b/spacy/about.py
index d566fbb1f..40444ffd1 100644
--- a/spacy/about.py
+++ b/spacy/about.py
@@ -3,7 +3,7 @@
 # https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
 
 __title__ = 'spacy-nightly'
-__version__ = '2.0.0a13'
+__version__ = '2.0.0a14'
 __summary__ = 'Industrial-strength Natural Language Processing (NLP) with Python and Cython'
 __uri__ = 'https://spacy.io'
 __author__ = 'Explosion AI'

From 664c5af745786312725917cd9a44418777868350 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 14 Sep 2017 16:59:25 +0200
Subject: [PATCH 017/213] Revert padding in parser

---
 spacy/syntax/_state.pxd    | 6 ++----
 spacy/syntax/nn_parser.pyx | 6 ------
 2 files changed, 2 insertions(+), 10 deletions(-)

diff --git a/spacy/syntax/_state.pxd b/spacy/syntax/_state.pxd
index 9a08691de..4fb16881a 100644
--- a/spacy/syntax/_state.pxd
+++ b/spacy/syntax/_state.pxd
@@ -119,12 +119,10 @@ cdef cppclass StateC:
             # TODO error =/
             pass
         for i in range(n):
-            # Token vectors should be padded, so that there's a vector for
-            # missing values at the start.
             if ids[i] >= 0:
-                ids[i] += this.offset + 1
+                ids[i] += this.offset
             else:
-                ids[i] = 0
+                ids[i] = -1
 
     int S(int i) nogil const:
         if i >= this._s_i:
diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index ad6ed280e..3ea17f2fe 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -394,7 +394,6 @@ cdef class Parser:
         tokvecs = self.model[0].ops.flatten(tokvecses)
         if USE_FINE_TUNE:
             tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
-        tokvecs = self._pad_tokvecs(tokvecs)
         nr_state = len(docs)
         nr_class = self.moves.n_moves
         nr_dim = tokvecs.shape[1]
@@ -454,7 +453,6 @@ cdef class Parser:
         tokvecs = self.model[0].ops.flatten(tokvecses)
         if USE_FINE_TUNE:
             tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
-        tokvecs = self._pad_tokvecs(tokvecs)
         cuda_stream = get_cuda_stream()
         state2vec, vec2scores = self.get_batch_model(len(docs), tokvecs,
                                                      cuda_stream, 0.0)
@@ -527,7 +525,6 @@ cdef class Parser:
         if losses is not None and self.name not in losses:
             losses[self.name] = 0.
         docs, tokvec_lists = docs_tokvecs
-        tokvecs = self.model[0].ops.flatten(tokvec_lists)
         if isinstance(docs, Doc) and isinstance(golds, GoldParse):
             docs = [docs]
             golds = [golds]
@@ -535,7 +532,6 @@ cdef class Parser:
             tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
             tokvecs = self.model[0].ops.flatten(tokvecs)
 
-        tokvecs = self._pad_tokvecs(tokvecs)
 
         cuda_stream = get_cuda_stream()
 
@@ -586,7 +582,6 @@ cdef class Parser:
                 break
         self._make_updates(d_tokvecs,
             backprops, sgd, cuda_stream)
-        d_tokvecs = self._unpad_tokvecs(d_tokvecs)
         d_tokvecs = self.model[0].ops.unflatten(d_tokvecs, [len(d) for d in docs])
         if USE_FINE_TUNE:
             d_tokvecs = bp_my_tokvecs(d_tokvecs, sgd=sgd)
@@ -643,7 +638,6 @@ cdef class Parser:
         d_tokvecs = self.model[0].ops.allocate(tokvecs.shape)
         self._make_updates(d_tokvecs, backprop_lower, sgd, cuda_stream)
         d_tokvecs = self.model[0].ops.unflatten(d_tokvecs, lengths)
-        d_tokvecs = self._unpad_tokvecs(d_tokvecs)
         if USE_FINE_TUNE:
             d_tokvecs = bp_my_tokvecs(d_tokvecs, sgd=sgd)
         return d_tokvecs

From 8c503487af306e4ca1fc93372c28cecebede95ca Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 14 Sep 2017 16:59:45 +0200
Subject: [PATCH 018/213] Fix lookup of missing NER actions

---
 spacy/syntax/ner.pyx | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/spacy/syntax/ner.pyx b/spacy/syntax/ner.pyx
index 11b429aa2..1a174aba8 100644
--- a/spacy/syntax/ner.pyx
+++ b/spacy/syntax/ner.pyx
@@ -161,8 +161,7 @@ cdef class BiluoPushDown(TransitionSystem):
     cdef Transition lookup_transition(self, object name) except *:
         cdef attr_t label
         if name == '-' or name == None:
-            move_str = 'M'
-            label = 0
+            return Transition(clas=0, move=MISSING, label=0, score=0)
         elif name == '!O':
             return Transition(clas=0, move=ISNT, label=0, score=0)
         elif '-' in name:

From 18347ab69ceb4d57a87269bd141b300081b82983 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 14 Sep 2017 19:07:35 +0200
Subject: [PATCH 019/213] Implement AddHistory layer wrapper

---
 spacy/_ml.py | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 003541f4b..d3c82897f 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -78,6 +78,37 @@ def add_tuples(X, drop=0.):
     return (vals1+vals2, length), add_tuples_bwd
 
 
+def AddHistory(layer, decay=0.0001):
+    ops = layer.ops
+    nonlocals = []
+    if layer.nI:
+        average_inputs = ops.allocate((layer.nO, layer.nI-layer.nO))
+        nonlocals = []
+    def history_fwd(X, drop=0.):
+        if not nonlocals:
+            nonlocals.append(ops.allocate((layer.nO, X.shape[1])))
+            model.history = nonlocals[0]
+        average_inputs = nonlocals[0]
+        hist = ops.xp.tensordot(X, average_inputs, axes=[[1], [1]])
+        X_hist = ops.xp.hstack((X, hist))
+        Y, bp_Y = layer.begin_update(X_hist, drop=drop)
+        for i in range(Y.shape[0]):
+            amax = Y[i].argmax()
+            average_inputs[amax] *= 1-decay
+            average_inputs[amax] += decay * X[i]
+        def history_bwd(dY, sgd=None):
+            dX_hist = bp_Y(dY, sgd=sgd)
+            dX = dX_hist[:, :X.shape[1]]
+            return dX
+        return Y, history_bwd
+    model = wrap(history_fwd, layer)
+    if layer.nI:
+        model.history = average_inputs
+    else:
+        model.history = None
+    return model
+
+
 def _zero_init(model):
     def _zero_init_impl(self, X, y):
         self.W.fill(0)

From bd3da3d6fb8626613e7ee76931ea6ae67786011e Mon Sep 17 00:00:00 2001
From: Ines Montani <ines.montani@gmail.com>
Date: Thu, 14 Sep 2017 19:23:13 +0200
Subject: [PATCH 020/213] Port over change from #1323 and tidy up

---
 spacy/lang/zh/__init__.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spacy/lang/zh/__init__.py b/spacy/lang/zh/__init__.py
index 3f68336f8..46ad3946f 100644
--- a/spacy/lang/zh/__init__.py
+++ b/spacy/lang/zh/__init__.py
@@ -14,8 +14,8 @@ class Chinese(Language):
         except ImportError:
             raise ImportError("The Chinese tokenizer requires the Jieba library: "
                               "https://github.com/fxsjy/jieba")
-        words = list(jieba.cut(text, cut_all=True))
-        words=[x for x in words if x]
+        words = list(jieba.cut(text, cut_all=False))
+        words = [x for x in words if x]
         return Doc(self.vocab, words=words, spaces=[False]*len(words))
 
 

From d84607f6bb7fa561d65734b1d2d15770c5de05b9 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 14 Sep 2017 20:34:40 +0200
Subject: [PATCH 021/213] Vectorize update in AddHistory

---
 spacy/_ml.py | 23 ++++++++++-------------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index d3c82897f..1f3d50cbd 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -81,31 +81,28 @@ def add_tuples(X, drop=0.):
 def AddHistory(layer, decay=0.0001):
     ops = layer.ops
     nonlocals = []
-    if layer.nI:
-        average_inputs = ops.allocate((layer.nO, layer.nI-layer.nO))
-        nonlocals = []
     def history_fwd(X, drop=0.):
         if not nonlocals:
-            nonlocals.append(ops.allocate((layer.nO, X.shape[1])))
+            if hasattr(layer, 'nO'):
+                nO = layer.nO
+            else:
+                nO = layer._layers[-1].nO
+            nonlocals.append(ops.allocate((nO, X.shape[1])))
             model.history = nonlocals[0]
         average_inputs = nonlocals[0]
         hist = ops.xp.tensordot(X, average_inputs, axes=[[1], [1]])
         X_hist = ops.xp.hstack((X, hist))
         Y, bp_Y = layer.begin_update(X_hist, drop=drop)
-        for i in range(Y.shape[0]):
-            amax = Y[i].argmax()
-            average_inputs[amax] *= 1-decay
-            average_inputs[amax] += decay * X[i]
+        amax = Y.argmax(axis=1)
+        average_inputs *= 1-decay
+        ops.scatter_add(average_inputs, amax, X * decay)
         def history_bwd(dY, sgd=None):
             dX_hist = bp_Y(dY, sgd=sgd)
             dX = dX_hist[:, :X.shape[1]]
-            return dX
+            return ops.xp.ascontiguousarray(dX)
         return Y, history_bwd
     model = wrap(history_fwd, layer)
-    if layer.nI:
-        model.history = average_inputs
-    else:
-        model.history = None
+    model.history = None
     return model
 
 

From 027a5d8b75c74fe2ae27d21ecb1d4ca36ec23cb3 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 15 Sep 2017 10:36:46 +0200
Subject: [PATCH 022/213] Update train_ner_standalone example

---
 examples/training/train_ner_standalone.py | 192 +++++++++-------------
 1 file changed, 80 insertions(+), 112 deletions(-)

diff --git a/examples/training/train_ner_standalone.py b/examples/training/train_ner_standalone.py
index 9591d1b71..6cca56c69 100644
--- a/examples/training/train_ner_standalone.py
+++ b/examples/training/train_ner_standalone.py
@@ -13,24 +13,27 @@ Input data:
 https://www.lt.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_LangTech/data/GermEval2014_complete_data.zip
 
 Developed for: spaCy 1.7.1
-Last tested for: spaCy 1.7.1
+Last tested for: spaCy 2.0.0a13
 '''
 from __future__ import unicode_literals, print_function
 import plac
 from pathlib import Path
 import random
 import json
+from thinc.neural.optimizers import Adam
+from thinc.neural.ops import NumpyOps
+import tqdm
 
-import spacy.orth as orth_funcs
 from spacy.vocab import Vocab
-from spacy.pipeline import BeamEntityRecognizer
-from spacy.pipeline import EntityRecognizer
+from spacy.pipeline import TokenVectorEncoder, NeuralEntityRecognizer
 from spacy.tokenizer import Tokenizer
 from spacy.tokens import Doc
 from spacy.attrs import *
 from spacy.gold import GoldParse
-from spacy.gold import _iob_to_biluo as iob_to_biluo
+from spacy.gold import iob_to_biluo
+from spacy.gold import minibatch
 from spacy.scorer import Scorer
+import spacy.util
 
 try:
     unicode
@@ -38,95 +41,40 @@ except NameError:
     unicode = str
 
 
+spacy.util.set_env_log(True)
+
+
 def init_vocab():
     return Vocab(
         lex_attr_getters={
             LOWER: lambda string: string.lower(),
-            SHAPE: orth_funcs.word_shape,
+            NORM: lambda string: string.lower(),
             PREFIX: lambda string: string[0],
             SUFFIX: lambda string: string[-3:],
-            CLUSTER: lambda string: 0,
-            IS_ALPHA: orth_funcs.is_alpha,
-            IS_ASCII: orth_funcs.is_ascii,
-            IS_DIGIT: lambda string: string.isdigit(),
-            IS_LOWER: orth_funcs.is_lower,
-            IS_PUNCT: orth_funcs.is_punct,
-            IS_SPACE: lambda string: string.isspace(),
-            IS_TITLE: orth_funcs.is_title,
-            IS_UPPER: orth_funcs.is_upper,
-            IS_STOP: lambda string: False,
-            IS_OOV: lambda string: True
         })
 
 
-def save_vocab(vocab, path):
-    path = Path(path)
-    if not path.exists():
-        path.mkdir()
-    elif not path.is_dir():
-        raise IOError("Can't save vocab to %s\nNot a directory" % path)
-    with (path / 'strings.json').open('w') as file_:
-        vocab.strings.dump(file_)
-    vocab.dump((path / 'lexemes.bin').as_posix())
-
-
-def load_vocab(path):
-    path = Path(path)
-    if not path.exists():
-        raise IOError("Cannot load vocab from %s\nDoes not exist" % path)
-    if not path.is_dir():
-        raise IOError("Cannot load vocab from %s\nNot a directory" % path)
-    return Vocab.load(path)
-
-
-def init_ner_model(vocab, features=None):
-    if features is None:
-        features = tuple(EntityRecognizer.feature_templates)
-    return EntityRecognizer(vocab, features=features)
-
-
-def save_ner_model(model, path):
-    path = Path(path)
-    if not path.exists():
-        path.mkdir()
-    if not path.is_dir():
-        raise IOError("Can't save model to %s\nNot a directory" % path)
-    model.model.dump((path / 'model').as_posix())
-    with (path / 'config.json').open('w') as file_:
-        data = json.dumps(model.cfg)
-        if not isinstance(data, unicode):
-            data = data.decode('utf8')
-        file_.write(data)
-
-
-def load_ner_model(vocab, path):
-    return EntityRecognizer.load(path, vocab)
-
-
 class Pipeline(object):
-    @classmethod
-    def load(cls, path):
-        path = Path(path)
-        if not path.exists():
-            raise IOError("Cannot load pipeline from %s\nDoes not exist" % path)
-        if not path.is_dir():
-            raise IOError("Cannot load pipeline from %s\nNot a directory" % path)
-        vocab = load_vocab(path)
-        tokenizer = Tokenizer(vocab, {}, None, None, None)
-        ner_model = load_ner_model(vocab, path / 'ner')
-        return cls(vocab, tokenizer, ner_model)
-
-    def __init__(self, vocab=None, tokenizer=None, entity=None):
+    def __init__(self, vocab=None, tokenizer=None, tensorizer=None, entity=None):
         if vocab is None:
             vocab = init_vocab()
         if tokenizer is None:
             tokenizer = Tokenizer(vocab, {}, None, None, None)
+        if tensorizer is None:
+            tensorizer = TokenVectorEncoder(vocab)
         if entity is None:
-            entity = init_ner_model(self.vocab)
+            entity = NeuralEntityRecognizer(vocab)
         self.vocab = vocab
         self.tokenizer = tokenizer
+        self.tensorizer = tensorizer
         self.entity = entity
-        self.pipeline = [self.entity]
+        self.pipeline = [tensorizer, self.entity]
+
+    def begin_training(self):
+        for model in self.pipeline:
+            model.begin_training([])
+        optimizer = Adam(NumpyOps(), 0.001)
+        return optimizer
 
     def __call__(self, input_):
         doc = self.make_doc(input_)
@@ -147,14 +95,18 @@ class Pipeline(object):
         gold = GoldParse(doc, entities=annotations)
         return gold
 
-    def update(self, input_, annot):
-        doc = self.make_doc(input_)
-        gold = self.make_gold(input_, annot)
-        for ner in gold.ner:
-            if ner not in (None, '-', 'O'):
-                action, label = ner.split('-', 1)
-                self.entity.add_label(label)
-        return self.entity.update(doc, gold)
+    def update(self, inputs, annots, sgd, losses=None, drop=0.):
+        if losses is None:
+            losses = {}
+        docs = [self.make_doc(input_) for input_ in inputs]
+        golds = [self.make_gold(input_, annot) for input_, annot in
+                 zip(inputs, annots)]
+
+        tensors, bp_tensors = self.tensorizer.update(docs, golds, drop=drop)
+        d_tensors = self.entity.update((docs, tensors), golds, drop=drop,
+                                      sgd=sgd, losses=losses)
+        bp_tensors(d_tensors, sgd=sgd)
+        return losses
 
     def evaluate(self, examples):
         scorer = Scorer()
@@ -164,34 +116,38 @@ class Pipeline(object):
             scorer.score(doc, gold)
         return scorer.scores
 
-    def average_weights(self):
-        self.entity.model.end_training()
-
-    def save(self, path):
+    def to_disk(self, path):
         path = Path(path)
         if not path.exists():
             path.mkdir()
         elif not path.is_dir():
             raise IOError("Can't save pipeline to %s\nNot a directory" % path)
-        save_vocab(self.vocab, path / 'vocab')
-        save_ner_model(self.entity, path / 'ner')
+        self.vocab.to_disk(path / 'vocab')
+        self.tensorizer.to_disk(path / 'tensorizer')
+        self.entity.to_disk(path / 'ner')
+
+    def from_disk(self, path):
+        path = Path(path)
+        if not path.exists():
+            raise IOError("Cannot load pipeline from %s\nDoes not exist" % path)
+        if not path.is_dir():
+            raise IOError("Cannot load pipeline from %s\nNot a directory" % path)
+        self.vocab = self.vocab.from_disk(path / 'vocab')
+        self.tensorizer = self.tensorizer.from_disk(path / 'tensorizer')
+        self.entity = self.entity.from_disk(path / 'ner')
 
 
-def train(nlp, train_examples, dev_examples, ctx, nr_epoch=5):
-    next_epoch = train_examples
+def train(nlp, train_examples, dev_examples, nr_epoch=5):
+    sgd = nlp.begin_training()
     print("Iter", "Loss", "P", "R", "F")
     for i in range(nr_epoch):
-        this_epoch = next_epoch
-        next_epoch = []
-        loss = 0
-        for input_, annot in this_epoch:
-            loss += nlp.update(input_, annot)
-            if (i+1) < nr_epoch:
-                next_epoch.append((input_, annot))
-        random.shuffle(next_epoch)
+        random.shuffle(train_examples)
+        losses = {}
+        for batch in minibatch(tqdm.tqdm(train_examples, leave=False), size=8):
+            inputs, annots = zip(*batch)
+            nlp.update(list(inputs), list(annots), sgd, losses=losses)
         scores = nlp.evaluate(dev_examples)
-        report_scores(i, loss, scores)
-    nlp.average_weights()
+        report_scores(i, losses['ner'], scores)
     scores = nlp.evaluate(dev_examples)
     report_scores(channels, i+1, loss, scores)
 
@@ -208,7 +164,8 @@ def read_examples(path):
     with path.open() as file_:
         sents = file_.read().strip().split('\n\n')
         for sent in sents:
-            if not sent.strip():
+            sent = sent.strip()
+            if not sent:
                 continue
             tokens = sent.split('\n')
             while tokens and tokens[0].startswith('#'):
@@ -217,28 +174,39 @@ def read_examples(path):
             iob = []
             for token in tokens:
                 if token.strip():
-                    pieces = token.split()
+                    pieces = token.split('\t')
                     words.append(pieces[1])
                     iob.append(pieces[2])
             yield words, iob_to_biluo(iob)
 
 
+def get_labels(examples):
+    labels = set()
+    for words, tags in examples:
+        for tag in tags:
+            if '-' in tag:
+                labels.add(tag.split('-')[1])
+    return sorted(labels)
+
+
 @plac.annotations(
     model_dir=("Path to save the model", "positional", None, Path),
     train_loc=("Path to your training data", "positional", None, Path),
     dev_loc=("Path to your development data", "positional", None, Path),
 )
-def main(model_dir=Path('/home/matt/repos/spaCy/spacy/data/de-1.0.0'),
-        train_loc=None, dev_loc=None, nr_epoch=30):
-    
-    train_examples = read_examples(train_loc)
+def main(model_dir, train_loc, dev_loc, nr_epoch=30):
+    print(model_dir, train_loc, dev_loc)
+    train_examples = list(read_examples(train_loc))
     dev_examples = read_examples(dev_loc)
-    nlp = Pipeline.load(model_dir)
+    nlp = Pipeline()
+    for label in get_labels(train_examples):
+        nlp.entity.add_label(label)
+        print("Add label", label)
 
-    train(nlp, train_examples, list(dev_examples), ctx, nr_epoch)
+    train(nlp, train_examples, list(dev_examples), nr_epoch)
 
-    nlp.save(model_dir)
+    nlp.to_disk(model_dir)
 
 
 if __name__ == '__main__':
-    main()
+    plac.call(main)

From 8b481e04658443013e132988dc77740b4aa6a167 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 15 Sep 2017 10:38:08 +0200
Subject: [PATCH 023/213] Remove redundant brackets

---
 spacy/syntax/nn_parser.pyx | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 3ea17f2fe..e2dc35966 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -262,8 +262,8 @@ cdef class Parser:
                 upper.is_noop = True
             else:
                 upper = chain(
-                    clone(Maxout(hidden_width), (depth-1)),
-                    zero_init(Affine(nr_class, drop_factor=0.0))
+                    clone(Maxout(hidden_width), depth-1),
+                    zero_init(Affine(nr_class, hidden_width, drop_factor=0.0))
                 )
                 upper.is_noop = False
         # TODO: This is an unfortunate hack atm!

From 2f08489694f0ad74f03ccf566814628c57a1976c Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 15 Sep 2017 10:41:40 +0200
Subject: [PATCH 024/213] Remove AddHistory layer -- didnt work as planned

---
 spacy/_ml.py | 28 ----------------------------
 1 file changed, 28 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 1f3d50cbd..003541f4b 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -78,34 +78,6 @@ def add_tuples(X, drop=0.):
     return (vals1+vals2, length), add_tuples_bwd
 
 
-def AddHistory(layer, decay=0.0001):
-    ops = layer.ops
-    nonlocals = []
-    def history_fwd(X, drop=0.):
-        if not nonlocals:
-            if hasattr(layer, 'nO'):
-                nO = layer.nO
-            else:
-                nO = layer._layers[-1].nO
-            nonlocals.append(ops.allocate((nO, X.shape[1])))
-            model.history = nonlocals[0]
-        average_inputs = nonlocals[0]
-        hist = ops.xp.tensordot(X, average_inputs, axes=[[1], [1]])
-        X_hist = ops.xp.hstack((X, hist))
-        Y, bp_Y = layer.begin_update(X_hist, drop=drop)
-        amax = Y.argmax(axis=1)
-        average_inputs *= 1-decay
-        ops.scatter_add(average_inputs, amax, X * decay)
-        def history_bwd(dY, sgd=None):
-            dX_hist = bp_Y(dY, sgd=sgd)
-            dX = dX_hist[:, :X.shape[1]]
-            return ops.xp.ascontiguousarray(dX)
-        return Y, history_bwd
-    model = wrap(history_fwd, layer)
-    model.history = None
-    return model
-
-
 def _zero_init(model):
     def _zero_init_impl(self, X, y):
         self.W.fill(0)

From 86367ab092d75af98bfb68bc3b6c499d28d0067f Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 15 Sep 2017 12:41:59 +0200
Subject: [PATCH 025/213] Start work on appveyor, for Windows build

---
 .appveyor.yml | 52 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/.appveyor.yml b/.appveyor.yml
index 4dd7b0a31..d63512fcf 100644
--- a/.appveyor.yml
+++ b/.appveyor.yml
@@ -1 +1,53 @@
+environment:
+
+  matrix:
+
+    # For Python versions available on Appveyor, see
+    # http://www.appveyor.com/docs/installed-software#python
+    # The list here is complete (excluding Python 2.6, which
+    # isn't covered by this document) at the time of writing.
+
+    - PYTHON: "C:\\Python27"
+    #- PYTHON: "C:\\Python33"
+    #- PYTHON: "C:\\Python34"
+    #- PYTHON: "C:\\Python35"
+    #- PYTHON: "C:\\Python27-x64"
+    #- PYTHON: "C:\\Python33-x64"
+    #- DISTUTILS_USE_SDK: "1"
+    #- PYTHON: "C:\\Python34-x64"
+    #- DISTUTILS_USE_SDK: "1"
+    #- PYTHON: "C:\\Python35-x64"
+    #- PYTHON: "C:\\Python36-x64"
+
+install:
+  # We need wheel installed to build wheels
+  - "%PYTHON%\\python.exe -m pip install wheel"
+  - "%PYTHON%\\python.exe -m pip install -e ."
+
 build: off
+
+test_script:
+  # Put your test command here.
+  # If you don't need to build C extensions on 64-bit Python 3.3 or 3.4,
+  # you can remove "build.cmd" from the front of the command, as it's
+  # only needed to support those cases.
+  # Note that you must use the environment variable %PYTHON% to refer to
+  # the interpreter you're using - Appveyor does not do anything special
+  # to put the Python version you want to use on PATH.
+  - "%PYTHON%\\python.exe -m pytest spacy/"
+
+after_test:
+  # This step builds your wheels.
+  # Again, you only need build.cmd if you're building C extensions for
+  # 64-bit Python 3.3/3.4. And you need to use %PYTHON% to get the correct
+  # interpreter
+  - "%PYTHON%\\python.exe setup.py bdist_wheel"
+
+artifacts:
+  # bdist_wheel puts your built wheel in the dist directory
+  - path: dist\*
+
+#on_success:
+#  You can use this step to upload your artifacts to a public website.
+#  See Appveyor's documentation for more details. Or you can simply
+#  access your wheels from the Appveyor "artifacts" tab for your build.

From 1f840a9211347ec5d3f7dc64eccf74e254aa414c Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 15 Sep 2017 12:49:02 +0200
Subject: [PATCH 026/213] Appveyor

---
 .appveyor.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.appveyor.yml b/.appveyor.yml
index d63512fcf..1fc3c920f 100644
--- a/.appveyor.yml
+++ b/.appveyor.yml
@@ -17,7 +17,7 @@ environment:
     #- PYTHON: "C:\\Python34-x64"
     #- DISTUTILS_USE_SDK: "1"
     #- PYTHON: "C:\\Python35-x64"
-    #- PYTHON: "C:\\Python36-x64"
+    - PYTHON: "C:\\Python36-x64"
 
 install:
   # We need wheel installed to build wheels

From 25ec8935adfc50f25109b411dd59980c2f065c52 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 15 Sep 2017 12:53:07 +0200
Subject: [PATCH 027/213] Appveyor

---
 .appveyor.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.appveyor.yml b/.appveyor.yml
index 1fc3c920f..f2d166754 100644
--- a/.appveyor.yml
+++ b/.appveyor.yml
@@ -22,6 +22,7 @@ environment:
 install:
   # We need wheel installed to build wheels
   - "%PYTHON%\\python.exe -m pip install wheel"
+  - "%PYTHON%\\python.exe -m pip install cython"
   - "%PYTHON%\\python.exe -m pip install -e ."
 
 build: off

From 02273eeca8290e5fb7906871c76a9e6db1c6f943 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 15 Sep 2017 12:55:33 +0200
Subject: [PATCH 028/213] Appveyor

---
 .appveyor.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.appveyor.yml b/.appveyor.yml
index f2d166754..a379cdd31 100644
--- a/.appveyor.yml
+++ b/.appveyor.yml
@@ -23,6 +23,7 @@ install:
   # We need wheel installed to build wheels
   - "%PYTHON%\\python.exe -m pip install wheel"
   - "%PYTHON%\\python.exe -m pip install cython"
+  - "%PYTHON%\\python.exe -m pip install -r requirements.txt"
   - "%PYTHON%\\python.exe -m pip install -e ."
 
 build: off

From 07cdbd121910c7984f7f597234b0cf986af9c2d2 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 15 Sep 2017 22:47:53 +0200
Subject: [PATCH 029/213] Require thinc 6.8.1, for Windows

---
 requirements.txt | 4 ++--
 setup.py         | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index aae0f9388..54c888a11 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,9 +1,9 @@
-cython<0.24
+cython>=0.24
 pathlib
 numpy>=1.7
 cymem>=1.30,<1.32
 preshed>=1.0.0,<2.0.0
-thinc>=6.8.0,<6.9.0
+thinc>=6.8.1,<6.9.0
 murmurhash>=0.28,<0.29
 plac<1.0.0,>=0.9.6
 six
diff --git a/setup.py b/setup.py
index 6a22f4076..535dddd0d 100755
--- a/setup.py
+++ b/setup.py
@@ -195,7 +195,7 @@ def setup_package():
                 'murmurhash>=0.28,<0.29',
                 'cymem>=1.30,<1.32',
                 'preshed>=1.0.0,<2.0.0',
-                'thinc>=6.8.0,<6.9.0',
+                'thinc>=6.8.1,<6.9.0',
                 'plac<1.0.0,>=0.9.6',
                 'pip>=9.0.0,<10.0.0',
                 'six',

From 2432308f3ef136e5e90d7be39ea795d3c6e61510 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 15 Sep 2017 23:55:19 +0200
Subject: [PATCH 030/213] Build in separate step for appveyor

---
 .appveyor.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.appveyor.yml b/.appveyor.yml
index a379cdd31..8dbdd2868 100644
--- a/.appveyor.yml
+++ b/.appveyor.yml
@@ -24,6 +24,7 @@ install:
   - "%PYTHON%\\python.exe -m pip install wheel"
   - "%PYTHON%\\python.exe -m pip install cython"
   - "%PYTHON%\\python.exe -m pip install -r requirements.txt"
+  - "%PYTHON%\\python.exe -m python setup.py build_ext --inplace"
   - "%PYTHON%\\python.exe -m pip install -e ."
 
 build: off

From 1ffc9a7fbfdd7ef620b025a770c4ba07305ce81d Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 15 Sep 2017 23:59:36 +0200
Subject: [PATCH 031/213] Fix appveyor

---
 .appveyor.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.appveyor.yml b/.appveyor.yml
index 8dbdd2868..12399a5a1 100644
--- a/.appveyor.yml
+++ b/.appveyor.yml
@@ -24,7 +24,7 @@ install:
   - "%PYTHON%\\python.exe -m pip install wheel"
   - "%PYTHON%\\python.exe -m pip install cython"
   - "%PYTHON%\\python.exe -m pip install -r requirements.txt"
-  - "%PYTHON%\\python.exe -m python setup.py build_ext --inplace"
+  - "%PYTHON%\\python.exe setup.py build_ext --inplace"
   - "%PYTHON%\\python.exe -m pip install -e ."
 
 build: off

From f730d07e4e7ab3d4cba3d16537b7ca7a5a098307 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 16 Sep 2017 00:25:18 +0200
Subject: [PATCH 032/213] Fix prange error for Windows

---
 spacy/syntax/nn_parser.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index e2dc35966..ea484f1c2 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -419,7 +419,7 @@ cdef class Parser:
         cdef int has_hidden = not getattr(vec2scores, 'is_noop', False)
         while not next_step.empty():
             if not has_hidden:
-                for i in cython.parallel.prange(
+                for i in range(
                         next_step.size(), num_threads=6, nogil=True):
                     self._parse_step(next_step[i],
                         feat_weights, nr_class, nr_feat, nr_piece)

From 3fa5b40b5cace40a5b8fde8112354abce6488b77 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 16 Sep 2017 11:21:35 +0200
Subject: [PATCH 033/213] Add test for hash consistency

---
 spacy/tests/stringstore/test_stringstore.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/spacy/tests/stringstore/test_stringstore.py b/spacy/tests/stringstore/test_stringstore.py
index 65b994606..602ebcee6 100644
--- a/spacy/tests/stringstore/test_stringstore.py
+++ b/spacy/tests/stringstore/test_stringstore.py
@@ -6,6 +6,16 @@ from ...strings import StringStore
 import pytest
 
 
+def test_string_hash(stringstore):
+    '''Test that string hashing is stable across platforms'''
+    ss = stringstore
+    assert ss.add('apple') == 8566208034543834098
+    heart = '\U0001f499'
+    print(heart)
+    h = ss.add(heart)
+    assert h == 11841826740069053588L
+ 
+
 def test_stringstore_from_api_docs(stringstore):
     apple_hash = stringstore.add('apple')
     assert apple_hash == 8566208034543834098

From 8a829eb98c41194a08f48fd1a9bec496ec673c98 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 16 Sep 2017 11:49:31 +0200
Subject: [PATCH 034/213] Fix travis.sh

---
 travis.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/travis.sh b/travis.sh
index 4b7d8017c..eed6a96f2 100755
--- a/travis.sh
+++ b/travis.sh
@@ -17,6 +17,7 @@ fi
 
 if [ "${VIA}" == "compile" ]; then
   pip install -r requirements.txt
+  python setup.py build_ext --inplace
   pip install -e .
 fi
 

From 11f2a05ede85b547b0b33d5642db6e3eaa1fba07 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 16 Sep 2017 12:20:04 +0200
Subject: [PATCH 035/213] Fix code explosion from long enum in Python 3, Cython
 0.24+

---
 spacy/symbols.pxd |  2 +-
 spacy/symbols.pyx | 11 ++++++++++-
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/spacy/symbols.pxd b/spacy/symbols.pxd
index 0b713cb21..e981de6ae 100644
--- a/spacy/symbols.pxd
+++ b/spacy/symbols.pxd
@@ -1,4 +1,4 @@
-cpdef enum symbol_t:
+cdef enum symbol_t:
     NIL
     IS_ALPHA
     IS_ASCII
diff --git a/spacy/symbols.pyx b/spacy/symbols.pyx
index 9f4009579..dd0e38cad 100644
--- a/spacy/symbols.pyx
+++ b/spacy/symbols.pyx
@@ -1,4 +1,6 @@
 # coding: utf8
+#cython: optimize.unpack_method_calls=False
+
 from __future__ import unicode_literals
 
 IDS = {
@@ -458,4 +460,11 @@ IDS = {
     "xcomp": xcomp
 }
 
-NAMES = [it[0] for it in sorted(IDS.items(), key=lambda it: it[1])]
+def sort_nums(x):
+    return x[1]
+
+NAMES = [it[0] for it in sorted(IDS.items(), key=sort_nums)]
+# Unfortunate hack here, to work around problem with long cpdef enum
+# (which is generating an enormous amount of C++ in Cython 0.24+)
+# We keep the enum cdef, and just make sure the names are available to Python
+locals().update(IDS)

From 8c945310fb16912a23ef8311cd4cd00aeb3798e2 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 16 Sep 2017 16:21:13 +0200
Subject: [PATCH 036/213] Excuse emoji failure on narrow unicode builds

---
 spacy/tests/tokenizer/test_exceptions.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/spacy/tests/tokenizer/test_exceptions.py b/spacy/tests/tokenizer/test_exceptions.py
index 57281b998..132f27433 100644
--- a/spacy/tests/tokenizer/test_exceptions.py
+++ b/spacy/tests/tokenizer/test_exceptions.py
@@ -1,6 +1,7 @@
 # coding: utf-8
 from __future__ import unicode_literals
 
+import sys
 import pytest
 
 
@@ -37,9 +38,10 @@ def test_tokenizer_excludes_false_pos_emoticons(tokenizer, text, length):
     tokens = tokenizer(text)
     assert len(tokens) == length
 
-
 @pytest.mark.parametrize('text,length', [('can you still dunk?🍕🍔😵LOL', 8),
                                          ('i💙you', 3), ('🤘🤘yay!', 4)])
 def test_tokenizer_handles_emoji(tokenizer, text, length):
-    tokens = tokenizer(text)
-    assert len(tokens) == length
+    # These break on narrow unicode builds, e.g. Windows
+    if sys.maxunicode >= 1114111:
+        tokens = tokenizer(text)
+        assert len(tokens) == length

From ebf8942564246729c79a299d597f13e8bd1215b2 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 16 Sep 2017 16:22:38 +0200
Subject: [PATCH 037/213] Fix test for Python3

---
 spacy/tests/stringstore/test_stringstore.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/tests/stringstore/test_stringstore.py b/spacy/tests/stringstore/test_stringstore.py
index 602ebcee6..3f2992a6f 100644
--- a/spacy/tests/stringstore/test_stringstore.py
+++ b/spacy/tests/stringstore/test_stringstore.py
@@ -13,7 +13,7 @@ def test_string_hash(stringstore):
     heart = '\U0001f499'
     print(heart)
     h = ss.add(heart)
-    assert h == 11841826740069053588L
+    assert h == 11841826740069053588
  
 
 def test_stringstore_from_api_docs(stringstore):

From e0a2aa92891af890738b6290cd622cc87fe652ac Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 16 Sep 2017 12:45:09 -0500
Subject: [PATCH 038/213] Support having word vectors data on GPU

---
 spacy/vectors.pyx | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/spacy/vectors.pyx b/spacy/vectors.pyx
index 0eec5a00a..b912be80b 100644
--- a/spacy/vectors.pyx
+++ b/spacy/vectors.pyx
@@ -6,6 +6,8 @@ import msgpack
 import msgpack_numpy
 msgpack_numpy.patch()
 cimport numpy as np
+from thinc.neural.util import get_array_module
+from thinc.neural._classes.model import Model
 
 from .typedefs cimport attr_t
 from .strings cimport StringStore
@@ -31,7 +33,7 @@ cdef class Vectors:
         self.i = 0
         self.data = data
         self.key2row = {}
-        self.keys = np.ndarray((self.data.shape[0],), dtype='uint64') 
+        self.keys = np.ndarray((self.data.shape[0],), dtype='uint64')
 
     def __reduce__(self):
         return (Vectors, (self.strings, self.data))
@@ -118,9 +120,14 @@ cdef class Vectors:
             self.data
 
     def to_disk(self, path, **exclude):
+        xp = get_array_module(self.data)
+        if xp is numpy:
+            save_array = lambda arr, file_: xp.save(file_, arr, allow_pickle=False)
+        else:
+            save_array = lambda arr, file_: xp.save(file_, arr)
         serializers = OrderedDict((
-            ('vectors', lambda p: numpy.save(p.open('wb'), self.data, allow_pickle=False)),
-            ('keys', lambda p: numpy.save(p.open('wb'), self.keys, allow_pickle=False)),
+            ('vectors', lambda p: save_array(self.data, p.open('wb'))),
+            ('keys', lambda p: xp.save(p.open('wb'), self.keys))
         ))
         return util.to_disk(path, serializers, exclude)
 
@@ -133,8 +140,9 @@ cdef class Vectors:
                     self.key2row[key] = i
 
         def load_vectors(path):
+            xp = Model.ops.xp
             if path.exists():
-                self.data = numpy.load(path)
+                self.data = xp.load(path)
 
         serializers = OrderedDict((
             ('keys', load_keys),

From 2a93404da683193683003cdd597e29496b21e31e Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 16 Sep 2017 12:45:37 -0500
Subject: [PATCH 039/213] Support optional pre-trained vectors in tensorizer
 model

---
 spacy/_ml.py | 41 +++++++++++++++++++++++++++--------------
 1 file changed, 27 insertions(+), 14 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 003541f4b..14341c407 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -21,7 +21,7 @@ from thinc.api import FeatureExtracter, with_getitem
 from thinc.neural.pooling import Pooling, max_pool, mean_pool, sum_pool
 from thinc.neural._classes.attention import ParametricAttention
 from thinc.linear.linear import LinearModel
-from thinc.api import uniqued, wrap, flatten_add_lengths
+from thinc.api import uniqued, wrap, flatten_add_lengths, noop
 
 
 from .attrs import ID, ORTH, LOWER, NORM, PREFIX, SUFFIX, SHAPE, TAG, DEP, CLUSTER
@@ -226,7 +226,7 @@ def drop_layer(layer, factor=2.):
     return model
 
 
-def Tok2Vec(width, embed_size, preprocess=None):
+def Tok2Vec(width, embed_size, preprocess=True, pretrained_dims=0):
     cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
     with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
         norm = HashEmbed(width, embed_size, column=cols.index(NORM), name='embed_norm')
@@ -234,18 +234,30 @@ def Tok2Vec(width, embed_size, preprocess=None):
         suffix = HashEmbed(width, embed_size//2, column=cols.index(SUFFIX), name='embed_suffix')
         shape = HashEmbed(width, embed_size//2, column=cols.index(SHAPE), name='embed_shape')
 
-        embed = (norm | prefix | suffix | shape ) >> LN(Maxout(width, width*4, pieces=3))
-        tok2vec = (
-            with_flatten(
-                asarray(Model.ops, dtype='uint64')
-                >> uniqued(embed, column=5)
-                >> Residual(
-                    (ExtractWindow(nW=1) >> LN(Maxout(width, width*3)))
-                ) ** 4, pad=4
+        trained_vectors = (
+            FeatureExtracter(cols)
+            >> with_flatten(
+                uniqued(
+                    (norm | prefix | suffix | shape)
+                    >> LN(Maxout(width, width*4, pieces=3)), column=5)
             )
         )
-        if preprocess not in (False, None):
-            tok2vec = preprocess >> tok2vec
+        if pretrained_dims:
+            embed = concatenate_lists(trained_vectors, SpacyVectors)
+        else:
+            embed = trained_vectors
+        convolution = Residual(ExtractWindow(nW=1) >> LN(Maxout(width, width*3, pieces=3)))
+
+        tok2vec = (
+            embed
+            >> with_flatten(
+                Affine(width, width+pretrained_dims)
+                >> convolution
+                >> convolution
+                >> convolution
+                >> convolution,
+                pad=1)
+        )
         # Work around thinc API limitations :(. TODO: Revise in Thinc 7
         tok2vec.nO = width
         tok2vec.embed = embed
@@ -457,10 +469,11 @@ def getitem(i):
 
 def build_tagger_model(nr_class, token_vector_width, **cfg):
     embed_size = util.env_opt('embed_size', 7500)
+    pretrained_dims = cfg.get('pretrained_dims', 0)
     with Model.define_operators({'>>': chain, '+': add}):
         # Input: (doc, tensor) tuples
-        private_tok2vec = Tok2Vec(token_vector_width, embed_size, preprocess=doc2feats())
-
+        private_tok2vec = Tok2Vec(token_vector_width, embed_size,
+                                  pretrained_dims=pretrained_dims)
         model = (
             fine_tune(private_tok2vec)
             >> with_flatten(

From 84e637e2e67c3b1b4509c94c08e00687b5881b8f Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 16 Sep 2017 12:46:02 -0500
Subject: [PATCH 040/213] Pass option for pretrained vectors in pipeline

---
 spacy/pipeline.pyx | 45 +++++++++++++++++++++++++--------------------
 1 file changed, 25 insertions(+), 20 deletions(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index 7e00a443d..4643b759a 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -41,7 +41,7 @@ from .syntax import nonproj
 from .compat import json_dumps
 
 from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS
-from ._ml import rebatch, Tok2Vec, flatten, get_col, doc2feats
+from ._ml import rebatch, Tok2Vec, flatten
 from ._ml import build_text_classifier, build_tagger_model
 from .parts_of_speech import X
 
@@ -137,6 +137,7 @@ class BaseThincComponent(object):
     def from_bytes(self, bytes_data, **exclude):
         def load_model(b):
             if self.model is True:
+                self.cfg['pretrained_dims'] = self.vocab.vectors_length
                 self.model = self.Model(**self.cfg)
             self.model.from_bytes(b)
 
@@ -159,6 +160,7 @@ class BaseThincComponent(object):
     def from_disk(self, path, **exclude):
         def load_model(p):
             if self.model is True:
+                self.cfg['pretrained_dims'] = self.vocab.vectors_length
                 self.model = self.Model(**self.cfg)
             self.model.from_bytes(p.open('rb').read())
 
@@ -193,7 +195,7 @@ class TokenVectorEncoder(BaseThincComponent):
         """
         width = util.env_opt('token_vector_width', width)
         embed_size = util.env_opt('embed_size', embed_size)
-        return Tok2Vec(width, embed_size, preprocess=None)
+        return Tok2Vec(width, embed_size, **cfg)
 
     def __init__(self, vocab, model=True, **cfg):
         """Construct a new statistical model. Weights are not allocated on
@@ -210,7 +212,6 @@ class TokenVectorEncoder(BaseThincComponent):
             >>> tok2vec.model = tok2vec.Model(128, 5000)
         """
         self.vocab = vocab
-        self.doc2feats = doc2feats()
         self.model = model
         self.cfg = dict(cfg)
 
@@ -245,8 +246,7 @@ class TokenVectorEncoder(BaseThincComponent):
         docs (iterable): A sequence of `Doc` objects.
         RETURNS (object): Vector representations for each token in the documents.
         """
-        feats = self.doc2feats(docs)
-        tokvecs = self.model(feats)
+        tokvecs = self.model(docs)
         return tokvecs
 
     def set_annotations(self, docs, tokvecses):
@@ -270,8 +270,7 @@ class TokenVectorEncoder(BaseThincComponent):
         """
         if isinstance(docs, Doc):
             docs = [docs]
-        feats = self.doc2feats(docs)
-        tokvecs, bp_tokvecs = self.model.begin_update(feats, drop=drop)
+        tokvecs, bp_tokvecs = self.model.begin_update(docs, drop=drop)
         return tokvecs, bp_tokvecs
 
     def get_loss(self, docs, golds, scores):
@@ -285,9 +284,8 @@ class TokenVectorEncoder(BaseThincComponent):
         gold_tuples (iterable): Gold-standard training data.
         pipeline (list): The pipeline the model is part of.
         """
-        self.doc2feats = doc2feats()
         if self.model is True:
-            self.model = self.Model()
+            self.model = self.Model(**self.cfg)
 
 
 class NeuralTagger(BaseThincComponent):
@@ -394,12 +392,14 @@ class NeuralTagger(BaseThincComponent):
                                           exc=vocab.morphology.exc)
         token_vector_width = pipeline[0].model.nO
         if self.model is True:
-            self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
+            self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
+                                    pretrained_dims=self.vocab.vectors_length)
 
     @classmethod
-    def Model(cls, n_tags, token_vector_width):
-        return build_tagger_model(n_tags, token_vector_width)
- 
+    def Model(cls, n_tags, token_vector_width, pretrained_dims=0):
+        return build_tagger_model(n_tags, token_vector_width,
+                                  pretrained_dims)
+
     def use_params(self, params):
         with self.model.use_params(params):
             yield
@@ -419,7 +419,8 @@ class NeuralTagger(BaseThincComponent):
             if self.model is True:
                 token_vector_width = util.env_opt('token_vector_width',
                         self.cfg.get('token_vector_width', 128))
-                self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
+                self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
+                                        pretrained_dims=self.vocab.vectors_length)
             self.model.from_bytes(b)
 
         def load_tag_map(b):
@@ -428,7 +429,7 @@ class NeuralTagger(BaseThincComponent):
                 self.vocab.strings, tag_map=tag_map,
                 lemmatizer=self.vocab.morphology.lemmatizer,
                 exc=self.vocab.morphology.exc)
- 
+
         deserialize = OrderedDict((
             ('vocab', lambda b: self.vocab.from_bytes(b)),
             ('tag_map', load_tag_map),
@@ -454,7 +455,8 @@ class NeuralTagger(BaseThincComponent):
             if self.model is True:
                 token_vector_width = util.env_opt('token_vector_width',
                         self.cfg.get('token_vector_width', 128))
-                self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
+                self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
+                                        pretrained_dims=self.vocab.vectors_length)
             self.model.from_bytes(p.open('rb').read())
 
         def load_tag_map(p):
@@ -503,12 +505,14 @@ class NeuralLabeller(NeuralTagger):
                         self.labels[dep] = len(self.labels)
         token_vector_width = pipeline[0].model.nO
         if self.model is True:
-            self.model = self.Model(len(self.labels), token_vector_width)
+            self.model = self.Model(len(self.labels), token_vector_width,
+                                    pretrained_dims=self.vocab.vectors_length)
 
     @classmethod
-    def Model(cls, n_tags, token_vector_width):
-        return build_tagger_model(n_tags, token_vector_width)
-    
+    def Model(cls, n_tags, token_vector_width, pretrained_dims=0):
+        return build_tagger_model(n_tags, token_vector_width,
+                                  pretrained_dims)
+
     def get_loss(self, docs, golds, scores):
         scores = self.model.ops.flatten(scores)
         cdef int idx = 0
@@ -653,6 +657,7 @@ class TextCategorizer(BaseThincComponent):
         else:
             token_vector_width = 64
         if self.model is True:
+            self.cfg['pretrained_dims'] = self.vocab.vectors_length
             self.model = self.Model(len(self.labels), token_vector_width,
                                     **self.cfg)
 

From e37a50a436643c201a7ecb088325c9b94593fabd Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 16 Sep 2017 12:46:36 -0500
Subject: [PATCH 041/213] Pass documents to tensorizer, not 'features'

---
 spacy/language.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/spacy/language.py b/spacy/language.py
index 66b42ff94..538d12221 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -304,13 +304,12 @@ class Language(object):
                 self._optimizer = Adam(Model.ops, 0.001)
             sgd = self._optimizer
         tok2vec = self.pipeline[0]
-        feats = tok2vec.doc2feats(docs)
         grads = {}
         def get_grads(W, dW, key=None):
             grads[key] = (W, dW)
         pipes = list(self.pipeline[1:])
         random.shuffle(pipes)
-        tokvecses, bp_tokvecses = tok2vec.model.begin_update(feats, drop=drop)
+        tokvecses, bp_tokvecses = tok2vec.model.begin_update(docs, drop=drop)
         all_d_tokvecses = [tok2vec.model.ops.allocate(tv.shape) for tv in tokvecses]
         for proc in pipes:
             if not hasattr(proc, 'update'):

From 8665a77f482a9c607a79cc285949c6cc049bf5f0 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 16 Sep 2017 12:46:57 -0500
Subject: [PATCH 042/213] Fix feature error in NER

---
 spacy/syntax/_state.pxd | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/spacy/syntax/_state.pxd b/spacy/syntax/_state.pxd
index 3da9e5d4c..c4be8cff2 100644
--- a/spacy/syntax/_state.pxd
+++ b/spacy/syntax/_state.pxd
@@ -101,9 +101,10 @@ cdef cppclass StateC:
         elif n == 6:
             if this.B(0) >= 0:
                 ids[0] = this.B(0)
+                ids[1] = this.B(0)-1
             else:
                 ids[0] = -1
-            ids[1] = this.B(0)
+                ids[1] = -1
             ids[2] = this.B(1)
             ids[3] = this.E(0)
             if ids[3] >= 1:

From 5ff2491f245c95e8003b84aeeef379cb8ef5676d Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 16 Sep 2017 12:47:21 -0500
Subject: [PATCH 043/213] Pass option for pre-trained vectors in parser

---
 spacy/syntax/nn_parser.pyx | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 1c4107c06..04cf20d12 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -245,7 +245,7 @@ cdef class Parser:
         parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2)
         embed_size = util.env_opt('embed_size', 4000)
         tensors = fine_tune(Tok2Vec(token_vector_width, embed_size,
-                                    preprocess=doc2feats()))
+                                    pretrained_dims=cfg.get('pretrained_dims')))
         if parser_maxout_pieces == 1:
             lower = PrecomputableAffine(hidden_width if depth >= 1 else nr_class,
                         nF=cls.nr_feature,
@@ -391,9 +391,10 @@ cdef class Parser:
         if isinstance(tokvecses, np.ndarray):
             tokvecses = [tokvecses]
 
-        tokvecs = self.model[0].ops.flatten(tokvecses)
         if USE_FINE_TUNE:
             tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
+        else:
+            tokvecs = self.model[0].ops.flatten(tokvecses)
 
         nr_state = len(docs)
         nr_class = self.moves.n_moves
@@ -451,9 +452,10 @@ cdef class Parser:
         cdef Doc doc
         cdef int nr_class = self.moves.n_moves
         cdef StateClass stcls, output
-        tokvecs = self.model[0].ops.flatten(tokvecses)
         if USE_FINE_TUNE:
             tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
+        else:
+            tokvecs = self.model[0].ops.flatten(tokvecses)
         cuda_stream = get_cuda_stream()
         state2vec, vec2scores = self.get_batch_model(len(docs), tokvecs,
                                                      cuda_stream, 0.0)
@@ -533,6 +535,8 @@ cdef class Parser:
         if USE_FINE_TUNE:
             my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
             tokvecs = self.model[0].ops.flatten(my_tokvecs)
+        else:
+            tokvecs = self.model[0].ops.flatten(docs_tokvecs[1])
 
         cuda_stream = get_cuda_stream()
 
@@ -603,11 +607,11 @@ cdef class Parser:
         docs, tokvecs = docs_tokvecs
         lengths = [len(d) for d in docs]
         assert min(lengths) >= 1
-        tokvecs = self.model[0].ops.flatten(tokvecs)
         if USE_FINE_TUNE:
             my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
             tokvecs += self.model[0].ops.flatten(my_tokvecs)
-
+        else:
+            tokvecs = self.model[0].ops.flatten(tokvecs)
         states = self.moves.init_batch(docs)
         for gold in golds:
             self.moves.preprocess_gold(gold)
@@ -775,6 +779,7 @@ cdef class Parser:
             for label in labels:
                 self.moves.add_action(action, label)
         if self.model is True:
+            cfg['pretrained_dims'] = self.vocab.vectors_length
             self.model, cfg = self.Model(self.moves.n_moves, **cfg)
             self.cfg.update(cfg)
 
@@ -856,9 +861,11 @@ cdef class Parser:
         msg = util.from_bytes(bytes_data, deserializers, exclude)
         if 'model' not in exclude:
             if self.model is True:
-                self.model, cfg = self.Model(self.moves.n_moves)
+                self.model, cfg = self.Model(self.moves.n_moves,
+                                    pretrained_dims=self.vocab.vectors_length)
             else:
                 cfg = {}
+            cfg['pretrained_dims'] = self.vocab.vectors_length
             if 'tok2vec_model' in msg:
                 self.model[0].from_bytes(msg['tok2vec_model'])
             if 'lower_model' in msg:

From 68f66aebf81eeebef3750a5c881795effb58d7d7 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Sat, 16 Sep 2017 20:27:59 +0200
Subject: [PATCH 044/213] Use pkg_resources instead of pip for is_package
 (resolves #1293)

---
 requirements.txt | 1 -
 setup.py         | 1 -
 spacy/util.py    | 6 +++---
 3 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 54c888a11..6298b1982 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -13,7 +13,6 @@ requests>=2.13.0,<3.0.0
 regex==2017.4.5
 ftfy>=4.4.2,<5.0.0
 pytest>=3.0.6,<4.0.0
-pip>=9.0.0,<10.0.0
 mock>=2.0.0,<3.0.0
 msgpack-python
 msgpack-numpy
diff --git a/setup.py b/setup.py
index 535dddd0d..0b0d2cc81 100755
--- a/setup.py
+++ b/setup.py
@@ -197,7 +197,6 @@ def setup_package():
                 'preshed>=1.0.0,<2.0.0',
                 'thinc>=6.8.1,<6.9.0',
                 'plac<1.0.0,>=0.9.6',
-                'pip>=9.0.0,<10.0.0',
                 'six',
                 'pathlib',
                 'ujson>=1.35',
diff --git a/spacy/util.py b/spacy/util.py
index 95fcb493d..a55eed2f8 100644
--- a/spacy/util.py
+++ b/spacy/util.py
@@ -3,7 +3,7 @@ from __future__ import unicode_literals, print_function
 
 import os
 import ujson
-import pip
+import pkg_resources
 import importlib
 import regex as re
 from pathlib import Path
@@ -180,9 +180,9 @@ def is_package(name):
     name (unicode): Name of package.
     RETURNS (bool): True if installed package, False if not.
     """
-    packages = pip.get_installed_distributions()
+    packages = pkg_resources.working_set.by_key.keys()
     for package in packages:
-        if package.project_name.replace('-', '_') == name:
+        if package.replace('-', '_') == name:
             return True
     return False
 

From ece30c28a88e55f29a22fbe5f93a9988cec44836 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Sat, 16 Sep 2017 20:40:15 +0200
Subject: [PATCH 045/213] Don't split hyphenated words in German

This way, the tokenizer matches the tokenization in German treebanks
---
 spacy/lang/de/__init__.py                     |  2 ++
 spacy/lang/de/punctuation.py                  | 20 ++++++++++++++++
 .../tests/lang/de/test_prefix_suffix_infix.py | 24 +++++++++----------
 spacy/tests/lang/de/test_text.py              | 14 +++++------
 4 files changed, 40 insertions(+), 20 deletions(-)
 create mode 100644 spacy/lang/de/punctuation.py

diff --git a/spacy/lang/de/__init__.py b/spacy/lang/de/__init__.py
index b8a7580a0..1c64541e6 100644
--- a/spacy/lang/de/__init__.py
+++ b/spacy/lang/de/__init__.py
@@ -3,6 +3,7 @@ from __future__ import unicode_literals
 
 from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
 from .norm_exceptions import NORM_EXCEPTIONS
+from .punctuation import TOKENIZER_INFIXES
 from .tag_map import TAG_MAP
 from .stop_words import STOP_WORDS
 from .lemmatizer import LOOKUP
@@ -23,6 +24,7 @@ class GermanDefaults(Language.Defaults):
                                          NORM_EXCEPTIONS, BASE_NORMS)
 
     tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
+    infixes = tuple(TOKENIZER_INFIXES)
     tag_map = dict(TAG_MAP)
     stop_words = set(STOP_WORDS)
     syntax_iterators = dict(SYNTAX_ITERATORS)
diff --git a/spacy/lang/de/punctuation.py b/spacy/lang/de/punctuation.py
new file mode 100644
index 000000000..7024ed118
--- /dev/null
+++ b/spacy/lang/de/punctuation.py
@@ -0,0 +1,20 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+from ..char_classes import LIST_ELLIPSES, LIST_ICONS
+from ..char_classes import QUOTES, ALPHA, ALPHA_LOWER, ALPHA_UPPER
+
+
+_quotes = QUOTES.replace("'", '')
+
+_infixes = (LIST_ELLIPSES + LIST_ICONS +
+            [r'(?<=[{}])\.(?=[{}])'.format(ALPHA_LOWER, ALPHA_UPPER),
+             r'(?<=[{a}])[,!?](?=[{a}])'.format(a=ALPHA),
+             r'(?<=[{a}"])[:<>=](?=[{a}])'.format(a=ALPHA),
+             r'(?<=[{a}]),(?=[{a}])'.format(a=ALPHA),
+             r'(?<=[{a}])([{q}\)\]\(\[])(?=[\{a}])'.format(a=ALPHA, q=_quotes),
+             r'(?<=[{a}])--(?=[{a}])'.format(a=ALPHA),
+             r'(?<=[0-9])-(?=[0-9])'])
+
+
+TOKENIZER_INFIXES = _infixes
diff --git a/spacy/tests/lang/de/test_prefix_suffix_infix.py b/spacy/tests/lang/de/test_prefix_suffix_infix.py
index dcf4f4ef0..bdc68037e 100644
--- a/spacy/tests/lang/de/test_prefix_suffix_infix.py
+++ b/spacy/tests/lang/de/test_prefix_suffix_infix.py
@@ -67,12 +67,6 @@ def test_tokenizer_splits_uneven_wrap_interact(de_tokenizer, text):
     assert len(tokens) == 4
 
 
-@pytest.mark.parametrize('text', ["blau-rot"])
-def test_tokenizer_splits_hyphens(de_tokenizer, text):
-    tokens = de_tokenizer(text)
-    assert len(tokens) == 3
-
-
 @pytest.mark.parametrize('text', ["0.1-13.5", "0.0-0.1", "103.27-300"])
 def test_tokenizer_splits_numeric_range(de_tokenizer, text):
     tokens = de_tokenizer(text)
@@ -100,17 +94,21 @@ def test_tokenizer_splits_ellipsis_infix(de_tokenizer, text):
     assert len(tokens) == 3
 
 
+@pytest.mark.parametrize('text', ['Islam-Konferenz', 'Ost-West-Konflikt'])
+def test_tokenizer_keeps_hyphens(de_tokenizer, text):
+    tokens = de_tokenizer(text)
+    assert len(tokens) == 1
+
+
 def test_tokenizer_splits_double_hyphen_infix(de_tokenizer):
     tokens = de_tokenizer("Viele Regeln--wie die Bindestrich-Regeln--sind kompliziert.")
-    assert len(tokens) == 12
+    assert len(tokens) == 10
     assert tokens[0].text == "Viele"
     assert tokens[1].text == "Regeln"
     assert tokens[2].text == "--"
     assert tokens[3].text == "wie"
     assert tokens[4].text == "die"
-    assert tokens[5].text == "Bindestrich"
-    assert tokens[6].text == "-"
-    assert tokens[7].text == "Regeln"
-    assert tokens[8].text == "--"
-    assert tokens[9].text == "sind"
-    assert tokens[10].text == "kompliziert"
+    assert tokens[5].text == "Bindestrich-Regeln"
+    assert tokens[6].text == "--"
+    assert tokens[7].text == "sind"
+    assert tokens[8].text == "kompliziert"
diff --git a/spacy/tests/lang/de/test_text.py b/spacy/tests/lang/de/test_text.py
index 84fa6f2a5..34180b982 100644
--- a/spacy/tests/lang/de/test_text.py
+++ b/spacy/tests/lang/de/test_text.py
@@ -25,15 +25,15 @@ Umfang kläglich dünnen Beine flimmerten ihm hilflos vor den Augen.
     assert len(tokens) == 109
 
 
-@pytest.mark.parametrize('text,length', [
-    ("Donaudampfschifffahrtsgesellschaftskapitänsanwärterposten", 1),
-    ("Rindfleischetikettierungsüberwachungsaufgabenübertragungsgesetz", 1),
-    ("Kraftfahrzeug-Haftpflichtversicherung", 3),
-    ("Vakuum-Mittelfrequenz-Induktionsofen", 5)
+@pytest.mark.parametrize('text', [
+    "Donaudampfschifffahrtsgesellschaftskapitänsanwärterposten",
+    "Rindfleischetikettierungsüberwachungsaufgabenübertragungsgesetz",
+    "Kraftfahrzeug-Haftpflichtversicherung",
+    "Vakuum-Mittelfrequenz-Induktionsofen"
     ])
-def test_tokenizer_handles_long_words(de_tokenizer, text, length):
+def test_tokenizer_handles_long_words(de_tokenizer, text):
     tokens = de_tokenizer(text)
-    assert len(tokens) == length
+    assert len(tokens) == 1
 
 
 @pytest.mark.parametrize('text,length', [

From c003c561c34daa216f3a96176bc3e739cff4439a Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 17 Sep 2017 05:46:03 -0500
Subject: [PATCH 046/213] Revert NER action loading change, for model
 compatibility

---
 spacy/syntax/ner.pyx | 44 ++++++++++++++++++++++----------------------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/spacy/syntax/ner.pyx b/spacy/syntax/ner.pyx
index 1a174aba8..d8c748099 100644
--- a/spacy/syntax/ner.pyx
+++ b/spacy/syntax/ner.pyx
@@ -219,28 +219,28 @@ cdef class BiluoPushDown(TransitionSystem):
             raise Exception(move)
         return t
 
-    def add_action(self, int action, label_name):
-        cdef attr_t label_id
-        if not isinstance(label_name, (int, long)):
-            label_id = self.strings.add(label_name)
-        else:
-            label_id = label_name
-        if action == OUT and label_id != 0:
-            return
-        if action == MISSING or action == ISNT:
-            return
-        # Check we're not creating a move we already have, so that this is
-        # idempotent
-        for trans in self.c[:self.n_moves]:
-            if trans.move == action and trans.label == label_id:
-                return 0
-        if self.n_moves >= self._size:
-            self._size *= 2
-            self.c = <Transition*>self.mem.realloc(self.c, self._size * sizeof(self.c[0]))
-        self.c[self.n_moves] = self.init_transition(self.n_moves, action, label_id)
-        assert self.c[self.n_moves].label == label_id
-        self.n_moves += 1
-        return 1
+    #def add_action(self, int action, label_name):
+    #    cdef attr_t label_id
+    #    if not isinstance(label_name, (int, long)):
+    #        label_id = self.strings.add(label_name)
+    #    else:
+    #        label_id = label_name
+    #    if action == OUT and label_id != 0:
+    #        return
+    #    if action == MISSING or action == ISNT:
+    #        return
+    #    # Check we're not creating a move we already have, so that this is
+    #    # idempotent
+    #    for trans in self.c[:self.n_moves]:
+    #        if trans.move == action and trans.label == label_id:
+    #            return 0
+    #    if self.n_moves >= self._size:
+    #        self._size *= 2
+    #        self.c = <Transition*>self.mem.realloc(self.c, self._size * sizeof(self.c[0]))
+    #    self.c[self.n_moves] = self.init_transition(self.n_moves, action, label_id)
+    #    assert self.c[self.n_moves].label == label_id
+    #    self.n_moves += 1
+    #    return 1
 
 
 

From 8f913a74ca4eaa2e36354530bfe8e822b1697777 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 17 Sep 2017 05:46:36 -0500
Subject: [PATCH 047/213] Fix defaults and args to build_tagger_model

---
 spacy/_ml.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 14341c407..2f6a36942 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -467,9 +467,8 @@ def getitem(i):
         return X[i], None
     return layerize(getitem_fwd)
 
-def build_tagger_model(nr_class, token_vector_width, **cfg):
-    embed_size = util.env_opt('embed_size', 7500)
-    pretrained_dims = cfg.get('pretrained_dims', 0)
+def build_tagger_model(nr_class, token_vector_width, pretrained_dims=0, **cfg):
+    embed_size = util.env_opt('embed_size', 4000)
     with Model.define_operators({'>>': chain, '+': add}):
         # Input: (doc, tensor) tuples
         private_tok2vec = Tok2Vec(token_vector_width, embed_size,

From 31c2e91c35ae4299f53b2ca411cd6686cc86748c Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 17 Sep 2017 05:46:55 -0500
Subject: [PATCH 048/213] Fix wiring of pre-trained vectors in parser loading

---
 spacy/syntax/nn_parser.pyx | 1 +
 1 file changed, 1 insertion(+)

diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index bf873f0e2..cf18b6e96 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -825,6 +825,7 @@ cdef class Parser:
         if 'model' not in exclude:
             path = util.ensure_path(path)
             if self.model is True:
+                self.cfg['pretrained_dims'] = self.vocab.vectors_length
                 self.model, cfg = self.Model(**self.cfg)
             else:
                 cfg = {}

From 2b0efc77ae3bd4ab97353b4f55be7ae36e8523ad Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 17 Sep 2017 05:47:34 -0500
Subject: [PATCH 049/213] Fix wiring of pre-trained vectors in parser loading

---
 spacy/syntax/nn_parser.pyx | 1 +
 1 file changed, 1 insertion(+)

diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index cf18b6e96..532a69b36 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -871,6 +871,7 @@ cdef class Parser:
             if self.model is True:
                 self.model, cfg = self.Model(self.moves.n_moves,
                                     pretrained_dims=self.vocab.vectors_length)
+                cfg['pretrained_dims'] = self.vocab.vectors_length
             else:
                 cfg = {}
             cfg['pretrained_dims'] = self.vocab.vectors_length

From 16122f566ea18eb87880e51f6a119d9f28401fdc Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 17 Sep 2017 12:28:53 -0500
Subject: [PATCH 050/213] Fix cpdef enum in attrs.pyx

---
 spacy/attrs.pxd | 2 +-
 spacy/attrs.pyx | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/spacy/attrs.pxd b/spacy/attrs.pxd
index a8ee9cac0..74397fa64 100644
--- a/spacy/attrs.pxd
+++ b/spacy/attrs.pxd
@@ -1,5 +1,5 @@
 # Reserve 64 values for flag features
-cpdef enum attr_id_t:
+cdef enum attr_id_t:
     NULL_ATTR
     IS_ALPHA
     IS_ASCII
diff --git a/spacy/attrs.pyx b/spacy/attrs.pyx
index ba95e1e72..8efd9e189 100644
--- a/spacy/attrs.pyx
+++ b/spacy/attrs.pyx
@@ -94,6 +94,7 @@ IDS = {
 
 # ATTR IDs, in order of the symbol
 NAMES = [key for key, value in sorted(IDS.items(), key=lambda item: item[1])]
+locals().update(IDS)
 
 
 def intify_attrs(stringy_attrs, strings_map=None, _do_deprecated=False):

From 4f38a67a89168309642525ba56f71d692900f577 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 17 Sep 2017 12:29:14 -0500
Subject: [PATCH 051/213] Make width default to 0 in vectors.pyx

---
 spacy/vectors.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/vectors.pyx b/spacy/vectors.pyx
index b912be80b..346421153 100644
--- a/spacy/vectors.pyx
+++ b/spacy/vectors.pyx
@@ -23,7 +23,7 @@ cdef class Vectors:
     cdef public object keys
     cdef public int i
 
-    def __init__(self, strings, data_or_width):
+    def __init__(self, strings, data_or_width=0):
         self.strings = StringStore()
         if isinstance(data_or_width, int):
             self.data = data = numpy.zeros((len(strings), data_or_width),

From 039d609362138bd26d6c53f087a48c0888d9c887 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 17 Sep 2017 12:29:39 -0500
Subject: [PATCH 052/213] Remove hard-coded default vectors width

---
 spacy/vocab.pyx | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx
index bf7fb6903..b4a244287 100644
--- a/spacy/vocab.pyx
+++ b/spacy/vocab.pyx
@@ -65,7 +65,7 @@ cdef class Vocab:
                 self.strings.add(name)
         self.lex_attr_getters = lex_attr_getters
         self.morphology = Morphology(self.strings, tag_map, lemmatizer)
-        self.vectors = Vectors(self.strings, 300)
+        self.vectors = Vectors(self.strings)
 
     property lang:
         def __get__(self):
@@ -336,7 +336,7 @@ cdef class Vocab:
                 return None
             else:
                 return self.vectors.to_bytes(exclude='strings.json')
- 
+
         getters = OrderedDict((
             ('strings', lambda: self.strings.to_bytes()),
             ('lexemes', lambda: self.lexemes_to_bytes()),

From 8f42f8d305787cb74bf7d85ef16c9cd40b948895 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 17 Sep 2017 12:30:16 -0500
Subject: [PATCH 053/213] Remove unused 'preprocess' argument in Tok2Vec'

---
 spacy/_ml.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 2f6a36942..2b9a98fd2 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -226,7 +226,7 @@ def drop_layer(layer, factor=2.):
     return model
 
 
-def Tok2Vec(width, embed_size, preprocess=True, pretrained_dims=0):
+def Tok2Vec(width, embed_size, pretrained_dims=0):
     cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
     with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
         norm = HashEmbed(width, embed_size, column=cols.index(NORM), name='embed_norm')
@@ -242,7 +242,7 @@ def Tok2Vec(width, embed_size, preprocess=True, pretrained_dims=0):
                     >> LN(Maxout(width, width*4, pieces=3)), column=5)
             )
         )
-        if pretrained_dims:
+        if pretrained_dims >= 1:
             embed = concatenate_lists(trained_vectors, SpacyVectors)
         else:
             embed = trained_vectors

From c013e5996f3aec8fe6813f1af4386637c29114ec Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 17 Sep 2017 13:13:20 -0500
Subject: [PATCH 054/213] Fix parser test

---
 spacy/tests/parser/test_neural_parser.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/tests/parser/test_neural_parser.py b/spacy/tests/parser/test_neural_parser.py
index 30a6367c8..29350b30a 100644
--- a/spacy/tests/parser/test_neural_parser.py
+++ b/spacy/tests/parser/test_neural_parser.py
@@ -26,7 +26,7 @@ def arc_eager(vocab):
 
 @pytest.fixture
 def tok2vec():
-    return Tok2Vec(8, 100, preprocess=doc2feats())
+    return Tok2Vec(8, 100)
 
 
 @pytest.fixture

From 2148ae605b193ca1ff9859a5fc717989b6613034 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 17 Sep 2017 17:36:04 -0500
Subject: [PATCH 055/213] Dont use iterated convolutions

---
 spacy/_ml.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 2b9a98fd2..ce818c8e1 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -252,11 +252,8 @@ def Tok2Vec(width, embed_size, pretrained_dims=0):
             embed
             >> with_flatten(
                 Affine(width, width+pretrained_dims)
-                >> convolution
-                >> convolution
-                >> convolution
-                >> convolution,
-                pad=1)
+                >> convolution ** 4,
+                pad=4)
         )
         # Work around thinc API limitations :(. TODO: Revise in Thinc 7
         tok2vec.nO = width

From 2480f8f521ea22b74ff90aed0853e9c0e2543b1b Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Mon, 18 Sep 2017 15:31:57 +0200
Subject: [PATCH 056/213] Add missing return in Doc.from_disk() (closes #1330)

---
 spacy/tokens/doc.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/tokens/doc.pyx b/spacy/tokens/doc.pyx
index 3b1f38b68..fcb5a16fa 100644
--- a/spacy/tokens/doc.pyx
+++ b/spacy/tokens/doc.pyx
@@ -660,7 +660,7 @@ cdef class Doc:
         """
         with path.open('rb') as file_:
             bytes_data = file_.read()
-        self.from_bytes(bytes_data, **exclude)
+        return self.from_bytes(bytes_data, **exclude)
 
     def to_bytes(self, **exclude):
         """Serialize, i.e. export the document contents to a binary string.

From 7b3f391f80a8c30b815cd202f5340933280120d5 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 18 Sep 2017 11:35:59 -0500
Subject: [PATCH 057/213] Try dropping the Affine layer, conditionally

---
 spacy/_ml.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index ce818c8e1..dde78af8a 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -251,7 +251,8 @@ def Tok2Vec(width, embed_size, pretrained_dims=0):
         tok2vec = (
             embed
             >> with_flatten(
-                Affine(width, width+pretrained_dims)
+                (Affine(width, width+pretrained_dims)
+                 if pretrained_dims else noop())
                 >> convolution ** 4,
                 pad=4)
         )

From 3fa76c17d19b49162652976207e030e484888f02 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 18 Sep 2017 15:00:05 -0500
Subject: [PATCH 058/213] Refactor Tok2Vec

---
 spacy/_ml.py | 28 ++++++++++++++++------------
 1 file changed, 16 insertions(+), 12 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index dde78af8a..d79d6e39b 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -242,20 +242,24 @@ def Tok2Vec(width, embed_size, pretrained_dims=0):
                     >> LN(Maxout(width, width*4, pieces=3)), column=5)
             )
         )
-        if pretrained_dims >= 1:
-            embed = concatenate_lists(trained_vectors, SpacyVectors)
-        else:
-            embed = trained_vectors
         convolution = Residual(ExtractWindow(nW=1) >> LN(Maxout(width, width*3, pieces=3)))
 
-        tok2vec = (
-            embed
-            >> with_flatten(
-                (Affine(width, width+pretrained_dims)
-                 if pretrained_dims else noop())
-                >> convolution ** 4,
-                pad=4)
-        )
+        if pretrained_dims >= 1:
+            embed = concatenate_lists(trained_vectors, SpacyVectors)
+            tok2vec = (
+                embed
+                >> with_flatten(
+                    Affine(width, width+pretrained_dims)
+                    >> convolution ** 4,
+                    pad=4)
+            )
+        else:
+            embed = trained_vectors
+            tok2vec = (
+                embed
+                >> with_flatten(convolution ** 4, pad=4)
+            )
+
         # Work around thinc API limitations :(. TODO: Revise in Thinc 7
         tok2vec.nO = width
         tok2vec.embed = embed

From c858927271b8e28e587c067e53b3d3b66c2e1559 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 18 Sep 2017 18:04:16 -0500
Subject: [PATCH 059/213] Copy vectors to GPU on begin training

---
 spacy/language.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/spacy/language.py b/spacy/language.py
index 2a5558824..25648ed42 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -364,6 +364,9 @@ class Language(object):
             device.use()
             Model.ops = CupyOps()
             Model.Ops = CupyOps
+            if self.vocab.vectors.data.shape[1] >= 1:
+                self.vocab.vectors.data = Model.ops.asarray(
+                    self.vocab.vectors.data)
         else:
             device = None
         for proc in self.pipeline:

From a0c4b33d0354fa841c93293de248bf6cf294e80d Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 18 Sep 2017 18:04:47 -0500
Subject: [PATCH 060/213] Support resuming a model during spacy train

---
 spacy/cli/train.py | 26 ++++++++++++++++++--------
 1 file changed, 18 insertions(+), 8 deletions(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 7ad94ce9c..8a3446cfe 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -53,7 +53,6 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
     if dev_path and not dev_path.exists():
         prints(dev_path, title="Development data not found", exits=1)
 
-    lang_class = util.get_lang_class(lang)
 
     pipeline = ['token_vectors', 'tags', 'dependencies', 'entities']
     if no_tagger and 'tags' in pipeline: pipeline.remove('tags')
@@ -71,22 +70,22 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                                    util.env_opt('batch_to', 64),
                                    util.env_opt('batch_compound', 1.001))
 
-    if resume:
-        prints(output_path / 'model9.pickle', title="Resuming training")
-        nlp = dill.load((output_path / 'model9.pickle').open('rb'))
-    else:
+    if not resume:
+        lang_class = util.get_lang_class(lang)
         nlp = lang_class(pipeline=pipeline)
+    else:
+        print("Load resume")
+        nlp = _resume_model(lang, pipeline)
+        lang_class = nlp.__class__
+
     corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
     n_train_words = corpus.count_train()
-
     optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
     nlp._optimizer = None
 
     print("Itn.\tLoss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
     try:
         for i in range(n_iter):
-            if resume:
-                i += 20
             with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
                 train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
                                                gold_preproc=gold_preproc, max_length=0)
@@ -120,6 +119,17 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                 dill.dump(nlp, file_, -1)
 
 
+def _resume_model(lang, pipeline):
+    nlp = util.load_model(lang)
+    pipes = {getattr(pipe, 'name', None) for pipe in nlp.pipeline}
+    for name in pipeline:
+        if name not in pipes:
+            factory = nlp.Defaults.factories[name]
+            nlp.pipeline.extend(factory(nlp))
+    nlp.meta['pipeline'] = pipeline
+    return nlp
+
+
 def _render_parses(i, to_render):
     to_render[0].user_data['title'] = "Batch %d" % i
     with Path('/tmp/entities.html').open('w') as file_:

From 40837b275d9ed028bbdbe10ecaeb7af5d395f8eb Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 18 Sep 2017 18:05:38 -0500
Subject: [PATCH 061/213] Fix tensorizer with pretrained vectors

---
 spacy/pipeline.pyx | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index 4643b759a..824b6932d 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -285,7 +285,9 @@ class TokenVectorEncoder(BaseThincComponent):
         pipeline (list): The pipeline the model is part of.
         """
         if self.model is True:
-            self.model = self.Model(**self.cfg)
+            self.model = self.Model(
+                pretrained_dims=self.vocab.vectors_length,
+                **self.cfg)
 
 
 class NeuralTagger(BaseThincComponent):

From 2489dcaccf6fe4551a05804d982408d2899ce70f Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 19 Sep 2017 23:42:12 +0200
Subject: [PATCH 062/213] Fix serialization of parser

---
 spacy/syntax/nn_parser.pyx | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 532a69b36..52e677390 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -307,6 +307,8 @@ cdef class Parser:
             cfg['beam_width'] = util.env_opt('beam_width', 1)
         if 'beam_density' not in cfg:
             cfg['beam_density'] = util.env_opt('beam_density', 0.0)
+        if 'pretrained_dims' not in cfg:
+            cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
         self.cfg = cfg
         if 'actions' in self.cfg:
             for action, labels in self.cfg.get('actions', {}).items():

From b36a38f63dda5e229212e8c10521e7f21fe418fd Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 19 Sep 2017 23:42:27 +0200
Subject: [PATCH 063/213] Fix serialization of pretrained_dims property

---
 spacy/pipeline.pyx | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index 824b6932d..29670c155 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -214,6 +214,7 @@ class TokenVectorEncoder(BaseThincComponent):
         self.vocab = vocab
         self.model = model
         self.cfg = dict(cfg)
+        self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
 
     def __call__(self, doc):
         """Add context-sensitive vectors to a `Doc`, e.g. from a CNN or LSTM
@@ -458,7 +459,7 @@ class NeuralTagger(BaseThincComponent):
                 token_vector_width = util.env_opt('token_vector_width',
                         self.cfg.get('token_vector_width', 128))
                 self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
-                                        pretrained_dims=self.vocab.vectors_length)
+                                        **self.cfg)
             self.model.from_bytes(p.open('rb').read())
 
         def load_tag_map(p):
@@ -470,10 +471,10 @@ class NeuralTagger(BaseThincComponent):
                 exc=self.vocab.morphology.exc)
 
         deserialize = OrderedDict((
+            ('cfg', lambda p: self.cfg.update(_load_cfg(p))),
             ('vocab', lambda p: self.vocab.from_disk(p)),
             ('tag_map', load_tag_map),
             ('model', load_model),
-            ('cfg', lambda p: self.cfg.update(_load_cfg(p)))
         ))
         util.from_disk(path, deserialize, exclude)
         return self

From 78301b2d29fe0c37fb05d98b3cf141cc3d6b060a Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 00:19:34 +0200
Subject: [PATCH 064/213] Avoid comparison to None in Tok2Vec

---
 spacy/_ml.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index d79d6e39b..eaf72b44f 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -227,6 +227,8 @@ def drop_layer(layer, factor=2.):
 
 
 def Tok2Vec(width, embed_size, pretrained_dims=0):
+    if pretrained_dims is None:
+        pretrained_dims = 0
     cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
     with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
         norm = HashEmbed(width, embed_size, column=cols.index(NORM), name='embed_norm')

From 828cc91545458613dff701e804eaec442423e739 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 21:54:31 +0200
Subject: [PATCH 065/213] Fix PhraseMatcher for spaCy 2

---
 spacy/matcher.pyx | 23 ++++++++++-------------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/spacy/matcher.pyx b/spacy/matcher.pyx
index c75d23957..d321218b8 100644
--- a/spacy/matcher.pyx
+++ b/spacy/matcher.pyx
@@ -426,7 +426,7 @@ cdef class PhraseMatcher:
         self._phrase_key = <attr_t*>self.mem.alloc(max_length, sizeof(attr_t))
         self.max_length = max_length
         self.vocab = vocab
-        self.matcher = Matcher(self.vocab, {})
+        self.matcher = Matcher(self.vocab)
         self.phrase_ids = PreshMap()
         for phrase in phrases:
             if len(phrase) < max_length:
@@ -435,7 +435,7 @@ cdef class PhraseMatcher:
         abstract_patterns = []
         for length in range(1, max_length):
             abstract_patterns.append([{tag: True} for tag in get_bilou(length)])
-        self.matcher.add('Candidate', 'MWE', {}, abstract_patterns, acceptor=self.accept_match)
+        self.matcher.add('Candidate', None, *abstract_patterns)
 
     def add(self, Doc tokens):
         cdef int length = tokens.length
@@ -454,22 +454,19 @@ cdef class PhraseMatcher:
         self.phrase_ids[key] = True
 
     def __call__(self, Doc doc):
-        matches = []
-        for ent_id, label, start, end in self.matcher(doc):
-            cand = doc[start : end]
-            start = cand[0].idx
-            end = cand[-1].idx + len(cand[-1])
-            matches.append((start, end, cand.root.tag_, cand.text, 'MWE'))
-        for match in matches:
-            doc.merge(*match)
-        return matches
+        matches = self.matcher(doc)
+        accepted = []
+        for ent_id, start, end in matches:
+            if self.accept_match(doc, ent_id, start, end):
+                accepted.append((ent_id, start, end))
+        return accepted
 
     def pipe(self, stream, batch_size=1000, n_threads=2):
         for doc in stream:
             self(doc)
             yield doc
 
-    def accept_match(self, Doc doc, attr_t ent_id, attr_t label, int start, int end):
+    def accept_match(self, Doc doc, attr_t ent_id, int start, int end):
         assert (end - start) < self.max_length
         cdef int i, j
         for i in range(self.max_length):
@@ -478,6 +475,6 @@ cdef class PhraseMatcher:
             self._phrase_key[i] = doc.c[j].lex.orth
         cdef hash_t key = hash64(self._phrase_key, self.max_length * sizeof(attr_t), 0)
         if self.phrase_ids.get(key):
-            return (ent_id, label, start, end)
+            return True
         else:
             return False

From 43ad250dd5c4a9731acf648a40b8218fc677df81 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 21:54:49 +0200
Subject: [PATCH 066/213] Update matcher tests

---
 spacy/tests/test_matcher.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/spacy/tests/test_matcher.py b/spacy/tests/test_matcher.py
index 388aab03e..651707019 100644
--- a/spacy/tests/test_matcher.py
+++ b/spacy/tests/test_matcher.py
@@ -34,7 +34,6 @@ def test_matcher_from_api_docs(en_vocab):
     assert len(patterns[0])
 
 
-@pytest.mark.xfail
 def test_matcher_from_usage_docs(en_vocab):
     text = "Wow 😀 This is really cool! 😂 😂"
     doc = get_doc(en_vocab, words=text.split(' '))
@@ -46,7 +45,8 @@ def test_matcher_from_usage_docs(en_vocab):
         if doc.vocab.strings[match_id] == 'HAPPY':
             doc.sentiment += 0.1
         span = doc[start : end]
-        token = span.merge(norm='happy emoji')
+        token = span.merge()
+        token.vocab[token.text].norm_ = 'happy emoji'
 
     matcher = Matcher(en_vocab)
     matcher.add('HAPPY', label_sentiment, *pos_patterns)
@@ -98,7 +98,6 @@ def test_matcher_match_multi(matcher):
                             (doc.vocab.strings['Java'], 5, 6)]
 
 
-@pytest.mark.xfail
 def test_matcher_phrase_matcher(en_vocab):
     words = ["Google", "Now"]
     doc = get_doc(en_vocab, words)

From cc408fc1898b7693a3130483e51119e9d78d0693 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 22:20:35 +0200
Subject: [PATCH 067/213] Make PhraseMatcher API like Matcher API

---
 spacy/matcher.pyx           | 72 ++++++++++++++++++++++---------------
 spacy/tests/test_matcher.py |  3 +-
 2 files changed, 46 insertions(+), 29 deletions(-)

diff --git a/spacy/matcher.pyx b/spacy/matcher.pyx
index d321218b8..ba3559966 100644
--- a/spacy/matcher.pyx
+++ b/spacy/matcher.pyx
@@ -421,52 +421,67 @@ cdef class PhraseMatcher:
     cdef int max_length
     cdef attr_t* _phrase_key
 
-    def __init__(self, Vocab vocab, phrases, max_length=10):
+    cdef public object _callbacks
+
+    def __init__(self, Vocab vocab, max_length=10):
         self.mem = Pool()
         self._phrase_key = <attr_t*>self.mem.alloc(max_length, sizeof(attr_t))
         self.max_length = max_length
         self.vocab = vocab
         self.matcher = Matcher(self.vocab)
         self.phrase_ids = PreshMap()
-        for phrase in phrases:
-            if len(phrase) < max_length:
-                self.add(phrase)
-
         abstract_patterns = []
         for length in range(1, max_length):
             abstract_patterns.append([{tag: True} for tag in get_bilou(length)])
         self.matcher.add('Candidate', None, *abstract_patterns)
+        self._callbacks = {}
 
-    def add(self, Doc tokens):
-        cdef int length = tokens.length
-        assert length < self.max_length
-        tags = get_bilou(length)
-        assert len(tags) == length, length
+    def add(self, key, on_match, *docs):
+        cdef Doc doc
+        for doc in docs:
+            if len(doc) >= self.max_length:
+                msg = (
+                    "Pattern length (%d) >= phrase_matcher.max_length (%d). "
+                    "Length can be set on initialization, up to 10."
+                )
+                raise ValueError(msg % (len(doc), self.max_length))
+        cdef hash_t ent_id = self.matcher._normalize_key(key)
+        self._callbacks[ent_id] = on_match
 
+        cdef int length
         cdef int i
-        for i in range(self.max_length):
-            self._phrase_key[i] = 0
-        for i, tag in enumerate(tags):
-            lexeme = self.vocab[tokens.c[i].lex.orth]
-            lexeme.set_flag(tag, True)
-            self._phrase_key[i] = lexeme.orth
-        cdef hash_t key = hash64(self._phrase_key, self.max_length * sizeof(attr_t), 0)
-        self.phrase_ids[key] = True
+        cdef hash_t phrase_hash
+        for doc in docs:
+            length = doc.length
+            tags = get_bilou(length)
+            for i in range(self.max_length):
+                self._phrase_key[i] = 0
+            for i, tag in enumerate(tags):
+                lexeme = self.vocab[doc.c[i].lex.orth]
+                lexeme.set_flag(tag, True)
+                self._phrase_key[i] = lexeme.orth
+            phrase_hash = hash64(self._phrase_key,
+                                 self.max_length * sizeof(attr_t), 0)
+            self.phrase_ids[phrase_hash] = ent_id
 
     def __call__(self, Doc doc):
-        matches = self.matcher(doc)
-        accepted = []
-        for ent_id, start, end in matches:
-            if self.accept_match(doc, ent_id, start, end):
-                accepted.append((ent_id, start, end))
-        return accepted
+        matches = []
+        for _, start, end in self.matcher(doc):
+            ent_id = self.accept_match(doc, start, end)
+            if ent_id is not None:
+                matches.append((ent_id, start, end))
+        for i, (ent_id, start, end) in enumerate(matches):
+            on_match = self._callbacks.get(ent_id)
+            if on_match is not None:
+                on_match(self, doc, i, matches)
+        return matches
 
     def pipe(self, stream, batch_size=1000, n_threads=2):
         for doc in stream:
             self(doc)
             yield doc
 
-    def accept_match(self, Doc doc, attr_t ent_id, int start, int end):
+    def accept_match(self, Doc doc, int start, int end):
         assert (end - start) < self.max_length
         cdef int i, j
         for i in range(self.max_length):
@@ -474,7 +489,8 @@ cdef class PhraseMatcher:
         for i, j in enumerate(range(start, end)):
             self._phrase_key[i] = doc.c[j].lex.orth
         cdef hash_t key = hash64(self._phrase_key, self.max_length * sizeof(attr_t), 0)
-        if self.phrase_ids.get(key):
-            return True
+        ent_id = <hash_t>self.phrase_ids.get(key)
+        if ent_id == 0:
+            return None
         else:
-            return False
+            return ent_id
diff --git a/spacy/tests/test_matcher.py b/spacy/tests/test_matcher.py
index 651707019..1b9f92519 100644
--- a/spacy/tests/test_matcher.py
+++ b/spacy/tests/test_matcher.py
@@ -101,7 +101,8 @@ def test_matcher_match_multi(matcher):
 def test_matcher_phrase_matcher(en_vocab):
     words = ["Google", "Now"]
     doc = get_doc(en_vocab, words)
-    matcher = PhraseMatcher(en_vocab, [doc])
+    matcher = PhraseMatcher(en_vocab)
+    matcher.add('COMPANY', None, doc)
     words = ["I", "like", "Google", "Now", "best"]
     doc = get_doc(en_vocab, words)
     assert len(matcher(doc)) == 1

From 0c93c73e496f9c57da523393e33a6f88aa3eac25 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 22:26:40 +0200
Subject: [PATCH 068/213] Add __reduce__ method for PhraseMatcher

---
 spacy/matcher.pyx | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/spacy/matcher.pyx b/spacy/matcher.pyx
index ba3559966..ef4044d21 100644
--- a/spacy/matcher.pyx
+++ b/spacy/matcher.pyx
@@ -422,6 +422,7 @@ cdef class PhraseMatcher:
     cdef attr_t* _phrase_key
 
     cdef public object _callbacks
+    cdef public object _patterns
 
     def __init__(self, Vocab vocab, max_length=10):
         self.mem = Pool()
@@ -436,6 +437,9 @@ cdef class PhraseMatcher:
         self.matcher.add('Candidate', None, *abstract_patterns)
         self._callbacks = {}
 
+    def __reduce__(self):
+        return (self.__class__, (self.vocab,), None, None)
+
     def add(self, key, on_match, *docs):
         cdef Doc doc
         for doc in docs:

From 01858e9b5972a8c1dec86f88eef3f17fea63cdc6 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 22:51:41 +0200
Subject: [PATCH 069/213] Fix PhraseMatcher example

---
 examples/multi_word_matches.py | 74 +++++++++++++++++-----------------
 1 file changed, 37 insertions(+), 37 deletions(-)

diff --git a/examples/multi_word_matches.py b/examples/multi_word_matches.py
index 73f48bf42..ca9b0cc92 100644
--- a/examples/multi_word_matches.py
+++ b/examples/multi_word_matches.py
@@ -20,72 +20,72 @@ The algorithm is O(n) at run-time for document of length n because we're only ev
 matching over the tag patterns. So no matter how many phrases we're looking for,
 our pattern set stays very small (exact size depends on the maximum length we're
 looking for, as the query language currently has no quantifiers)
+
+The example expects a .bz2 file from the Reddit corpus, and a patterns file,
+formatted in jsonl as a sequence of entries like this:
+
+{"text":"Anchorage"}
+{"text":"Angola"}
+{"text":"Ann Arbor"}
+{"text":"Annapolis"}
+{"text":"Appalachia"}
+{"text":"Argentina"}
 """
 from __future__ import print_function, unicode_literals, division
-from ast import literal_eval
 from bz2 import BZ2File
 import time
 import math
 import codecs
 
 import plac
+import ujson
 
-from preshed.maps import PreshMap
-from preshed.counter import PreshCounter
-from spacy.strings import hash_string
-from spacy.en import English
 from spacy.matcher import PhraseMatcher
+import spacy
 
 
 def read_gazetteer(tokenizer, loc, n=-1):
     for i, line in enumerate(open(loc)):
-        phrase = literal_eval('u' + line.strip())
-        if ' (' in phrase and phrase.endswith(')'):
-            phrase = phrase.split(' (', 1)[0]
-        if i >= n:
-            break
-        phrase = tokenizer(phrase)
-        if all((t.is_lower and t.prob >= -10) for t in phrase):
-            continue
+        data = ujson.loads(line.strip())
+        phrase = tokenizer(data['text'])
+        for w in phrase:
+            _ = tokenizer.vocab[w.text]
         if len(phrase) >= 2:
             yield phrase
 
 
-def read_text(bz2_loc):
+def read_text(bz2_loc, n=10000):
     with BZ2File(bz2_loc) as file_:
-        for line in file_:
-            yield line.decode('utf8')
+        for i, line in enumerate(file_):
+            data = ujson.loads(line)
+            yield data['body']
+            if i >= n:
+                break
 
 
 def get_matches(tokenizer, phrases, texts, max_length=6):
-    matcher = PhraseMatcher(tokenizer.vocab, phrases, max_length=max_length)
-    print("Match")
+    matcher = PhraseMatcher(tokenizer.vocab, max_length=max_length)
+    matcher.add('Phrase', None, *phrases)
     for text in texts:
         doc = tokenizer(text)
+        for w in doc:
+            _ = doc.vocab[w.text]
         matches = matcher(doc)
-        for mwe in doc.ents:
-            yield mwe
+        for ent_id, start, end in matches:
+            yield (ent_id, doc[start:end].text)
 
 
-def main(patterns_loc, text_loc, counts_loc, n=10000000):
-    nlp = English(parser=False, tagger=False, entity=False)
-    print("Make matcher")
-    phrases = read_gazetteer(nlp.tokenizer, patterns_loc, n=n)
-    counts = PreshCounter()
+def main(patterns_loc, text_loc, n=10000):
+    nlp = spacy.blank('en')
+    nlp.vocab.lex_attr_getters = {}
+    phrases = read_gazetteer(nlp.tokenizer, patterns_loc)
+    count = 0
     t1 = time.time()
-    for mwe in get_matches(nlp.tokenizer, phrases, read_text(text_loc)):
-        counts.inc(hash_string(mwe.text), 1)
+    for ent_id, text in get_matches(nlp.tokenizer, phrases, read_text(text_loc, n=n)):
+        count += 1
     t2 = time.time()
-    print("10m tokens in %d s" % (t2 - t1))
-    
-    with codecs.open(counts_loc, 'w', 'utf8') as file_:
-        for phrase in read_gazetteer(nlp.tokenizer, patterns_loc, n=n):
-            text = phrase.string
-            key = hash_string(text)
-            count = counts[key]
-            if count != 0:
-                file_.write('%d\t%s\n' % (count, text))
-    
+    print("%d docs in %.3f s. %d matches" % (n, (t2 - t1), count))
+
 
 if __name__ == '__main__':
     if False:

From f92ab03dc87711ca03cd4a29a886bc1c827b0934 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 22:51:58 +0200
Subject: [PATCH 070/213] Rename phrase matcher example

---
 examples/{multi_word_matches.py => phrase_matcher.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename examples/{multi_word_matches.py => phrase_matcher.py} (100%)

diff --git a/examples/multi_word_matches.py b/examples/phrase_matcher.py
similarity index 100%
rename from examples/multi_word_matches.py
rename to examples/phrase_matcher.py

From 842e21de9f54c3e37e43f698c75a246d69d4551c Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 23:55:30 +0200
Subject: [PATCH 071/213] Fix int type error for Python 2

---
 spacy/matcher.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/matcher.pyx b/spacy/matcher.pyx
index ef4044d21..5106161a0 100644
--- a/spacy/matcher.pyx
+++ b/spacy/matcher.pyx
@@ -466,7 +466,7 @@ cdef class PhraseMatcher:
                 self._phrase_key[i] = lexeme.orth
             phrase_hash = hash64(self._phrase_key,
                                  self.max_length * sizeof(attr_t), 0)
-            self.phrase_ids[phrase_hash] = ent_id
+            self.phrase_ids.set(phrase_hash, <void*>ent_id)
 
     def __call__(self, Doc doc):
         matches = []

From f5144f04be1e5b6d7bb937611f1303ec39054f99 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 19:14:41 -0500
Subject: [PATCH 072/213] Add argument for CNN maxout pieces

---
 spacy/_ml.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index eaf72b44f..004d9ca73 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -226,9 +226,9 @@ def drop_layer(layer, factor=2.):
     return model
 
 
-def Tok2Vec(width, embed_size, pretrained_dims=0):
-    if pretrained_dims is None:
-        pretrained_dims = 0
+def Tok2Vec(width, embed_size, pretrained_dims=0, **kwargs):
+    assert pretrained_dims is not None
+    cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 3)
     cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
     with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
         norm = HashEmbed(width, embed_size, column=cols.index(NORM), name='embed_norm')
@@ -244,7 +244,10 @@ def Tok2Vec(width, embed_size, pretrained_dims=0):
                     >> LN(Maxout(width, width*4, pieces=3)), column=5)
             )
         )
-        convolution = Residual(ExtractWindow(nW=1) >> LN(Maxout(width, width*3, pieces=3)))
+        convolution = Residual(
+            ExtractWindow(nW=1)
+            >> LN(Maxout(width, width*3, pieces=cnn_maxout_pieces))
+        )
 
         if pretrained_dims >= 1:
             embed = concatenate_lists(trained_vectors, SpacyVectors)

From b832f89ff8cd7c959c6645ad71aed1f9c65617b2 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 19:15:20 -0500
Subject: [PATCH 073/213] Add resume_training function

---
 spacy/language.py | 38 ++++++++++++++++++++++++++++----------
 1 file changed, 28 insertions(+), 10 deletions(-)

diff --git a/spacy/language.py b/spacy/language.py
index 25648ed42..9d1538a18 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -342,7 +342,28 @@ class Language(object):
         for doc, gold in docs_golds:
             yield doc, gold
 
-    def begin_training(self, get_gold_tuples, **cfg):
+    def resume_training(self, **cfg):
+        if cfg.get('device', -1) >= 0:
+            device = util.use_gpu(cfg['device'])
+            if self.vocab.vectors.data.shape[1] >= 1:
+                self.vocab.vectors.data = Model.ops.asarray(
+                    self.vocab.vectors.data)
+        else:
+            device = None
+        learn_rate = util.env_opt('learn_rate', 0.001)
+        beta1 = util.env_opt('optimizer_B1', 0.9)
+        beta2 = util.env_opt('optimizer_B2', 0.999)
+        eps = util.env_opt('optimizer_eps', 1e-08)
+        L2 = util.env_opt('L2_penalty', 1e-6)
+        max_grad_norm = util.env_opt('grad_norm_clip', 1.)
+        self._optimizer = Adam(Model.ops, learn_rate, L2=L2, beta1=beta1,
+                              beta2=beta2, eps=eps)
+        self._optimizer.max_grad_norm = max_grad_norm
+        self._optimizer.device = device
+        return self._optimizer
+
+
+    def begin_training(self, get_gold_tuples=None, **cfg):
         """Allocate models, pre-process training data and acquire a trainer and
         optimizer. Used as a contextmanager.
 
@@ -353,17 +374,14 @@ class Language(object):
         if self.parser:
             self.pipeline.append(NeuralLabeller(self.vocab))
         # Populate vocab
-        for _, annots_brackets in get_gold_tuples():
-            for annots, _ in annots_brackets:
-                for word in annots[1]:
-                    _ = self.vocab[word]
+        if get_gold_tuples is not None:
+            for _, annots_brackets in get_gold_tuples():
+                for annots, _ in annots_brackets:
+                    for word in annots[1]:
+                        _ = self.vocab[word]
         contexts = []
         if cfg.get('device', -1) >= 0:
-            import cupy.cuda.device
-            device = cupy.cuda.device.Device(cfg['device'])
-            device.use()
-            Model.ops = CupyOps()
-            Model.Ops = CupyOps
+            device = util.use_gpu(cfg['device'])
             if self.vocab.vectors.data.shape[1] >= 1:
                 self.vocab.vectors.data = Model.ops.asarray(
                     self.vocab.vectors.data)

From 24e85c20484336cfe386bd97e8c3e2e097a315a3 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 19:15:49 -0500
Subject: [PATCH 074/213] Pass values for CNN maxout pieces option

---
 spacy/pipeline.pyx         | 8 +++++---
 spacy/syntax/nn_parser.pyx | 1 +
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index 29670c155..dcc06cdf7 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -215,6 +215,7 @@ class TokenVectorEncoder(BaseThincComponent):
         self.model = model
         self.cfg = dict(cfg)
         self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
+        self.cfg.setdefault('cnn_maxout_pieces', 2)
 
     def __call__(self, doc):
         """Add context-sensitive vectors to a `Doc`, e.g. from a CNN or LSTM
@@ -286,9 +287,7 @@ class TokenVectorEncoder(BaseThincComponent):
         pipeline (list): The pipeline the model is part of.
         """
         if self.model is True:
-            self.model = self.Model(
-                pretrained_dims=self.vocab.vectors_length,
-                **self.cfg)
+            self.model = self.Model(**self.cfg)
 
 
 class NeuralTagger(BaseThincComponent):
@@ -297,6 +296,7 @@ class NeuralTagger(BaseThincComponent):
         self.vocab = vocab
         self.model = model
         self.cfg = dict(cfg)
+        self.cfg.setdefault('cnn_maxout_pieces', 2)
 
     def __call__(self, doc):
         tags = self.predict(([doc], [doc.tensor]))
@@ -442,6 +442,7 @@ class NeuralTagger(BaseThincComponent):
         return self
 
     def to_disk(self, path, **exclude):
+        self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
         serialize = OrderedDict((
             ('vocab', lambda p: self.vocab.to_disk(p)),
             ('tag_map', lambda p: p.open('wb').write(msgpack.dumps(
@@ -486,6 +487,7 @@ class NeuralLabeller(NeuralTagger):
         self.vocab = vocab
         self.model = model
         self.cfg = dict(cfg)
+        self.cfg.setdefault('cnn_maxout_pieces', 2)
 
     @property
     def labels(self):
diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 52e677390..ad0e35428 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -309,6 +309,7 @@ cdef class Parser:
             cfg['beam_density'] = util.env_opt('beam_density', 0.0)
         if 'pretrained_dims' not in cfg:
             cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
+        cfg.setdefault('cnn_maxout_pieces', 2)
         self.cfg = cfg
         if 'actions' in self.cfg:
             for action, labels in self.cfg.get('actions', {}).items():

From ffda38356a1d51c1bced19c1c1bfdced3756c891 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 19:16:35 -0500
Subject: [PATCH 075/213] Add util function to enable GPU

---
 spacy/util.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/spacy/util.py b/spacy/util.py
index a55eed2f8..429d9bae5 100644
--- a/spacy/util.py
+++ b/spacy/util.py
@@ -14,6 +14,7 @@ import numpy
 import io
 import dill
 from collections import OrderedDict
+from thinc.neural._classes.model import Model
 
 import msgpack
 import msgpack_numpy
@@ -557,3 +558,14 @@ def minify_html(html):
     RETURNS (unicode): "Minified" HTML.
     """
     return html.strip().replace('    ', '').replace('\n', '')
+
+
+def use_gpu(gpu_id):
+    import cupy.cuda.device
+    from thinc.neural.ops import CupyOps
+    device = cupy.cuda.device.Device(gpu_id)
+    device.use()
+    Model.ops = CupyOps()
+    Model.Ops = CupyOps
+    return device
+

From 1d73dec8b12a84dcf3d3c7949fe257b1cc0d011f Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 20 Sep 2017 19:17:10 -0500
Subject: [PATCH 076/213] Refactor train script

---
 spacy/cli/train.py | 40 +++++++++++++++++++++++++++-------------
 1 file changed, 27 insertions(+), 13 deletions(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 8a3446cfe..f80e285c0 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -8,6 +8,7 @@ import cytoolz
 from pathlib import Path
 import dill
 import tqdm
+from thinc.neural._classes.model import Model
 from thinc.neural.optimizers import linear_decay
 from timeit import default_timer as timer
 
@@ -69,18 +70,20 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
     batch_sizes = util.compounding(util.env_opt('batch_from', 1),
                                    util.env_opt('batch_to', 64),
                                    util.env_opt('batch_compound', 1.001))
+    corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
+    n_train_words = corpus.count_train()
 
     if not resume:
         lang_class = util.get_lang_class(lang)
         nlp = lang_class(pipeline=pipeline)
+        optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
     else:
         print("Load resume")
-        nlp = _resume_model(lang, pipeline)
+        util.use_gpu(use_gpu)
+        nlp = _resume_model(lang, pipeline, corpus)
+        optimizer = nlp.resume_training(device=use_gpu)
         lang_class = nlp.__class__
 
-    corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
-    n_train_words = corpus.count_train()
-    optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
     nlp._optimizer = None
 
     print("Itn.\tLoss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
@@ -101,11 +104,11 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                 util.set_env_log(False)
                 epoch_model_path = output_path / ('model%d' % i)
                 nlp.to_disk(epoch_model_path)
-                nlp_loaded = lang_class(pipeline=pipeline)
-                nlp_loaded = nlp_loaded.from_disk(epoch_model_path)
-                scorer = nlp_loaded.evaluate(
+                #nlp_loaded = lang_class(pipeline=pipeline)
+                #nlp_loaded = nlp_loaded.from_disk(epoch_model_path)
+                scorer = nlp.evaluate(
                             corpus.dev_docs(
-                                nlp_loaded,
+                                nlp,
                                 gold_preproc=gold_preproc))
                 acc_loc =(output_path / ('model%d' % i) / 'accuracy.json')
                 with acc_loc.open('w') as file_:
@@ -114,19 +117,30 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
             print_progress(i, losses, scorer.scores)
     finally:
         print("Saving model...")
-        with (output_path / 'model-final.pickle').open('wb') as file_:
-            with nlp.use_params(optimizer.averages):
-                dill.dump(nlp, file_, -1)
+        try:
+            with (output_path / 'model-final.pickle').open('wb') as file_:
+                with nlp.use_params(optimizer.averages):
+                    dill.dump(nlp, file_, -1)
+        except:
+            pass
 
 
-def _resume_model(lang, pipeline):
+def _resume_model(lang, pipeline, corpus):
     nlp = util.load_model(lang)
     pipes = {getattr(pipe, 'name', None) for pipe in nlp.pipeline}
     for name in pipeline:
         if name not in pipes:
             factory = nlp.Defaults.factories[name]
-            nlp.pipeline.extend(factory(nlp))
+            for pipe in factory(nlp):
+                if hasattr(pipe, 'begin_training'):
+                    pipe.begin_training(corpus.train_tuples,
+                                        pipeline=nlp.pipeline)
+                nlp.pipeline.append(pipe)
     nlp.meta['pipeline'] = pipeline
+    if nlp.vocab.vectors.data.shape[1] >= 1:
+        nlp.vocab.vectors.data = Model.ops.asarray(
+                                    nlp.vocab.vectors.data)
+
     return nlp
 
 

From 20193371f5deb85137b892158465344a6af7fbcb Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 21 Sep 2017 14:59:48 +0200
Subject: [PATCH 077/213] Don't share CNN, to reduce complexities

---
 spacy/_ml.py                             |  20 ++---
 spacy/about.py                           |   3 +-
 spacy/cli/train.py                       |   2 +-
 spacy/language.py                        |  21 +----
 spacy/pipeline.pyx                       |  42 +++------
 spacy/syntax/_beam_utils.pyx             |   4 +-
 spacy/syntax/nn_parser.pyx               | 106 ++++++++---------------
 spacy/tests/parser/test_neural_parser.py |  21 ++---
 8 files changed, 69 insertions(+), 150 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 004d9ca73..37bf6335b 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -226,8 +226,8 @@ def drop_layer(layer, factor=2.):
     return model
 
 
-def Tok2Vec(width, embed_size, pretrained_dims=0, **kwargs):
-    assert pretrained_dims is not None
+def Tok2Vec(width, embed_size, **kwargs):
+    pretrained_dims = kwargs.get('pretrained_dims', 0)
     cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 3)
     cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
     with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
@@ -474,20 +474,18 @@ def getitem(i):
         return X[i], None
     return layerize(getitem_fwd)
 
+
 def build_tagger_model(nr_class, token_vector_width, pretrained_dims=0, **cfg):
     embed_size = util.env_opt('embed_size', 4000)
     with Model.define_operators({'>>': chain, '+': add}):
-        # Input: (doc, tensor) tuples
-        private_tok2vec = Tok2Vec(token_vector_width, embed_size,
-                                  pretrained_dims=pretrained_dims)
-        model = (
-            fine_tune(private_tok2vec)
-            >> with_flatten(
-                Maxout(token_vector_width, token_vector_width)
-                >> Softmax(nr_class, token_vector_width)
-            )
+        tok2vec = Tok2Vec(token_vector_width, embed_size,
+                          pretrained_dims=pretrained_dims)
+        model = with_flatten(
+            tok2vec
+            >> Softmax(nr_class, token_vector_width)
         )
     model.nI = None
+    model.tok2vec = tok2vec
     return model
 
 
diff --git a/spacy/about.py b/spacy/about.py
index 40444ffd1..0ae019946 100644
--- a/spacy/about.py
+++ b/spacy/about.py
@@ -3,12 +3,13 @@
 # https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
 
 __title__ = 'spacy-nightly'
-__version__ = '2.0.0a14'
+__version__ = '2.0.0a15'
 __summary__ = 'Industrial-strength Natural Language Processing (NLP) with Python and Cython'
 __uri__ = 'https://spacy.io'
 __author__ = 'Explosion AI'
 __email__ = 'contact@explosion.ai'
 __license__ = 'MIT'
+__release__ = False
 
 __docs_models__ = 'https://spacy.io/docs/usage/models'
 __download_url__ = 'https://github.com/explosion/spacy-models/releases/download'
diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index f80e285c0..c87aabb01 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -55,7 +55,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
         prints(dev_path, title="Development data not found", exits=1)
 
 
-    pipeline = ['token_vectors', 'tags', 'dependencies', 'entities']
+    pipeline = ['tags', 'dependencies', 'entities']
     if no_tagger and 'tags' in pipeline: pipeline.remove('tags')
     if no_parser and 'dependencies' in pipeline: pipeline.remove('dependencies')
     if no_entities and 'entities' in pipeline: pipeline.remove('entities')
diff --git a/spacy/language.py b/spacy/language.py
index 9d1538a18..a6ab0453f 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -303,31 +303,17 @@ class Language(object):
             if self._optimizer is None:
                 self._optimizer = Adam(Model.ops, 0.001)
             sgd = self._optimizer
-        tok2vec = self.pipeline[0]
         grads = {}
         def get_grads(W, dW, key=None):
             grads[key] = (W, dW)
-        pipes = list(self.pipeline[1:])
+        pipes = list(self.pipeline)
         random.shuffle(pipes)
-        tokvecses, bp_tokvecses = tok2vec.model.begin_update(docs, drop=drop)
-        all_d_tokvecses = [tok2vec.model.ops.allocate(tv.shape) for tv in tokvecses]
         for proc in pipes:
             if not hasattr(proc, 'update'):
                 continue
-            d_tokvecses = proc.update((docs, tokvecses), golds,
-                                      drop=drop, sgd=get_grads, losses=losses)
-            if update_shared and d_tokvecses is not None:
-                for i, d_tv in enumerate(d_tokvecses):
-                    all_d_tokvecses[i] += d_tv
-        if update_shared and bp_tokvecses is not None:
-            bp_tokvecses(all_d_tokvecses, sgd=sgd)
+            proc.update(docs, golds, drop=drop, sgd=get_grads, losses=losses)
         for key, (W, dW) in grads.items():
             sgd(W, dW, key=key)
-        # Clear the tensor variable, to free GPU memory.
-        # If we don't do this, the memory leak gets pretty
-        # bad, because we may be holding part of a batch.
-        for doc in docs:
-            doc.tensor = None
 
     def preprocess_gold(self, docs_golds):
         """Can be called before training to pre-process gold data. By default,
@@ -371,8 +357,6 @@ class Language(object):
         **cfg: Config parameters.
         returns: An optimizer
         """
-        if self.parser:
-            self.pipeline.append(NeuralLabeller(self.vocab))
         # Populate vocab
         if get_gold_tuples is not None:
             for _, annots_brackets in get_gold_tuples():
@@ -418,7 +402,6 @@ class Language(object):
         assert len(docs) == len(golds)
         for doc, gold in zip(docs, golds):
             scorer.score(doc, gold)
-            doc.tensor = None
         return scorer
 
     @contextmanager
diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index dcc06cdf7..8ad62d696 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -299,27 +299,25 @@ class NeuralTagger(BaseThincComponent):
         self.cfg.setdefault('cnn_maxout_pieces', 2)
 
     def __call__(self, doc):
-        tags = self.predict(([doc], [doc.tensor]))
+        tags = self.predict([doc])
         self.set_annotations([doc], tags)
         return doc
 
     def pipe(self, stream, batch_size=128, n_threads=-1):
         for docs in cytoolz.partition_all(batch_size, stream):
             docs = list(docs)
-            tokvecs = [d.tensor for d in docs]
-            tag_ids = self.predict((docs, tokvecs))
+            tag_ids = self.predict(docs)
             self.set_annotations(docs, tag_ids)
             yield from docs
 
-    def predict(self, docs_tokvecs):
-        scores = self.model(docs_tokvecs)
+    def predict(self, docs):
+        scores = self.model(docs)
         scores = self.model.ops.flatten(scores)
         guesses = scores.argmax(axis=1)
         if not isinstance(guesses, numpy.ndarray):
             guesses = guesses.get()
-        tokvecs = docs_tokvecs[1]
         guesses = self.model.ops.unflatten(guesses,
-                    [tv.shape[0] for tv in tokvecs])
+                    [len(d) for d in docs])
         return guesses
 
     def set_annotations(self, docs, batch_tag_ids):
@@ -339,20 +337,15 @@ class NeuralTagger(BaseThincComponent):
                 idx += 1
         doc.is_tagged = True
 
-    def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
+    def update(self, docs, golds, drop=0., sgd=None, losses=None):
         if losses is not None and self.name not in losses:
             losses[self.name] = 0.
-        docs, tokvecs = docs_tokvecs
 
-        if self.model.nI is None:
-            self.model.nI = tokvecs[0].shape[1]
-        tag_scores, bp_tag_scores = self.model.begin_update(docs_tokvecs, drop=drop)
+        tag_scores, bp_tag_scores = self.model.begin_update(docs, drop=drop)
         loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
 
-        d_tokvecs = bp_tag_scores(d_tag_scores, sgd=sgd)
         if losses is not None:
             losses[self.name] += loss
-        return d_tokvecs
 
     def get_loss(self, docs, golds, scores):
         scores = self.model.ops.flatten(scores)
@@ -399,9 +392,9 @@ class NeuralTagger(BaseThincComponent):
                                     pretrained_dims=self.vocab.vectors_length)
 
     @classmethod
-    def Model(cls, n_tags, token_vector_width, pretrained_dims=0):
+    def Model(cls, n_tags, token_vector_width, pretrained_dims=0, **cfg):
         return build_tagger_model(n_tags, token_vector_width,
-                                  pretrained_dims)
+                                  pretrained_dims, **cfg)
 
     def use_params(self, params):
         with self.model.use_params(params):
@@ -573,15 +566,10 @@ class SimilarityHook(BaseThincComponent):
             yield self(doc)
 
     def predict(self, doc1, doc2):
-        return self.model.predict([(doc1.tensor, doc2.tensor)])
+        return self.model.predict([(doc1, doc2)])
 
-    def update(self, doc1_tensor1_doc2_tensor2, golds, sgd=None, drop=0.):
-        doc1s, tensor1s, doc2s, tensor2s = doc1_tensor1_doc2_tensor2
-        sims, bp_sims = self.model.begin_update(zip(tensor1s, tensor2s),
-                                                drop=drop)
-        d_tensor1s, d_tensor2s = bp_sims(golds, sgd=sgd)
-
-        return d_tensor1s, d_tensor2s
+    def update(self, doc1_doc2, golds, sgd=None, drop=0.):
+        sims, bp_sims = self.model.begin_update(doc1_doc2, drop=drop)
 
     def begin_training(self, _=tuple(), pipeline=None):
         """
@@ -636,15 +624,13 @@ class TextCategorizer(BaseThincComponent):
             for j, label in enumerate(self.labels):
                 doc.cats[label] = float(scores[i, j])
 
-    def update(self, docs_tensors, golds, state=None, drop=0., sgd=None, losses=None):
-        docs, tensors = docs_tensors
+    def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
         scores, bp_scores = self.model.begin_update(docs, drop=drop)
         loss, d_scores = self.get_loss(docs, golds, scores)
-        d_tensors = bp_scores(d_scores, sgd=sgd)
+        bp_scores(d_scores, sgd=sgd)
         if losses is not None:
             losses.setdefault(self.name, 0.0)
             losses[self.name] += loss
-        return d_tensors
 
     def get_loss(self, docs, golds, scores):
         truths = numpy.zeros((len(golds), len(self.labels)), dtype='f')
diff --git a/spacy/syntax/_beam_utils.pyx b/spacy/syntax/_beam_utils.pyx
index 4d90fe23b..a26900f6b 100644
--- a/spacy/syntax/_beam_utils.pyx
+++ b/spacy/syntax/_beam_utils.pyx
@@ -147,10 +147,10 @@ def get_token_ids(states, int n_tokens):
 
 nr_update = 0
 def update_beam(TransitionSystem moves, int nr_feature, int max_steps,
-                states, tokvecs, golds,
+                states, golds,
                 state2vec, vec2scores, 
                 int width, float density,
-                sgd=None, losses=None, drop=0.):
+                losses=None, drop=0.):
     global nr_update
     cdef MaxViolation violn
     nr_update += 1
diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index ad0e35428..77f99624a 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -48,7 +48,7 @@ from .. import util
 from ..util import get_async, get_cuda_stream
 from .._ml import zero_init, PrecomputableAffine, PrecomputableMaxouts
 from .._ml import Tok2Vec, doc2feats, rebatch, fine_tune
-from .._ml import Residual, drop_layer
+from .._ml import Residual, drop_layer, flatten
 from ..compat import json_dumps
 
 from . import _parse_features
@@ -244,8 +244,9 @@ cdef class Parser:
         hidden_width = util.env_opt('hidden_width', hidden_width)
         parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2)
         embed_size = util.env_opt('embed_size', 4000)
-        tensors = fine_tune(Tok2Vec(token_vector_width, embed_size,
-                                    pretrained_dims=cfg.get('pretrained_dims')))
+        tok2vec = Tok2Vec(token_vector_width, embed_size,
+                          pretrained_dims=cfg.get('pretrained_dims', 0))
+        tok2vec = chain(tok2vec, flatten)
         if parser_maxout_pieces == 1:
             lower = PrecomputableAffine(hidden_width if depth >= 1 else nr_class,
                         nF=cls.nr_feature,
@@ -277,7 +278,7 @@ cdef class Parser:
             'hidden_width': hidden_width,
             'maxout_pieces': parser_maxout_pieces
         }
-        return (tensors, lower, upper), cfg
+        return (tok2vec, lower, upper), cfg
 
     def __init__(self, Vocab vocab, moves=True, model=True, **cfg):
         """
@@ -309,7 +310,6 @@ cdef class Parser:
             cfg['beam_density'] = util.env_opt('beam_density', 0.0)
         if 'pretrained_dims' not in cfg:
             cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
-        cfg.setdefault('cnn_maxout_pieces', 2)
         self.cfg = cfg
         if 'actions' in self.cfg:
             for action, labels in self.cfg.get('actions', {}).items():
@@ -335,11 +335,11 @@ cdef class Parser:
             beam_density = self.cfg.get('beam_density', 0.0)
         cdef Beam beam
         if beam_width == 1:
-            states = self.parse_batch([doc], [doc.tensor])
+            states = self.parse_batch([doc])
             self.set_annotations([doc], states)
             return doc
         else:
-            beam = self.beam_parse([doc], [doc.tensor],
+            beam = self.beam_parse([doc], 
                         beam_width=beam_width, beam_density=beam_density)[0]
             output = self.moves.get_beam_annot(beam)
             state = <StateClass>beam.at(0)
@@ -368,11 +368,10 @@ cdef class Parser:
         cdef Beam beam
         for docs in cytoolz.partition_all(batch_size, docs):
             docs = list(docs)
-            tokvecs = [doc.tensor for doc in docs]
             if beam_width == 1:
-                parse_states = self.parse_batch(docs, tokvecs)
+                parse_states = self.parse_batch(docs)
             else:
-                beams = self.beam_parse(docs, tokvecs,
+                beams = self.beam_parse(docs,
                             beam_width=beam_width, beam_density=beam_density)
                 parse_states = []
                 for beam in beams:
@@ -380,7 +379,7 @@ cdef class Parser:
             self.set_annotations(docs, parse_states)
             yield from docs
 
-    def parse_batch(self, docs, tokvecses):
+    def parse_batch(self, docs):
         cdef:
             precompute_hiddens state2vec
             StateClass state
@@ -391,21 +390,15 @@ cdef class Parser:
             int nr_class, nr_feat, nr_piece, nr_dim, nr_state
         if isinstance(docs, Doc):
             docs = [docs]
-        if isinstance(tokvecses, np.ndarray):
-            tokvecses = [tokvecses]
 
-        if USE_FINE_TUNE:
-            tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
-        else:
-            tokvecs = self.model[0].ops.flatten(tokvecses)
+        cuda_stream = get_cuda_stream()
+        (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
+                                                                            0.0)
+
         nr_state = len(docs)
         nr_class = self.moves.n_moves
         nr_dim = tokvecs.shape[1]
         nr_feat = self.nr_feature
-
-        cuda_stream = get_cuda_stream()
-        state2vec, vec2scores = self.get_batch_model(nr_state, tokvecs,
-                                                     cuda_stream, 0.0)
         nr_piece = state2vec.nP
 
         states = self.moves.init_batch(docs)
@@ -448,19 +441,15 @@ cdef class Parser:
                     next_step.push_back(st)
         return states
 
-    def beam_parse(self, docs, tokvecses, int beam_width=3, float beam_density=0.001):
+    def beam_parse(self, docs, int beam_width=3, float beam_density=0.001):
         cdef Beam beam
         cdef np.ndarray scores
         cdef Doc doc
         cdef int nr_class = self.moves.n_moves
         cdef StateClass stcls, output
-        if USE_FINE_TUNE:
-            tokvecs = self.model[0].ops.flatten(self.model[0]((docs, tokvecses)))
-        else:
-            tokvecs = self.model[0].ops.flatten(tokvecses)
         cuda_stream = get_cuda_stream()
-        state2vec, vec2scores = self.get_batch_model(len(docs), tokvecs,
-                                                     cuda_stream, 0.0)
+        (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
+                                                                            0.0)
         beams = []
         cdef int offset = 0
         cdef int j = 0
@@ -520,30 +509,24 @@ cdef class Parser:
         free(scores)
         free(token_ids)
 
-    def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
+    def update(self, docs, golds, drop=0., sgd=None, losses=None):
         if not any(self.moves.has_gold(gold) for gold in golds):
             return None
         if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() >= 0.5:
-            return self.update_beam(docs_tokvecs, golds,
+            return self.update_beam(docs, golds,
                     self.cfg['beam_width'], self.cfg['beam_density'],
                     drop=drop, sgd=sgd, losses=losses)
         if losses is not None and self.name not in losses:
             losses[self.name] = 0.
-        docs, tokvec_lists = docs_tokvecs
         if isinstance(docs, Doc) and isinstance(golds, GoldParse):
             docs = [docs]
             golds = [golds]
-        if USE_FINE_TUNE:
-            my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
-            tokvecs = self.model[0].ops.flatten(my_tokvecs)
-        else:
-            tokvecs = self.model[0].ops.flatten(docs_tokvecs[1])
 
         cuda_stream = get_cuda_stream()
 
         states, golds, max_steps = self._init_gold_batch(docs, golds)
-        state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream,
-                                                      0.0)
+        (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
+                                                                            0.0)
         todo = [(s, g) for (s, g) in zip(states, golds)
                 if not s.is_final() and g is not None]
         if not todo:
@@ -587,13 +570,9 @@ cdef class Parser:
             if n_steps >= max_steps:
                 break
         self._make_updates(d_tokvecs,
-            backprops, sgd, cuda_stream)
-        d_tokvecs = self.model[0].ops.unflatten(d_tokvecs, [len(d) for d in docs])
-        if USE_FINE_TUNE:
-            d_tokvecs = bp_my_tokvecs(d_tokvecs, sgd=sgd)
-        return d_tokvecs
+            bp_tokvecs, backprops, sgd, cuda_stream)
 
-    def update_beam(self, docs_tokvecs, golds, width=None, density=None,
+    def update_beam(self, docs, golds, width=None, density=None,
             drop=0., sgd=None, losses=None):
         if not any(self.moves.has_gold(gold) for gold in golds):
             return None
@@ -605,26 +584,20 @@ cdef class Parser:
             density = self.cfg.get('beam_density', 0.0)
         if losses is not None and self.name not in losses:
             losses[self.name] = 0.
-        docs, tokvecs = docs_tokvecs
         lengths = [len(d) for d in docs]
         assert min(lengths) >= 1
-        if USE_FINE_TUNE:
-            my_tokvecs, bp_my_tokvecs = self.model[0].begin_update(docs_tokvecs, drop=drop)
-            tokvecs = self.model[0].ops.flatten(my_tokvecs)
-        else:
-            tokvecs = self.model[0].ops.flatten(tokvecs)
         states = self.moves.init_batch(docs)
         for gold in golds:
             self.moves.preprocess_gold(gold)
 
         cuda_stream = get_cuda_stream()
-        state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream, 0.0)
+        (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream, 0.0)
 
         states_d_scores, backprops = _beam_utils.update_beam(self.moves, self.nr_feature, 500,
-                                        states, tokvecs, golds,
+                                        states, golds,
                                         state2vec, vec2scores,
                                         width, density,
-                                        sgd=sgd, drop=drop, losses=losses)
+                                        drop=drop, losses=losses)
         backprop_lower = []
         cdef float batch_size = len(docs)
         for i, d_scores in enumerate(states_d_scores):
@@ -642,20 +615,7 @@ cdef class Parser:
             else:
                 backprop_lower.append((ids, d_vector, bp_vectors))
         d_tokvecs = self.model[0].ops.allocate(tokvecs.shape)
-        self._make_updates(d_tokvecs, backprop_lower, sgd, cuda_stream)
-        d_tokvecs = self.model[0].ops.unflatten(d_tokvecs, lengths)
-        if USE_FINE_TUNE:
-            d_tokvecs = bp_my_tokvecs(d_tokvecs, sgd=sgd)
-        return d_tokvecs
-
-    def _pad_tokvecs(self, tokvecs):
-        # Add a vector for missing values at the start of tokvecs
-        xp = get_array_module(tokvecs)
-        pad = xp.zeros((1, tokvecs.shape[1]), dtype=tokvecs.dtype)
-        return xp.vstack((pad, tokvecs))
-
-    def _unpad_tokvecs(self, d_tokvecs):
-        return d_tokvecs[1:]
+        self._make_updates(d_tokvecs, bp_tokvecs, backprop_lower, sgd, cuda_stream)
 
     def _init_gold_batch(self, whole_docs, whole_golds):
         """Make a square batch, of length equal to the shortest doc. A long
@@ -693,7 +653,7 @@ cdef class Parser:
             max_moves = max(max_moves, len(oracle_actions))
         return states, golds, max_moves
 
-    def _make_updates(self, d_tokvecs, backprops, sgd, cuda_stream=None):
+    def _make_updates(self, d_tokvecs, bp_tokvecs, backprops, sgd, cuda_stream=None):
         # Tells CUDA to block, so our async copies complete.
         if cuda_stream is not None:
             cuda_stream.synchronize()
@@ -704,6 +664,7 @@ cdef class Parser:
             d_state_features *= mask.reshape(ids.shape + (1,))
             self.model[0].ops.scatter_add(d_tokvecs, ids * mask,
                 d_state_features)
+        bp_tokvecs(d_tokvecs, sgd=sgd)
 
     @property
     def move_names(self):
@@ -713,11 +674,12 @@ cdef class Parser:
             names.append(name)
         return names
 
-    def get_batch_model(self, batch_size, tokvecs, stream, dropout):
-        _, lower, upper = self.model
-        state2vec = precompute_hiddens(batch_size, tokvecs,
+    def get_batch_model(self, docs, stream, dropout):
+        tok2vec, lower, upper = self.model
+        tokvecs, bp_tokvecs = tok2vec.begin_update(docs, drop=dropout)
+        state2vec = precompute_hiddens(len(docs), tokvecs,
                         lower, stream, drop=dropout)
-        return state2vec, upper
+        return (tokvecs, bp_tokvecs), state2vec, upper
 
     nr_feature = 8
 
diff --git a/spacy/tests/parser/test_neural_parser.py b/spacy/tests/parser/test_neural_parser.py
index 29350b30a..8747b01ba 100644
--- a/spacy/tests/parser/test_neural_parser.py
+++ b/spacy/tests/parser/test_neural_parser.py
@@ -61,33 +61,22 @@ def test_predict_doc(parser, tok2vec, model, doc):
     parser(doc)
 
 
-def test_update_doc(parser, tok2vec, model, doc, gold):
+def test_update_doc(parser, model, doc, gold):
     parser.model = model
-    tokvecs, bp_tokvecs = tok2vec.begin_update([doc])
-    d_tokvecs = parser.update(([doc], tokvecs), [gold])
-    assert d_tokvecs[0].shape == tokvecs[0].shape
     def optimize(weights, gradient, key=None):
         weights -= 0.001 * gradient
-    bp_tokvecs(d_tokvecs, sgd=optimize)
-    assert d_tokvecs[0].sum() == 0.
+    parser.update([doc], [gold], sgd=optimize)
 
 
-def test_predict_doc_beam(parser, tok2vec, model, doc):
-    doc.tensor = tok2vec([doc])[0]
+def test_predict_doc_beam(parser, model, doc):
     parser.model = model
     parser(doc, beam_width=32, beam_density=0.001)
-    for word in doc:
-        print(word.text, word.head, word.dep_)
 
 
-def test_update_doc_beam(parser, tok2vec, model, doc, gold):
+def test_update_doc_beam(parser, model, doc, gold):
     parser.model = model
-    tokvecs, bp_tokvecs = tok2vec.begin_update([doc])
-    d_tokvecs = parser.update_beam(([doc], tokvecs), [gold])
-    assert d_tokvecs[0].shape == tokvecs[0].shape
     def optimize(weights, gradient, key=None):
         weights -= 0.001 * gradient
-    bp_tokvecs(d_tokvecs, sgd=optimize)
-    assert d_tokvecs[0].sum() == 0.
+    parser.update_beam([doc], [gold], sgd=optimize)
 
 

From 0a9016cadeb93b33deb711764177127c5f187a09 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 21 Sep 2017 13:06:45 -0500
Subject: [PATCH 078/213] Fix serialization during training

---
 spacy/cli/train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index f80e285c0..801706614 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -104,8 +104,8 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                 util.set_env_log(False)
                 epoch_model_path = output_path / ('model%d' % i)
                 nlp.to_disk(epoch_model_path)
-                #nlp_loaded = lang_class(pipeline=pipeline)
-                #nlp_loaded = nlp_loaded.from_disk(epoch_model_path)
+                nlp_loaded = lang_class(pipeline=pipeline)
+                nlp_loaded = nlp_loaded.from_disk(epoch_model_path)
                 scorer = nlp.evaluate(
                             corpus.dev_docs(
                                 nlp,

From 40a4873b70ec6f70f64f961fcb2573d9e2c12817 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 21 Sep 2017 13:07:26 -0500
Subject: [PATCH 079/213] Fix serialization of model options

---
 spacy/_ml.py       |  7 ++++++-
 spacy/pipeline.pyx | 23 ++++++++---------------
 2 files changed, 14 insertions(+), 16 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 004d9ca73..c139623c1 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -474,8 +474,13 @@ def getitem(i):
         return X[i], None
     return layerize(getitem_fwd)
 
-def build_tagger_model(nr_class, token_vector_width, pretrained_dims=0, **cfg):
+def build_tagger_model(nr_class, **cfg):
     embed_size = util.env_opt('embed_size', 4000)
+    if 'token_vector_width' in cfg:
+        token_vector_width = cfg['token_vector_width']
+    else:
+        token_vector_width = util.env_opt('token_vector_width', 128)
+    pretrained_dims = cfg.get('pretrained_dims', 0)
     with Model.define_operators({'>>': chain, '+': add}):
         # Input: (doc, tensor) tuples
         private_tok2vec = Tok2Vec(token_vector_width, embed_size,
diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index dcc06cdf7..3d302a782 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -297,6 +297,7 @@ class NeuralTagger(BaseThincComponent):
         self.model = model
         self.cfg = dict(cfg)
         self.cfg.setdefault('cnn_maxout_pieces', 2)
+        self.cfg.setdefault('pretrained_dims', self.vocab.vectors.data.shape[1])
 
     def __call__(self, doc):
         tags = self.predict(([doc], [doc.tensor]))
@@ -393,15 +394,12 @@ class NeuralTagger(BaseThincComponent):
             vocab.morphology = Morphology(vocab.strings, new_tag_map,
                                           vocab.morphology.lemmatizer,
                                           exc=vocab.morphology.exc)
-        token_vector_width = pipeline[0].model.nO
         if self.model is True:
-            self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
-                                    pretrained_dims=self.vocab.vectors_length)
+            self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
 
     @classmethod
-    def Model(cls, n_tags, token_vector_width, pretrained_dims=0):
-        return build_tagger_model(n_tags, token_vector_width,
-                                  pretrained_dims)
+    def Model(cls, n_tags, **cfg):
+        return build_tagger_model(n_tags, **cfg)
 
     def use_params(self, params):
         with self.model.use_params(params):
@@ -422,8 +420,7 @@ class NeuralTagger(BaseThincComponent):
             if self.model is True:
                 token_vector_width = util.env_opt('token_vector_width',
                         self.cfg.get('token_vector_width', 128))
-                self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
-                                        pretrained_dims=self.vocab.vectors_length)
+                self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
             self.model.from_bytes(b)
 
         def load_tag_map(b):
@@ -457,10 +454,7 @@ class NeuralTagger(BaseThincComponent):
     def from_disk(self, path, **exclude):
         def load_model(p):
             if self.model is True:
-                token_vector_width = util.env_opt('token_vector_width',
-                        self.cfg.get('token_vector_width', 128))
-                self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
-                                        **self.cfg)
+                self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
             self.model.from_bytes(p.open('rb').read())
 
         def load_tag_map(p):
@@ -514,9 +508,8 @@ class NeuralLabeller(NeuralTagger):
                                     pretrained_dims=self.vocab.vectors_length)
 
     @classmethod
-    def Model(cls, n_tags, token_vector_width, pretrained_dims=0):
-        return build_tagger_model(n_tags, token_vector_width,
-                                  pretrained_dims)
+    def Model(cls, n_tags, **cfg):
+        return build_tagger_model(n_tags, **cfg)
 
     def get_loss(self, docs, golds, scores):
         scores = self.model.ops.flatten(scores)

From a18659630788e40eb7aa3c729160b6fb4da07a09 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 22 Sep 2017 09:37:03 -0500
Subject: [PATCH 080/213] Add 'reapply' combinator, for iterated CNN

---
 spacy/_ml.py | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index c139623c1..91640c14a 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -271,6 +271,28 @@ def Tok2Vec(width, embed_size, pretrained_dims=0, **kwargs):
     return tok2vec
 
 
+def reapply(layer, n_times):
+    def reapply_fwd(X, drop=0.):
+        backprops = []
+        for i in range(n_times):
+            Y, backprop = layer.begin_update(X, drop=drop)
+            X = Y
+            backprops.append(backprop)
+        def reapply_bwd(dY, sgd=None):
+            dX = None
+            for backprop in reversed(backprops):
+                dY = backprop(dY, sgd=sgd)
+                if dX is None:
+                    dX = dY
+                else:
+                    dX += dY
+            return dX
+        return Y, reapply_bwd
+    return wrap(reapply_fwd, layer)
+
+
+
+
 def asarray(ops, dtype):
     def forward(X, drop=0.):
         return ops.asarray(X, dtype=dtype), None

From d9124f1aa3cdc6ff13ccda708fbafe9012adcd92 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 22 Sep 2017 09:38:22 -0500
Subject: [PATCH 081/213] Add link_vectors_to_models function

---
 spacy/_ml.py               |  2 ++
 spacy/pipeline.pyx         | 11 +++++++++--
 spacy/syntax/nn_parser.pyx |  7 ++++---
 spacy/vocab.pyx            |  4 ++++
 4 files changed, 19 insertions(+), 5 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 91640c14a..65ffb42a6 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -4,6 +4,7 @@ from thinc.neural import Model, Maxout, Softmax, Affine
 from thinc.neural._classes.hash_embed import HashEmbed
 from thinc.neural.ops import NumpyOps, CupyOps
 from thinc.neural.util import get_array_module
+import thinc.extra.load_nlp
 import random
 import cytoolz
 
@@ -31,6 +32,7 @@ from . import util
 import numpy
 import io
 
+VECTORS_KEY = 'spacy_pretrained_vectors'
 
 @layerize
 def _flatten_add_lengths(seqs, pad=0, drop=0.):
diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index 3d302a782..a7ff90174 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -43,6 +43,7 @@ from .compat import json_dumps
 from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS
 from ._ml import rebatch, Tok2Vec, flatten
 from ._ml import build_text_classifier, build_tagger_model
+from ._ml import link_vectors_to_models
 from .parts_of_speech import X
 
 
@@ -121,6 +122,7 @@ class BaseThincComponent(object):
         token_vector_width = pipeline[0].model.nO
         if self.model is True:
             self.model = self.Model(1, token_vector_width)
+            link_vectors_to_models(self.vocab)
 
     def use_params(self, params):
         with self.model.use_params(params):
@@ -215,7 +217,7 @@ class TokenVectorEncoder(BaseThincComponent):
         self.model = model
         self.cfg = dict(cfg)
         self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
-        self.cfg.setdefault('cnn_maxout_pieces', 2)
+        self.cfg.setdefault('cnn_maxout_pieces', 3)
 
     def __call__(self, doc):
         """Add context-sensitive vectors to a `Doc`, e.g. from a CNN or LSTM
@@ -288,6 +290,7 @@ class TokenVectorEncoder(BaseThincComponent):
         """
         if self.model is True:
             self.model = self.Model(**self.cfg)
+            link_vectors_to_models(self.vocab)
 
 
 class NeuralTagger(BaseThincComponent):
@@ -396,6 +399,7 @@ class NeuralTagger(BaseThincComponent):
                                           exc=vocab.morphology.exc)
         if self.model is True:
             self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
+            link_vectors_to_models(self.vocab)
 
     @classmethod
     def Model(cls, n_tags, **cfg):
@@ -504,8 +508,9 @@ class NeuralLabeller(NeuralTagger):
                         self.labels[dep] = len(self.labels)
         token_vector_width = pipeline[0].model.nO
         if self.model is True:
-            self.model = self.Model(len(self.labels), token_vector_width,
+            self.model = self.Model(len(self.labels), token_vector_width=token_vector_width,
                                     pretrained_dims=self.vocab.vectors_length)
+            link_vectors_to_models(self.vocab)
 
     @classmethod
     def Model(cls, n_tags, **cfg):
@@ -585,6 +590,7 @@ class SimilarityHook(BaseThincComponent):
         """
         if self.model is True:
             self.model = self.Model(pipeline[0].model.nO)
+            link_vectors_to_models(self.vocab)
 
 
 class TextCategorizer(BaseThincComponent):
@@ -658,6 +664,7 @@ class TextCategorizer(BaseThincComponent):
             self.cfg['pretrained_dims'] = self.vocab.vectors_length
             self.model = self.Model(len(self.labels), token_vector_width,
                                     **self.cfg)
+            link_vectors_to_models(self.vocab)
 
 
 cdef class EntityRecognizer(LinearParser):
diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index ad0e35428..010b3771e 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -49,6 +49,7 @@ from ..util import get_async, get_cuda_stream
 from .._ml import zero_init, PrecomputableAffine, PrecomputableMaxouts
 from .._ml import Tok2Vec, doc2feats, rebatch, fine_tune
 from .._ml import Residual, drop_layer
+from .._ml import link_vectors_to_models
 from ..compat import json_dumps
 
 from . import _parse_features
@@ -309,7 +310,7 @@ cdef class Parser:
             cfg['beam_density'] = util.env_opt('beam_density', 0.0)
         if 'pretrained_dims' not in cfg:
             cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
-        cfg.setdefault('cnn_maxout_pieces', 2)
+        cfg.setdefault('cnn_maxout_pieces', 3)
         self.cfg = cfg
         if 'actions' in self.cfg:
             for action, labels in self.cfg.get('actions', {}).items():
@@ -791,6 +792,7 @@ cdef class Parser:
         if self.model is True:
             cfg['pretrained_dims'] = self.vocab.vectors_length
             self.model, cfg = self.Model(self.moves.n_moves, **cfg)
+            link_vectors_to_models(self.vocab)
             self.cfg.update(cfg)
 
     def preprocess_gold(self, docs_golds):
@@ -872,8 +874,7 @@ cdef class Parser:
         msg = util.from_bytes(bytes_data, deserializers, exclude)
         if 'model' not in exclude:
             if self.model is True:
-                self.model, cfg = self.Model(self.moves.n_moves,
-                                    pretrained_dims=self.vocab.vectors_length)
+                self.model, cfg = self.Model(**self.cfg)
                 cfg['pretrained_dims'] = self.vocab.vectors_length
             else:
                 cfg = {}
diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx
index b4a244287..01e074617 100644
--- a/spacy/vocab.pyx
+++ b/spacy/vocab.pyx
@@ -27,6 +27,7 @@ from .vectors import Vectors
 from . import util
 from . import attrs
 from . import symbols
+from ._ml import link_vectors_to_models
 
 
 cdef class Vocab:
@@ -323,6 +324,7 @@ cdef class Vocab:
             self.lexemes_from_bytes(file_.read())
         if self.vectors is not None:
             self.vectors.from_disk(path, exclude='strings.json')
+        link_vectors_to_models(self)
         return self
 
     def to_bytes(self, **exclude):
@@ -362,6 +364,7 @@ cdef class Vocab:
             ('vectors', lambda b: serialize_vectors(b))
         ))
         util.from_bytes(bytes_data, setters, exclude)
+        link_vectors_to_models(self)
         return self
 
     def lexemes_to_bytes(self):
@@ -436,6 +439,7 @@ def unpickle_vocab(sstore, morphology, data_dir,
     vocab.lex_attr_getters = lex_attr_getters
     vocab.lexemes_from_bytes(lexemes_data)
     vocab.length = length
+    link_vectors_to_models(vocab)
     return vocab
 
 

From 980fb6e85482d0325897775be86ef4343f880941 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 22 Sep 2017 09:38:36 -0500
Subject: [PATCH 082/213] Refactor Tok2Vec

---
 spacy/_ml.py | 59 +++++++++++++++++++++++++++++-----------------------
 1 file changed, 33 insertions(+), 26 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 65ffb42a6..34f66233d 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -227,45 +227,52 @@ def drop_layer(layer, factor=2.):
     model.predict = layer
     return model
 
+def link_vectors_to_models(vocab):
+    vectors = vocab.vectors
+    ops = Model.ops
+    for word in vocab:
+        if word.orth in vectors.key2row:
+            word.rank = vectors.key2row[word.orth]
+        else:
+            word.rank = 0
+    data = ops.asarray(vectors.data)
+    # Set an entry here, so that vectors are accessed by StaticVectors
+    # (unideal, I know)
+    thinc.extra.load_nlp.VECTORS[(ops.device, VECTORS_KEY)] = data
 
-def Tok2Vec(width, embed_size, pretrained_dims=0, **kwargs):
-    assert pretrained_dims is not None
+
+def Tok2Vec(width, embed_size, **kwargs):
+    pretrained_dims = kwargs.get('pretrained_dims', 0)
     cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 3)
     cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
-    with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
+    with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add,
+                                 '*': reapply}):
         norm = HashEmbed(width, embed_size, column=cols.index(NORM), name='embed_norm')
         prefix = HashEmbed(width, embed_size//2, column=cols.index(PREFIX), name='embed_prefix')
         suffix = HashEmbed(width, embed_size//2, column=cols.index(SUFFIX), name='embed_suffix')
         shape = HashEmbed(width, embed_size//2, column=cols.index(SHAPE), name='embed_shape')
+        if pretrained_dims is not None and pretrained_dims >= 1:
+            glove = StaticVectors(VECTORS_KEY, width, column=cols.index(ID))
+
+            embed = uniqued(
+                (glove | norm | prefix | suffix | shape)
+                >> LN(Maxout(width, width*5, pieces=3)), column=5)
+        else:
+            embed = uniqued(
+                (norm | prefix | suffix | shape)
+                >> LN(Maxout(width, width*4, pieces=3)), column=5)
+
 
-        trained_vectors = (
-            FeatureExtracter(cols)
-            >> with_flatten(
-                uniqued(
-                    (norm | prefix | suffix | shape)
-                    >> LN(Maxout(width, width*4, pieces=3)), column=5)
-            )
-        )
         convolution = Residual(
             ExtractWindow(nW=1)
             >> LN(Maxout(width, width*3, pieces=cnn_maxout_pieces))
         )
 
-        if pretrained_dims >= 1:
-            embed = concatenate_lists(trained_vectors, SpacyVectors)
-            tok2vec = (
-                embed
-                >> with_flatten(
-                    Affine(width, width+pretrained_dims)
-                    >> convolution ** 4,
-                    pad=4)
-            )
-        else:
-            embed = trained_vectors
-            tok2vec = (
-                embed
-                >> with_flatten(convolution ** 4, pad=4)
-            )
+        tok2vec = (
+            FeatureExtracter(cols)
+            >> with_flatten(
+                embed >> (convolution * 4), pad=4)
+        )
 
         # Work around thinc API limitations :(. TODO: Revise in Thinc 7
         tok2vec.nO = width

From 05596159bfb8e50e4ecbf0f5841aa709a9a71f5d Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 22 Sep 2017 15:33:27 -0500
Subject: [PATCH 083/213] Fix serialization when pre-trained vectors

---
 spacy/pipeline.pyx | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index a7ff90174..f5b2db55a 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -145,8 +145,8 @@ class BaseThincComponent(object):
 
         deserialize = OrderedDict((
             ('cfg', lambda b: self.cfg.update(ujson.loads(b))),
-            ('model', load_model),
             ('vocab', lambda b: self.vocab.from_bytes(b))
+            ('model', load_model),
         ))
         util.from_bytes(bytes_data, deserialize, exclude)
         return self
@@ -154,8 +154,8 @@ class BaseThincComponent(object):
     def to_disk(self, path, **exclude):
         serialize = OrderedDict((
             ('cfg', lambda p: p.open('w').write(json_dumps(self.cfg))),
+            ('vocab', lambda p: self.vocab.to_disk(p)),
             ('model', lambda p: p.open('wb').write(self.model.to_bytes())),
-            ('vocab', lambda p: self.vocab.to_disk(p))
         ))
         util.to_disk(path, serialize, exclude)
 
@@ -168,8 +168,8 @@ class BaseThincComponent(object):
 
         deserialize = OrderedDict((
             ('cfg', lambda p: self.cfg.update(_load_cfg(p))),
-            ('model', load_model),
             ('vocab', lambda p: self.vocab.from_disk(p)),
+            ('model', load_model),
         ))
         util.from_disk(path, deserialize, exclude)
         return self
@@ -289,6 +289,7 @@ class TokenVectorEncoder(BaseThincComponent):
         pipeline (list): The pipeline the model is part of.
         """
         if self.model is True:
+            self.cfg['pretrained_dims'] = self.vocab.vectors_length
             self.model = self.Model(**self.cfg)
             link_vectors_to_models(self.vocab)
 
@@ -398,6 +399,7 @@ class NeuralTagger(BaseThincComponent):
                                           vocab.morphology.lemmatizer,
                                           exc=vocab.morphology.exc)
         if self.model is True:
+            self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
             self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
             link_vectors_to_models(self.vocab)
 
@@ -486,6 +488,7 @@ class NeuralLabeller(NeuralTagger):
         self.model = model
         self.cfg = dict(cfg)
         self.cfg.setdefault('cnn_maxout_pieces', 2)
+        self.cfg.setdefault('pretrained_dims', self.vocab.vectors.data.shape[1])
 
     @property
     def labels(self):
@@ -508,8 +511,8 @@ class NeuralLabeller(NeuralTagger):
                         self.labels[dep] = len(self.labels)
         token_vector_width = pipeline[0].model.nO
         if self.model is True:
-            self.model = self.Model(len(self.labels), token_vector_width=token_vector_width,
-                                    pretrained_dims=self.vocab.vectors_length)
+            self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
+            self.model = self.Model(len(self.labels), **self.cfg)
             link_vectors_to_models(self.vocab)
 
     @classmethod

From a2357cce3fdf38382fcce783b13132f4d473ddfd Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 23 Sep 2017 02:57:31 +0200
Subject: [PATCH 084/213] Set random seed in train script

---
 spacy/cli/train.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index c87aabb01..3551c4f2c 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -11,6 +11,8 @@ import tqdm
 from thinc.neural._classes.model import Model
 from thinc.neural.optimizers import linear_decay
 from timeit import default_timer as timer
+import random
+import numpy.random
 
 from ..tokens.doc import Doc
 from ..scorer import Scorer
@@ -21,6 +23,9 @@ from .. import util
 from .. import displacy
 from ..compat import json_dumps
 
+random.seed(0)
+numpy.random.seed(0)
+
 
 @plac.annotations(
     lang=("model language", "positional", None, str),

From 386c1a5bd886f43bfc9b6ce2482deb948b6b0ccc Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 23 Sep 2017 02:58:06 +0200
Subject: [PATCH 085/213] Fix tagger training

---
 spacy/pipeline.pyx | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index 8ad62d696..5ab70f2dd 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -343,6 +343,7 @@ class NeuralTagger(BaseThincComponent):
 
         tag_scores, bp_tag_scores = self.model.begin_update(docs, drop=drop)
         loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
+        bp_tag_scores(d_tag_scores, sgd=sgd)
 
         if losses is not None:
             losses[self.name] += loss
@@ -386,15 +387,13 @@ class NeuralTagger(BaseThincComponent):
             vocab.morphology = Morphology(vocab.strings, new_tag_map,
                                           vocab.morphology.lemmatizer,
                                           exc=vocab.morphology.exc)
-        token_vector_width = pipeline[0].model.nO
         if self.model is True:
-            self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width,
+            self.model = self.Model(self.vocab.morphology.n_tags,
                                     pretrained_dims=self.vocab.vectors_length)
 
     @classmethod
-    def Model(cls, n_tags, token_vector_width, pretrained_dims=0, **cfg):
-        return build_tagger_model(n_tags, token_vector_width,
-                                  pretrained_dims, **cfg)
+    def Model(cls, n_tags, **cfg):
+        return build_tagger_model(n_tags, **cfg)
 
     def use_params(self, params):
         with self.model.use_params(params):

From 4bd6a12b1f6c70b4ebdcc06f65e5846ba942b5c4 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 23 Sep 2017 02:58:54 +0200
Subject: [PATCH 086/213] Fix Tok2Vec

---
 spacy/_ml.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 37bf6335b..74757f502 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -475,14 +475,16 @@ def getitem(i):
     return layerize(getitem_fwd)
 
 
-def build_tagger_model(nr_class, token_vector_width, pretrained_dims=0, **cfg):
+def build_tagger_model(nr_class, pretrained_dims=0, **cfg):
     embed_size = util.env_opt('embed_size', 4000)
+    if 'token_vector_width' not in cfg:
+        token_vector_width = util.env_opt('token_vector_width', 128)
     with Model.define_operators({'>>': chain, '+': add}):
         tok2vec = Tok2Vec(token_vector_width, embed_size,
                           pretrained_dims=pretrained_dims)
-        model = with_flatten(
+        model = (
             tok2vec
-            >> Softmax(nr_class, token_vector_width)
+            >> with_flatten(Softmax(nr_class, token_vector_width))
         )
     model.nI = None
     model.tok2vec = tok2vec

From 0795857dcbb1f224e7ac3f45208ba1520730a82a Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 23 Sep 2017 02:59:18 +0200
Subject: [PATCH 087/213] Fix beam parsing

---
 spacy/syntax/nn_parser.pyx | 1 +
 1 file changed, 1 insertion(+)

diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 77f99624a..a56ed35a8 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -370,6 +370,7 @@ cdef class Parser:
             docs = list(docs)
             if beam_width == 1:
                 parse_states = self.parse_batch(docs)
+                beams = []
             else:
                 beams = self.beam_parse(docs,
                             beam_width=beam_width, beam_density=beam_density)

From e93d43a43a03ed207a7d9efe5817adb0afb0ef82 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 22 Sep 2017 20:00:40 -0500
Subject: [PATCH 088/213] Fix training with preset vectors

---
 spacy/cli/train.py | 45 ++++++++++-----------------------------------
 1 file changed, 10 insertions(+), 35 deletions(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 801706614..96233406d 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -30,14 +30,14 @@ from ..compat import json_dumps
     n_iter=("number of iterations", "option", "n", int),
     n_sents=("number of sentences", "option", "ns", int),
     use_gpu=("Use GPU", "option", "g", int),
-    resume=("Whether to resume training", "flag", "R", bool),
+    vectors=("Model to load vectors from", "option", "v"),
     no_tagger=("Don't train tagger", "flag", "T", bool),
     no_parser=("Don't train parser", "flag", "P", bool),
     no_entities=("Don't train NER", "flag", "N", bool),
     gold_preproc=("Use gold preprocessing", "flag", "G", bool),
 )
 def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
-          use_gpu=-1, resume=False, no_tagger=False, no_parser=False, no_entities=False,
+          use_gpu=-1, vectors=None, no_tagger=False, no_parser=False, no_entities=False,
           gold_preproc=False):
     """
     Train a model. Expects data in spaCy's JSON format.
@@ -73,25 +73,20 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
     corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
     n_train_words = corpus.count_train()
 
-    if not resume:
-        lang_class = util.get_lang_class(lang)
-        nlp = lang_class(pipeline=pipeline)
-        optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
-    else:
-        print("Load resume")
-        util.use_gpu(use_gpu)
-        nlp = _resume_model(lang, pipeline, corpus)
-        optimizer = nlp.resume_training(device=use_gpu)
-        lang_class = nlp.__class__
-
+    lang_class = util.get_lang_class(lang)
+    nlp = lang_class(pipeline=pipeline)
+    if vectors:
+        util.load_model(vectors, vocab=nlp.vocab)
+    optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
     nlp._optimizer = None
 
     print("Itn.\tLoss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
     try:
+        train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
+                                       gold_preproc=gold_preproc, max_length=0)
+        train_docs = list(train_docs)
         for i in range(n_iter):
             with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
-                train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
-                                               gold_preproc=gold_preproc, max_length=0)
                 losses = {}
                 for batch in minibatch(train_docs, size=batch_sizes):
                     docs, golds = zip(*batch)
@@ -124,26 +119,6 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
         except:
             pass
 
-
-def _resume_model(lang, pipeline, corpus):
-    nlp = util.load_model(lang)
-    pipes = {getattr(pipe, 'name', None) for pipe in nlp.pipeline}
-    for name in pipeline:
-        if name not in pipes:
-            factory = nlp.Defaults.factories[name]
-            for pipe in factory(nlp):
-                if hasattr(pipe, 'begin_training'):
-                    pipe.begin_training(corpus.train_tuples,
-                                        pipeline=nlp.pipeline)
-                nlp.pipeline.append(pipe)
-    nlp.meta['pipeline'] = pipeline
-    if nlp.vocab.vectors.data.shape[1] >= 1:
-        nlp.vocab.vectors.data = Model.ops.asarray(
-                                    nlp.vocab.vectors.data)
-
-    return nlp
-
-
 def _render_parses(i, to_render):
     to_render[0].user_data['title'] = "Batch %d" % i
     with Path('/tmp/entities.html').open('w') as file_:

From 7dc61b3f439c436ae4ca69ae0d9a2d14dda65723 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 22 Sep 2017 20:00:50 -0500
Subject: [PATCH 089/213] Whitespace

---
 spacy/language.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/spacy/language.py b/spacy/language.py
index 9d1538a18..130d7989d 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -362,7 +362,6 @@ class Language(object):
         self._optimizer.device = device
         return self._optimizer
 
-
     def begin_training(self, get_gold_tuples=None, **cfg):
         """Allocate models, pre-process training data and acquire a trainer and
         optimizer. Used as a contextmanager.

From 5a7fd0fd3683fb5949f11e81109853020113ca1e Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Fri, 22 Sep 2017 20:11:52 -0500
Subject: [PATCH 090/213] Fix vector linkage

---
 spacy/language.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/spacy/language.py b/spacy/language.py
index d63d4d163..edf0a4b5c 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -34,6 +34,7 @@ from .lang.tag_map import TAG_MAP
 from .lang.lex_attrs import LEX_ATTRS
 from . import util
 from .scorer import Scorer
+from ._ml import link_vectors_to_models
 
 
 class BaseDefaults(object):
@@ -370,6 +371,7 @@ class Language(object):
                     self.vocab.vectors.data)
         else:
             device = None
+        link_vectors_to_models(self.vocab)
         for proc in self.pipeline:
             if hasattr(proc, 'begin_training'):
                 context = proc.begin_training(get_gold_tuples(),

From 63bd87508d12ca3a55d6ab05834cf8e69cc5e21e Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sat, 23 Sep 2017 04:39:17 -0500
Subject: [PATCH 091/213] Don't use iterated convolutions

---
 spacy/_ml.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 7cdf9c68b..3bb76c268 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -270,7 +270,7 @@ def Tok2Vec(width, embed_size, **kwargs):
         tok2vec = (
             FeatureExtracter(cols)
             >> with_flatten(
-                embed >> (convolution * 4), pad=4)
+                embed >> (convolution ** 4), pad=4)
         )
 
         # Work around thinc API limitations :(. TODO: Revise in Thinc 7

From dc3a623d0008c1362a2f26369f777b5ceef8958b Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 24 Sep 2017 05:00:37 -0500
Subject: [PATCH 092/213] Remove unused update_shared argument

---
 spacy/cli/train.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 777121616..055cccab0 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -96,8 +96,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                 for batch in minibatch(train_docs, size=batch_sizes):
                     docs, golds = zip(*batch)
                     nlp.update(docs, golds, sgd=optimizer,
-                               drop=next(dropout_rates), losses=losses,
-                               update_shared=True)
+                               drop=next(dropout_rates), losses=losses)
                     pbar.update(sum(len(doc) for doc in docs))
 
             with nlp.use_params(optimizer.averages):

From 204b58c86491fa9f3cbddadf026c9a49e57b521a Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 24 Sep 2017 05:01:03 -0500
Subject: [PATCH 093/213] Fix evaluation during training

---
 spacy/cli/train.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 055cccab0..d9c345b97 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -105,10 +105,10 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                 nlp.to_disk(epoch_model_path)
                 nlp_loaded = lang_class(pipeline=pipeline)
                 nlp_loaded = nlp_loaded.from_disk(epoch_model_path)
-                scorer = nlp.evaluate(
-                            corpus.dev_docs(
-                                nlp,
-                                gold_preproc=gold_preproc))
+                scorer = nlp_loaded.evaluate(
+                            list(corpus.dev_docs(
+                                nlp_loaded,
+                                gold_preproc=gold_preproc)))
                 acc_loc =(output_path / ('model%d' % i) / 'accuracy.json')
                 with acc_loc.open('w') as file_:
                     file_.write(json_dumps(scorer.scores))

From 72bbcc0871568fc6944a45e1aa4907735c743453 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 24 Sep 2017 05:01:31 -0500
Subject: [PATCH 094/213] Handle lemmatization for unknown string IDs

---
 spacy/morphology.pyx | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/spacy/morphology.pyx b/spacy/morphology.pyx
index 13a0ed8e3..5ee11c151 100644
--- a/spacy/morphology.pyx
+++ b/spacy/morphology.pyx
@@ -146,6 +146,8 @@ cdef class Morphology:
                 self.add_special_case(tag_str, form_str, attrs)
 
     def lemmatize(self, const univ_pos_t univ_pos, attr_t orth, morphology):
+        if orth not in self.strings:
+            return orth
         cdef unicode py_string = self.strings[orth]
         if self.lemmatizer is None:
             return self.strings.add(py_string.lower())

From 8716ffe57d71cd0cd8d1e34b0417006e588ae478 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 24 Sep 2017 05:01:45 -0500
Subject: [PATCH 095/213] Serialize vocab last

---
 spacy/language.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/language.py b/spacy/language.py
index edf0a4b5c..502430368 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -491,7 +491,6 @@ class Language(object):
         """
         path = util.ensure_path(path)
         serializers = OrderedDict((
-            ('vocab', lambda p: self.vocab.to_disk(p)),
             ('tokenizer', lambda p: self.tokenizer.to_disk(p, vocab=False)),
             ('meta.json', lambda p: p.open('w').write(json_dumps(self.meta)))
         ))
@@ -503,6 +502,7 @@ class Language(object):
             if not hasattr(proc, 'to_disk'):
                 continue
             serializers[proc.name] = lambda p, proc=proc: proc.to_disk(p, vocab=False)
+        serializers['vocab'] = lambda p: self.vocab.to_disk(p)
         util.to_disk(path, serializers, {p: False for p in disable})
 
     def from_disk(self, path, disable=tuple()):

From 39f390dba7e4a5e7ac224b27731e8c463cb92f7d Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 25 Sep 2017 16:20:49 +0200
Subject: [PATCH 096/213] Add docstrings for Pipe API

---
 spacy/pipeline.pyx | 37 ++++++++++++++++++++++++++++++++++---
 1 file changed, 34 insertions(+), 3 deletions(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index dcc06cdf7..fef925d85 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -88,17 +88,30 @@ class BaseThincComponent(object):
 
     @classmethod
     def Model(cls, *shape, **kwargs):
+        '''Initialize a model for the pipe.'''
         raise NotImplementedError
 
     def __init__(self, vocab, model=True, **cfg):
+        '''Create a new pipe instance.'''
         raise NotImplementedError
 
     def __call__(self, doc):
+        '''Apply the pipe to one document. The document is
+        modified in-place, and returned.
+        
+        Both __call__ and pipe should delegate to the `predict()`
+        and `set_annotations()` methods.
+        '''
         scores = self.predict([doc])
         self.set_annotations([doc], scores)
         return doc
 
     def pipe(self, stream, batch_size=128, n_threads=-1):
+        '''Apply the pipe to a stream of documents.
+
+        Both __call__ and pipe should delegate to the `predict()`
+        and `set_annotations()` methods.
+        '''
         for docs in cytoolz.partition_all(batch_size, stream):
             docs = list(docs)
             scores = self.predict(docs)
@@ -106,27 +119,42 @@ class BaseThincComponent(object):
             yield from docs
 
     def predict(self, docs):
+        '''Apply the pipeline's model to a batch of docs, without
+        modifying them.
+        '''
         raise NotImplementedError
 
     def set_annotations(self, docs, scores):
+        '''Modify a batch of documents, using pre-computed scores.'''
         raise NotImplementedError
 
-    def update(self, docs_tensors, golds, state=None, drop=0., sgd=None, losses=None):
+    def update(self, docs, golds, drop=0., sgd=None, losses=None):
+        '''Learn from a batch of documents and gold-standard information,
+        updating the pipe's model.
+
+        Delegates to predict() and get_loss().
+        '''
         raise NotImplementedError
 
     def get_loss(self, docs, golds, scores):
+        '''Find the loss and gradient of loss for the batch of
+        documents and their predicted scores.'''
         raise NotImplementedError
 
     def begin_training(self, gold_tuples=tuple(), pipeline=None):
-        token_vector_width = pipeline[0].model.nO
+        '''Initialize the pipe for training, using data exampes if available.
+        If no model has been initialized yet, the model is added.'''
         if self.model is True:
-            self.model = self.Model(1, token_vector_width)
+            self.model = self.Model(**self.cfg)
 
     def use_params(self, params):
+        '''Modify the pipe's model, to use the given parameter values.
+        '''
         with self.model.use_params(params):
             yield
 
     def to_bytes(self, **exclude):
+        '''Serialize the pipe to a bytestring.'''
         serialize = OrderedDict((
             ('cfg', lambda: json_dumps(self.cfg)),
             ('model', lambda: self.model.to_bytes()),
@@ -135,6 +163,7 @@ class BaseThincComponent(object):
         return util.to_bytes(serialize, exclude)
 
     def from_bytes(self, bytes_data, **exclude):
+        '''Load the pipe from a bytestring.'''
         def load_model(b):
             if self.model is True:
                 self.cfg['pretrained_dims'] = self.vocab.vectors_length
@@ -150,6 +179,7 @@ class BaseThincComponent(object):
         return self
 
     def to_disk(self, path, **exclude):
+        '''Serialize the pipe to disk.'''
         serialize = OrderedDict((
             ('cfg', lambda p: p.open('w').write(json_dumps(self.cfg))),
             ('model', lambda p: p.open('wb').write(self.model.to_bytes())),
@@ -158,6 +188,7 @@ class BaseThincComponent(object):
         util.to_disk(path, serialize, exclude)
 
     def from_disk(self, path, **exclude):
+        '''Load the pipe from disk.'''
         def load_model(p):
             if self.model is True:
                 self.cfg['pretrained_dims'] = self.vocab.vectors_length

From d2d35b63b770c74f80e560fbb2efc5491064608c Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Mon, 25 Sep 2017 18:37:13 +0200
Subject: [PATCH 097/213] Fix formatting

---
 spacy/pipeline.pyx | 50 +++++++++++++++++++++++-----------------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index f660f88a6..90ff1ad88 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -48,7 +48,7 @@ from .parts_of_speech import X
 
 
 class SentenceSegmenter(object):
-    '''A simple spaCy hook, to allow custom sentence boundary detection logic
+    """A simple spaCy hook, to allow custom sentence boundary detection logic
     (that doesn't require the dependency parse).
 
     To change the sentence boundary detection strategy, pass a generator
@@ -57,7 +57,7 @@ class SentenceSegmenter(object):
 
     Sentence detection strategies should be generators that take `Doc` objects
     and yield `Span` objects for each sentence.
-    '''
+    """
     name = 'sbd'
 
     def __init__(self, vocab, strategy=None):
@@ -89,30 +89,30 @@ class BaseThincComponent(object):
 
     @classmethod
     def Model(cls, *shape, **kwargs):
-        '''Initialize a model for the pipe.'''
+        """Initialize a model for the pipe."""
         raise NotImplementedError
 
     def __init__(self, vocab, model=True, **cfg):
-        '''Create a new pipe instance.'''
+        """Create a new pipe instance."""
         raise NotImplementedError
 
     def __call__(self, doc):
-        '''Apply the pipe to one document. The document is
+        """Apply the pipe to one document. The document is
         modified in-place, and returned.
-        
+
         Both __call__ and pipe should delegate to the `predict()`
         and `set_annotations()` methods.
-        '''
+        """
         scores = self.predict([doc])
         self.set_annotations([doc], scores)
         return doc
 
     def pipe(self, stream, batch_size=128, n_threads=-1):
-        '''Apply the pipe to a stream of documents.
+        """Apply the pipe to a stream of documents.
 
         Both __call__ and pipe should delegate to the `predict()`
         and `set_annotations()` methods.
-        '''
+        """
         for docs in cytoolz.partition_all(batch_size, stream):
             docs = list(docs)
             scores = self.predict(docs)
@@ -120,43 +120,43 @@ class BaseThincComponent(object):
             yield from docs
 
     def predict(self, docs):
-        '''Apply the pipeline's model to a batch of docs, without
+        """Apply the pipeline's model to a batch of docs, without
         modifying them.
-        '''
+        """
         raise NotImplementedError
 
     def set_annotations(self, docs, scores):
-        '''Modify a batch of documents, using pre-computed scores.'''
+        """Modify a batch of documents, using pre-computed scores."""
         raise NotImplementedError
 
     def update(self, docs, golds, drop=0., sgd=None, losses=None):
-        '''Learn from a batch of documents and gold-standard information,
+        """Learn from a batch of documents and gold-standard information,
         updating the pipe's model.
 
         Delegates to predict() and get_loss().
-        '''
+        """
         raise NotImplementedError
 
     def get_loss(self, docs, golds, scores):
-        '''Find the loss and gradient of loss for the batch of
-        documents and their predicted scores.'''
+        """Find the loss and gradient of loss for the batch of
+        documents and their predicted scores."""
         raise NotImplementedError
 
     def begin_training(self, gold_tuples=tuple(), pipeline=None):
-        '''Initialize the pipe for training, using data exampes if available.
-        If no model has been initialized yet, the model is added.'''
+        """Initialize the pipe for training, using data exampes if available.
+        If no model has been initialized yet, the model is added."""
         if self.model is True:
             self.model = self.Model(**self.cfg)
         link_vectors_to_models(self.vocab)
 
     def use_params(self, params):
-        '''Modify the pipe's model, to use the given parameter values.
-        '''
+        """Modify the pipe's model, to use the given parameter values.
+        """
         with self.model.use_params(params):
             yield
 
     def to_bytes(self, **exclude):
-        '''Serialize the pipe to a bytestring.'''
+        """Serialize the pipe to a bytestring."""
         serialize = OrderedDict((
             ('cfg', lambda: json_dumps(self.cfg)),
             ('model', lambda: self.model.to_bytes()),
@@ -165,7 +165,7 @@ class BaseThincComponent(object):
         return util.to_bytes(serialize, exclude)
 
     def from_bytes(self, bytes_data, **exclude):
-        '''Load the pipe from a bytestring.'''
+        """Load the pipe from a bytestring."""
         def load_model(b):
             if self.model is True:
                 self.cfg['pretrained_dims'] = self.vocab.vectors_length
@@ -181,7 +181,7 @@ class BaseThincComponent(object):
         return self
 
     def to_disk(self, path, **exclude):
-        '''Serialize the pipe to disk.'''
+        """Serialize the pipe to disk."""
         serialize = OrderedDict((
             ('cfg', lambda p: p.open('w').write(json_dumps(self.cfg))),
             ('vocab', lambda p: self.vocab.to_disk(p)),
@@ -190,7 +190,7 @@ class BaseThincComponent(object):
         util.to_disk(path, serialize, exclude)
 
     def from_disk(self, path, **exclude):
-        '''Load the pipe from disk.'''
+        """Load the pipe from disk."""
         def load_model(p):
             if self.model is True:
                 self.cfg['pretrained_dims'] = self.vocab.vectors_length
@@ -596,7 +596,7 @@ class SimilarityHook(BaseThincComponent):
         return Siamese(Pooling(max_pool, mean_pool), CauchySimilarity(length))
 
     def __call__(self, doc):
-        '''Install similarity hook'''
+        """Install similarity hook"""
         doc.user_hooks['similarity'] = self.predict
         return doc
 

From edf7e4881debb5244e445225a98117f9263d9b0d Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Mon, 25 Sep 2017 19:00:47 +0200
Subject: [PATCH 098/213] Add meta.json option to cli.train and add relevant
 properties

Add accuracy scores to meta.json instead of accuracy.json and replace
all relevant properties like lang, pipeline, spacy_version in existing
meta.json. If not present, also add name and version placeholders to
make it packagable.
---
 spacy/cli/train.py | 25 ++++++++++++++++++++-----
 1 file changed, 20 insertions(+), 5 deletions(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 96233406d..d71523a9c 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -18,6 +18,7 @@ from ..gold import GoldParse, merge_sents
 from ..gold import GoldCorpus, minibatch
 from ..util import prints
 from .. import util
+from .. import about
 from .. import displacy
 from ..compat import json_dumps
 
@@ -35,10 +36,11 @@ from ..compat import json_dumps
     no_parser=("Don't train parser", "flag", "P", bool),
     no_entities=("Don't train NER", "flag", "N", bool),
     gold_preproc=("Use gold preprocessing", "flag", "G", bool),
+    meta_path=("Optional path to meta.json. All relevant properties will be overwritten.", "option", "m", Path)
 )
 def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
           use_gpu=-1, vectors=None, no_tagger=False, no_parser=False, no_entities=False,
-          gold_preproc=False):
+          gold_preproc=False, meta_path=None):
     """
     Train a model. Expects data in spaCy's JSON format.
     """
@@ -47,13 +49,19 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
     output_path = util.ensure_path(output_dir)
     train_path = util.ensure_path(train_data)
     dev_path = util.ensure_path(dev_data)
+    meta_path = util.ensure_path(meta_path)
     if not output_path.exists():
         output_path.mkdir()
     if not train_path.exists():
         prints(train_path, title="Training data not found", exits=1)
     if dev_path and not dev_path.exists():
         prints(dev_path, title="Development data not found", exits=1)
-
+    if meta_path is not None and not meta_path.exists():
+        prints(meta_path, title="meta.json not found", exits=1)
+    meta = util.read_json(meta_path) if meta_path else {}
+    if not isinstance(meta, dict):
+        prints("Expected dict but got: {}".format(type(meta)),
+               title="Not a valid meta.json format", exits=1)
 
     pipeline = ['token_vectors', 'tags', 'dependencies', 'entities']
     if no_tagger and 'tags' in pipeline: pipeline.remove('tags')
@@ -105,9 +113,16 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                             corpus.dev_docs(
                                 nlp,
                                 gold_preproc=gold_preproc))
-                acc_loc =(output_path / ('model%d' % i) / 'accuracy.json')
-                with acc_loc.open('w') as file_:
-                    file_.write(json_dumps(scorer.scores))
+                meta_loc = output_path / ('model%d' % i) / 'meta.json'
+                meta['accuracy'] = scorer.scores
+                meta['lang'] = nlp.lang
+                meta['pipeline'] = pipeline
+                meta['spacy_version'] = '>=%s' % about.__version__
+                meta.setdefault('name', 'model%d' % i)
+                meta.setdefault('version', '0.0.0')
+
+                with meta_loc.open('w') as file_:
+                    file_.write(json_dumps(meta))
                 util.set_env_log(True)
             print_progress(i, losses, scorer.scores)
     finally:

From 4ae9ea76845ad141f12d5bfa82ed5975830322ca Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 05:41:35 -0500
Subject: [PATCH 099/213] Remove unused argument in Language

---
 spacy/language.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/spacy/language.py b/spacy/language.py
index 502430368..701b5c140 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -279,8 +279,7 @@ class Language(object):
     def make_doc(self, text):
         return self.tokenizer(text)
 
-    def update(self, docs, golds, drop=0., sgd=None, losses=None,
-            update_shared=False):
+    def update(self, docs, golds, drop=0., sgd=None, losses=None):
         """Update the models in the pipeline.
 
         docs (iterable): A batch of `Doc` objects.

From bf917225ab123f354ead66f9685558cd52129fff Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 05:42:52 -0500
Subject: [PATCH 100/213] Allow multi-task objectives during training

---
 spacy/pipeline.pyx         | 109 ++++++++++++++++++++++++++++---------
 spacy/syntax/nn_parser.pxd |   1 +
 spacy/syntax/nn_parser.pyx |  16 +++++-
 3 files changed, 99 insertions(+), 27 deletions(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index b91ddcc9d..17e9a15de 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -291,7 +291,7 @@ class TokenVectorEncoder(BaseThincComponent):
         if self.model is True:
             self.cfg['pretrained_dims'] = self.vocab.vectors_length
             self.model = self.Model(**self.cfg)
-            link_vectors_to_models(self.vocab)
+        link_vectors_to_models(self.vocab)
 
 
 class NeuralTagger(BaseThincComponent):
@@ -395,7 +395,7 @@ class NeuralTagger(BaseThincComponent):
         if self.model is True:
             self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
             self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
-            link_vectors_to_models(self.vocab)
+        link_vectors_to_models(self.vocab)
 
     @classmethod
     def Model(cls, n_tags, **cfg):
@@ -477,9 +477,25 @@ class NeuralTagger(BaseThincComponent):
 
 class NeuralLabeller(NeuralTagger):
     name = 'nn_labeller'
-    def __init__(self, vocab, model=True, **cfg):
+    def __init__(self, vocab, model=True, target='dep_tag_offset', **cfg):
         self.vocab = vocab
         self.model = model
+        if target == 'dep':
+            self.make_label = self.make_dep
+        elif target == 'tag':
+            self.make_label = self.make_tag
+        elif target == 'ent':
+            self.make_label = self.make_ent
+        elif target == 'dep_tag_offset':
+            self.make_label = self.make_dep_tag_offset
+        elif target == 'ent_tag':
+            self.make_label = self.make_ent_tag
+        elif hasattr(target, '__call__'):
+            self.make_label = target
+        else:
+            raise ValueError(
+                "NeuralLabeller target should be function or one of "
+                "['dep', 'tag', 'ent', 'dep_tag_offset', 'ent_tag']")
         self.cfg = dict(cfg)
         self.cfg.setdefault('cnn_maxout_pieces', 2)
         self.cfg.setdefault('pretrained_dims', self.vocab.vectors.data.shape[1])
@@ -495,43 +511,78 @@ class NeuralLabeller(NeuralTagger):
     def set_annotations(self, docs, dep_ids):
         pass
 
-    def begin_training(self, gold_tuples=tuple(), pipeline=None):
+    def begin_training(self, gold_tuples=tuple(), pipeline=None, tok2vec=None):
         gold_tuples = nonproj.preprocess_training_data(gold_tuples)
         for raw_text, annots_brackets in gold_tuples:
             for annots, brackets in annots_brackets:
                 ids, words, tags, heads, deps, ents = annots
-                for dep in deps:
-                    if dep not in self.labels:
-                        self.labels[dep] = len(self.labels)
-        token_vector_width = pipeline[0].model.nO
+                for i in range(len(ids)):
+                    label = self.make_label(i, words, tags, heads, deps, ents)
+                    if label is not None and label not in self.labels:
+                        self.labels[label] = len(self.labels)
+        print(len(self.labels))
         if self.model is True:
-            self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
-            self.model = self.Model(len(self.labels), **self.cfg)
-            link_vectors_to_models(self.vocab)
+            self.model = chain(
+                tok2vec,
+                Softmax(len(self.labels), 128)
+            )
+        link_vectors_to_models(self.vocab)
 
     @classmethod
-    def Model(cls, n_tags, **cfg):
-        return build_tagger_model(n_tags, **cfg)
+    def Model(cls, n_tags, tok2vec=None, **cfg):
+        return build_tagger_model(n_tags, tok2vec=tok2vec, **cfg)
 
     def get_loss(self, docs, golds, scores):
-        scores = self.model.ops.flatten(scores)
         cdef int idx = 0
         correct = numpy.zeros((scores.shape[0],), dtype='i')
         guesses = scores.argmax(axis=1)
         for gold in golds:
-            for tag in gold.labels:
-                if tag is None or tag not in self.labels:
+            for i in range(len(gold.labels)):
+                label = self.make_label(i, gold.words, gold.tags, gold.heads,
+                                        gold.labels, gold.ents)
+                if label is None or label not in self.labels:
                     correct[idx] = guesses[idx]
                 else:
-                    correct[idx] = self.labels[tag]
+                    correct[idx] = self.labels[label]
                 idx += 1
         correct = self.model.ops.xp.array(correct, dtype='i')
         d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
         d_scores /= d_scores.shape[0]
         loss = (d_scores**2).sum()
-        d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs])
         return float(loss), d_scores
 
+    @staticmethod
+    def make_dep(i, words, tags, heads, deps, ents):
+        if deps[i] is None or heads[i] is None:
+            return None
+        return deps[i]
+
+    @staticmethod
+    def make_tag(i, words, tags, heads, deps, ents):
+        return tags[i]
+
+    @staticmethod
+    def make_ent(i, words, tags, heads, deps, ents):
+        if ents is None:
+            return None
+        return ents[i]
+
+    @staticmethod
+    def make_dep_tag_offset(i, words, tags, heads, deps, ents):
+        if deps[i] is None or heads[i] is None:
+            return None
+        offset = heads[i] - i
+        offset = min(offset, 2)
+        offset = max(offset, -2)
+        return '%s-%s:%d' % (deps[i], tags[i], offset)
+
+    @staticmethod
+    def make_ent_tag(i, words, tags, heads, deps, ents):
+        if ents is None or ents[i] is None:
+            return None
+        else:
+            return '%s-%s' % (tags[i], ents[i])
+
 
 class SimilarityHook(BaseThincComponent):
     """
@@ -695,6 +746,14 @@ cdef class NeuralDependencyParser(NeuralParser):
     name = 'parser'
     TransitionSystem = ArcEager
 
+    def init_multitask_objectives(self, gold_tuples, pipeline, **cfg):
+        for target in ['dep']:
+            labeller = NeuralLabeller(self.vocab, target=target)
+            tok2vec = self.model[0]
+            labeller.begin_training(gold_tuples, pipeline=pipeline, tok2vec=tok2vec)
+            pipeline.append(labeller)
+            self._multitasks.append(labeller)
+
     def __reduce__(self):
         return (NeuralDependencyParser, (self.vocab, self.moves, self.model), None, None)
 
@@ -705,13 +764,13 @@ cdef class NeuralEntityRecognizer(NeuralParser):
 
     nr_feature = 6
 
-    def predict_confidences(self, docs):
-        tensors = [d.tensor for d in docs]
-        samples = []
-        for i in range(10):
-            states = self.parse_batch(docs, tensors, drop=0.3)
-            for state in states:
-                samples.append(self._get_entities(state))
+    def init_multitask_objectives(self, gold_tuples, pipeline, **cfg):
+        for target in []:
+            labeller = NeuralLabeller(self.vocab, target=target)
+            tok2vec = self.model[0]
+            labeller.begin_training(gold_tuples, pipeline=pipeline, tok2vec=tok2vec)
+            pipeline.append(labeller)
+            self._multitasks.append(labeller)
 
     def __reduce__(self):
         return (NeuralEntityRecognizer, (self.vocab, self.moves, self.model), None, None)
diff --git a/spacy/syntax/nn_parser.pxd b/spacy/syntax/nn_parser.pxd
index 524718965..b0b7693b7 100644
--- a/spacy/syntax/nn_parser.pxd
+++ b/spacy/syntax/nn_parser.pxd
@@ -13,6 +13,7 @@ cdef class Parser:
     cdef public object model
     cdef readonly TransitionSystem moves
     cdef readonly object cfg
+    cdef public object _multitasks
 
     cdef void _parse_step(self, StateC* state,
             const float* feat_weights,
diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 9d9eda882..988c092af 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -318,6 +318,7 @@ cdef class Parser:
                 for label in labels:
                     self.moves.add_action(action, label)
         self.model = model
+        self._multitasks = []
 
     def __reduce__(self):
         return (Parser, (self.vocab, self.moves, self.model), None, None)
@@ -419,7 +420,7 @@ cdef class Parser:
         cdef int has_hidden = not getattr(vec2scores, 'is_noop', False)
         while not next_step.empty():
             if not has_hidden:
-                for i in range(
+                for i in cython.parallel.prange(
                         next_step.size(), num_threads=6, nogil=True):
                     self._parse_step(next_step[i],
                         feat_weights, nr_class, nr_feat, nr_piece)
@@ -745,7 +746,7 @@ cdef class Parser:
                 # order, or the model goes out of synch
                 self.cfg.setdefault('extra_labels', []).append(label)
 
-    def begin_training(self, gold_tuples, **cfg):
+    def begin_training(self, gold_tuples, pipeline=None, **cfg):
         if 'model' in cfg:
             self.model = cfg['model']
         gold_tuples = nonproj.preprocess_training_data(gold_tuples)
@@ -756,9 +757,20 @@ cdef class Parser:
         if self.model is True:
             cfg['pretrained_dims'] = self.vocab.vectors_length
             self.model, cfg = self.Model(self.moves.n_moves, **cfg)
+            self.init_multitask_objectives(gold_tuples, pipeline, **cfg)
             link_vectors_to_models(self.vocab)
             self.cfg.update(cfg)
 
+    def init_multitask_objectives(self, gold_tuples, pipeline, **cfg):
+        '''Setup models for secondary objectives, to benefit from multi-task
+        learning. This method is intended to be overridden by subclasses.
+
+        For instance, the dependency parser can benefit from sharing
+        an input representation with a label prediction model. These auxiliary
+        models are discarded after training.
+        '''
+        pass
+
     def preprocess_gold(self, docs_golds):
         for doc, gold in docs_golds:
             yield doc, gold

From 02c65155ababfc16d67714fefbe58723cb595f5f Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 12:50:31 +0200
Subject: [PATCH 101/213] Try to fix crazy travis error

---
 travis.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/travis.sh b/travis.sh
index eed6a96f2..78599665c 100755
--- a/travis.sh
+++ b/travis.sh
@@ -17,6 +17,7 @@ fi
 
 if [ "${VIA}" == "compile" ]; then
   pip install -r requirements.txt
+  python setup.py clean --all
   python setup.py build_ext --inplace
   pip install -e .
 fi

From e34e70673f163e30a549e44fc9a85cf3673f74c9 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 05:51:52 -0500
Subject: [PATCH 102/213] Allow tagger models to be built with pre-defined
 tok2vec layer

---
 spacy/_ml.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 3bb76c268..2e95aa55b 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -512,8 +512,11 @@ def build_tagger_model(nr_class, **cfg):
         token_vector_width = util.env_opt('token_vector_width', 128)
     pretrained_dims = cfg.get('pretrained_dims', 0)
     with Model.define_operators({'>>': chain, '+': add}):
-        tok2vec = Tok2Vec(token_vector_width, embed_size,
-                          pretrained_dims=pretrained_dims)
+        if 'tok2vec' in cfg:
+            tok2vec = cfg['tok2vec']
+        else:
+            tok2vec = Tok2Vec(token_vector_width, embed_size,
+                              pretrained_dims=pretrained_dims)
         model = (
             tok2vec
             >> with_flatten(Softmax(nr_class, token_vector_width))

From 50ad50f96acdd110cdbd4662ca9878dc33e74cea Mon Sep 17 00:00:00 2001
From: Ines Montani <ines.montani@gmail.com>
Date: Tue, 26 Sep 2017 13:11:17 +0200
Subject: [PATCH 103/213] Update matcher.pyx

---
 spacy/matcher.pyx | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/spacy/matcher.pyx b/spacy/matcher.pyx
index 5106161a0..84414c255 100644
--- a/spacy/matcher.pyx
+++ b/spacy/matcher.pyx
@@ -436,6 +436,9 @@ cdef class PhraseMatcher:
             abstract_patterns.append([{tag: True} for tag in get_bilou(length)])
         self.matcher.add('Candidate', None, *abstract_patterns)
         self._callbacks = {}
+        
+    def __len__(self):
+        raise NotImplementedError
 
     def __reduce__(self):
         return (self.__class__, (self.vocab,), None, None)

From 7123139b2bce61f21bcab3f10f179ec235d9ae67 Mon Sep 17 00:00:00 2001
From: Ines Montani <ines.montani@gmail.com>
Date: Tue, 26 Sep 2017 13:13:27 +0200
Subject: [PATCH 104/213] Add __contains__ to PhraseMatcher

---
 spacy/matcher.pyx | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/spacy/matcher.pyx b/spacy/matcher.pyx
index 84414c255..9d7e66835 100644
--- a/spacy/matcher.pyx
+++ b/spacy/matcher.pyx
@@ -439,6 +439,9 @@ cdef class PhraseMatcher:
         
     def __len__(self):
         raise NotImplementedError
+        
+    def __contains__(self):
+        raise NotImplementedError
 
     def __reduce__(self):
         return (self.__class__, (self.vocab,), None, None)

From 5056743ad52f27f58c067abbf21f34e154d60fbd Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 06:44:56 -0500
Subject: [PATCH 105/213] Fix parser serialization

---
 spacy/syntax/nn_parser.pyx | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 988c092af..a77352212 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -7,6 +7,7 @@ from __future__ import unicode_literals, print_function
 
 from collections import Counter, OrderedDict
 import ujson
+import json
 import contextlib
 
 from libc.math cimport exp
@@ -829,7 +830,7 @@ cdef class Parser:
             ('upper_model', lambda: self.model[2].to_bytes()),
             ('vocab', lambda: self.vocab.to_bytes()),
             ('moves', lambda: self.moves.to_bytes(strings=False)),
-            ('cfg', lambda: ujson.dumps(self.cfg))
+            ('cfg', lambda: json.dumps(self.cfg, indent=2, sort_keys=True))
         ))
         if 'model' in exclude:
             exclude['tok2vec_model'] = True
@@ -842,7 +843,7 @@ cdef class Parser:
         deserializers = OrderedDict((
             ('vocab', lambda b: self.vocab.from_bytes(b)),
             ('moves', lambda b: self.moves.from_bytes(b, strings=False)),
-            ('cfg', lambda b: self.cfg.update(ujson.loads(b))),
+            ('cfg', lambda b: self.cfg.update(json.loads(b))),
             ('tok2vec_model', lambda b: None),
             ('lower_model', lambda b: None),
             ('upper_model', lambda b: None)

From 18a27c7579059617518bd87091e1fbd6a073a543 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 06:45:14 -0500
Subject: [PATCH 106/213] Fix typo in tensorizer serialization

---
 spacy/pipeline.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index 17e9a15de..cd6fc3da6 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -145,7 +145,7 @@ class BaseThincComponent(object):
 
         deserialize = OrderedDict((
             ('cfg', lambda b: self.cfg.update(ujson.loads(b))),
-            ('vocab', lambda b: self.vocab.from_bytes(b))
+            ('vocab', lambda b: self.vocab.from_bytes(b)),
             ('model', load_model),
         ))
         util.from_bytes(bytes_data, deserialize, exclude)

From 5aaef3e7b8b44f3af347aaed269380727823c380 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 06:45:47 -0500
Subject: [PATCH 107/213] Dont link vectors in vocab deserialize

---
 spacy/vocab.pyx | 2 --
 1 file changed, 2 deletions(-)

diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx
index 01e074617..0a420849c 100644
--- a/spacy/vocab.pyx
+++ b/spacy/vocab.pyx
@@ -324,7 +324,6 @@ cdef class Vocab:
             self.lexemes_from_bytes(file_.read())
         if self.vectors is not None:
             self.vectors.from_disk(path, exclude='strings.json')
-        link_vectors_to_models(self)
         return self
 
     def to_bytes(self, **exclude):
@@ -364,7 +363,6 @@ cdef class Vocab:
             ('vectors', lambda b: serialize_vectors(b))
         ))
         util.from_bytes(bytes_data, setters, exclude)
-        link_vectors_to_models(self)
         return self
 
     def lexemes_to_bytes(self):

From 74f08e1ad5468ec5cf6545ad0719b736357016d1 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 06:45:56 -0500
Subject: [PATCH 108/213] Update test

---
 spacy/tests/serialize/test_serialize_tagger.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/tests/serialize/test_serialize_tagger.py b/spacy/tests/serialize/test_serialize_tagger.py
index 3154687c3..475be1cef 100644
--- a/spacy/tests/serialize/test_serialize_tagger.py
+++ b/spacy/tests/serialize/test_serialize_tagger.py
@@ -11,7 +11,7 @@ import pytest
 def taggers(en_vocab):
     tagger1 = Tagger(en_vocab)
     tagger2 = Tagger(en_vocab)
-    tagger1.model = tagger1.Model(8, 8)
+    tagger1.model = tagger1.Model(8)
     tagger2.model = tagger1.model
     return (tagger1, tagger2)
 

From 0196ff85da65e118501688eeee96120740655c0b Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 06:54:21 -0500
Subject: [PATCH 109/213] Try to fix travis

---
 travis.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/travis.sh b/travis.sh
index eed6a96f2..1940955b4 100755
--- a/travis.sh
+++ b/travis.sh
@@ -17,6 +17,7 @@ fi
 
 if [ "${VIA}" == "compile" ]; then
   pip install -r requirements.txt
+  export PYTHONPATH=`pwd`
   python setup.py build_ext --inplace
   pip install -e .
 fi

From a181987061d1e1042a21f162cc951219a1e5d485 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 06:55:15 -0500
Subject: [PATCH 110/213] Try to fix appveyor

---
 .appveyor.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.appveyor.yml b/.appveyor.yml
index 12399a5a1..a379cdd31 100644
--- a/.appveyor.yml
+++ b/.appveyor.yml
@@ -24,7 +24,6 @@ install:
   - "%PYTHON%\\python.exe -m pip install wheel"
   - "%PYTHON%\\python.exe -m pip install cython"
   - "%PYTHON%\\python.exe -m pip install -r requirements.txt"
-  - "%PYTHON%\\python.exe setup.py build_ext --inplace"
   - "%PYTHON%\\python.exe -m pip install -e ."
 
 build: off

From ddee15cee957956394677b0dbcfa8ec48a76e6e4 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 14:00:25 +0200
Subject: [PATCH 111/213] Try to fix travis

---
 travis.sh | 1 -
 1 file changed, 1 deletion(-)

diff --git a/travis.sh b/travis.sh
index 78599665c..d1a1b6b29 100755
--- a/travis.sh
+++ b/travis.sh
@@ -18,7 +18,6 @@ fi
 if [ "${VIA}" == "compile" ]; then
   pip install -r requirements.txt
   python setup.py clean --all
-  python setup.py build_ext --inplace
   pip install -e .
 fi
 

From 8c390e23a2ca8c0b24dece18d2faafcbe8066778 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 14:14:46 +0200
Subject: [PATCH 112/213] Require older Cython

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 6298b1982..5d515f7a1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-cython>=0.24
+cython>=0.24,<0.27.0
 pathlib
 numpy>=1.7
 cymem>=1.30,<1.32

From 9bfd585a11e67e0bc3683de491357044ef66b8f0 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 07:28:50 -0500
Subject: [PATCH 113/213] Fix parameter name in .pxd file

---
 spacy/tokens/doc.pxd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/tokens/doc.pxd b/spacy/tokens/doc.pxd
index d0c83e0f8..ad2b9876d 100644
--- a/spacy/tokens/doc.pxd
+++ b/spacy/tokens/doc.pxd
@@ -54,7 +54,7 @@ cdef class Doc:
 
     cdef public object noun_chunks_iterator
 
-    cdef int push_back(self, LexemeOrToken lex_or_tok, bint trailing_space) except -1
+    cdef int push_back(self, LexemeOrToken lex_or_tok, bint has_space) except -1
 
     cpdef np.ndarray to_array(self, object features)
 

From ca28590ddd0f1922d14290170c4c7ff8adcadab2 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 08:13:52 -0500
Subject: [PATCH 114/213] Use dep and ent multi-task objectives for parser'

---
 spacy/pipeline.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index cd6fc3da6..294440494 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -747,7 +747,7 @@ cdef class NeuralDependencyParser(NeuralParser):
     TransitionSystem = ArcEager
 
     def init_multitask_objectives(self, gold_tuples, pipeline, **cfg):
-        for target in ['dep']:
+        for target in ['dep', 'ent']:
             labeller = NeuralLabeller(self.vocab, target=target)
             tok2vec = self.model[0]
             labeller.begin_training(gold_tuples, pipeline=pipeline, tok2vec=tok2vec)

From 698fc0d016c3fde05234ef7125ac56343bd343c9 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 08:31:37 -0500
Subject: [PATCH 115/213] Remove merge artefact

---
 spacy/cli/train.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 2ed66b1a6..6178ecb3b 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -130,7 +130,6 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
 
                 with meta_loc.open('w') as file_:
                     file_.write(json_dumps(meta))
->>>>>>> origin/develop
                 util.set_env_log(True)
             print_progress(i, losses, scorer.scores)
     finally:

From 19c7c09bf735c74274cbf6d75a3ca89b248d3865 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 08:35:53 -0500
Subject: [PATCH 116/213] Fix PhraseMatcher.__contains__

---
 spacy/matcher.pyx | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/spacy/matcher.pyx b/spacy/matcher.pyx
index 9d7e66835..3bc6f859c 100644
--- a/spacy/matcher.pyx
+++ b/spacy/matcher.pyx
@@ -436,11 +436,11 @@ cdef class PhraseMatcher:
             abstract_patterns.append([{tag: True} for tag in get_bilou(length)])
         self.matcher.add('Candidate', None, *abstract_patterns)
         self._callbacks = {}
-        
+
     def __len__(self):
         raise NotImplementedError
-        
-    def __contains__(self):
+
+    def __contains__(self, key):
         raise NotImplementedError
 
     def __reduce__(self):

From 3274b46a0d3255975178f669c7b5c83f57be48fb Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 09:05:53 -0500
Subject: [PATCH 117/213] Try to fix compile error on Windows

---
 spacy/syntax/nn_parser.pyx | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index a77352212..99099cad8 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -419,21 +419,23 @@ cdef class Parser:
         c_token_ids = <int*>token_ids.data
         c_is_valid = <int*>is_valid.data
         cdef int has_hidden = not getattr(vec2scores, 'is_noop', False)
+        cdef int nr_step
         while not next_step.empty():
+            nr_step = next_step.size()
             if not has_hidden:
-                for i in cython.parallel.prange(
-                        next_step.size(), num_threads=6, nogil=True):
+                for i in cython.parallel.prange(nr_step, num_threads=6,
+                                                nogil=True):
                     self._parse_step(next_step[i],
                         feat_weights, nr_class, nr_feat, nr_piece)
             else:
-                for i in range(next_step.size()):
+                for i in range(nr_step):
                     st = next_step[i]
                     st.set_context_tokens(&c_token_ids[i*nr_feat], nr_feat)
                     self.moves.set_valid(&c_is_valid[i*nr_class], st)
                 vectors = state2vec(token_ids[:next_step.size()])
                 scores = vec2scores(vectors)
                 c_scores = <float*>scores.data
-                for i in range(next_step.size()):
+                for i in range(nr_step):
                     st = next_step[i]
                     guess = arg_max_if_valid(
                         &c_scores[i*nr_class], &c_is_valid[i*nr_class], nr_class)

From 10d291f129efe94115ccb679e3c52cf9a0292a19 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 26 Sep 2017 16:11:41 +0200
Subject: [PATCH 118/213] Port over change from #1351

---
 spacy/lang/char_classes.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/lang/char_classes.py b/spacy/lang/char_classes.py
index 0698cfd43..46e422df2 100644
--- a/spacy/lang/char_classes.py
+++ b/spacy/lang/char_classes.py
@@ -29,7 +29,7 @@ _units = ('km km² km³ m m² m³ dm dm² dm³ cm cm² cm³ mm mm² mm³ ha µm
           'kg g mg µg t lb oz m/s km/h kmh mph hPa Pa mbar mb MB kb KB gb GB tb '
           'TB T G M K %')
 _currency = r'\$ £ € ¥ ฿ US\$ C\$ A\$'
-_punct = r'… , : ; \! \? ¿ ¡ \( \) \[ \] \{ \} < > _ # \* &'
+_punct = r'… …… , : ; \! \? ¿ ¡ \( \) \[ \] \{ \} < > _ # \* &'
 _quotes = r'\' \'\' " ” “ `` ` ‘ ´ ‚ , „ » «'
 _hyphens = '- – — -- ---'
 _other_symbols = r'[\p{So}]'

From 5cba67146cc17f8075dc4010927a220c5353500e Mon Sep 17 00:00:00 2001
From: Wannaphong Phatthiyaphaibun <wannaphong@yahoo.com>
Date: Tue, 26 Sep 2017 21:36:27 +0700
Subject: [PATCH 119/213] add thai in spacy2

---
 spacy/lang/th/__init__.py             | 36 ++++++++++++
 spacy/lang/th/stop_words.py           | 62 ++++++++++++++++++++
 spacy/lang/th/tag_map.py              | 81 +++++++++++++++++++++++++++
 spacy/lang/th/tokenizer_exceptions.py | 80 ++++++++++++++++++++++++++
 spacy/tests/conftest.py               |  6 +-
 spacy/tests/lang/th/__init__.py       |  0
 spacy/tests/lang/th/test_tokenizer.py | 13 +++++
 7 files changed, 277 insertions(+), 1 deletion(-)
 create mode 100644 spacy/lang/th/__init__.py
 create mode 100644 spacy/lang/th/stop_words.py
 create mode 100644 spacy/lang/th/tag_map.py
 create mode 100644 spacy/lang/th/tokenizer_exceptions.py
 create mode 100644 spacy/tests/lang/th/__init__.py
 create mode 100644 spacy/tests/lang/th/test_tokenizer.py

diff --git a/spacy/lang/th/__init__.py b/spacy/lang/th/__init__.py
new file mode 100644
index 000000000..4b52cfa25
--- /dev/null
+++ b/spacy/lang/th/__init__.py
@@ -0,0 +1,36 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
+from .tag_map import TAG_MAP
+from .stop_words import STOP_WORDS
+
+from ..tokenizer_exceptions import BASE_EXCEPTIONS
+from ..tokens import Doc
+from ..norm_exceptions import BASE_NORMS
+from ...language import Language
+from ...attrs import LANG, NORM
+from ...util import update_exc, add_lookups
+
+
+class ThaiDefaults(Language.Defaults):
+    lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
+    lex_attr_getters[LANG] = lambda text: 'th'
+    tokenizer_exceptions = TOKENIZER_EXCEPTIONS
+    tag_map = dict(TAG_MAP)
+    stop_words = set(STOP_WORDS)
+
+
+class Thai(Language):
+	lang = 'th'
+	Defaults = ThaiDefaults
+	def make_doc(self, text):
+		try:
+			from pythainlp.tokenize import word_tokenize
+		except ImportError:
+			raise ImportError("The Thai tokenizer requires the PyThaiNLP library: "
+								"https://github.com/wannaphongcom/pythainlp/")
+		words = [x for x in list(word_tokenize(text,"newmm"))]
+		return Doc(self.vocab, words=words, spaces=[False]*len(words))
+
+__all__ = ['Thai']
diff --git a/spacy/lang/th/stop_words.py b/spacy/lang/th/stop_words.py
new file mode 100644
index 000000000..e13dec984
--- /dev/null
+++ b/spacy/lang/th/stop_words.py
@@ -0,0 +1,62 @@
+# encoding: utf8
+from __future__ import unicode_literals
+
+# data from https://github.com/wannaphongcom/pythainlp/blob/dev/pythainlp/corpus/stopwords-th.txt
+# stop words as whitespace-separated list
+STOP_WORDS = set("""
+นี้	นํา	นั้น	นัก	นอกจาก	ทุก	ที่สุด	ที่	ทําให้	ทํา	ทาง	ทั้งนี้	ดัง	ซึ่ง	ช่วง	จาก	จัด	จะ	คือ	ความ	ครั้ง	คง	ขึ้น	ของ
+ขอ	รับ	ระหว่าง	รวม	ยัง	มี	มาก	มา	พร้อม	พบ	ผ่าน	ผล	บาง	น่า	เปิดเผย	เปิด	เนื่องจาก	เดียวกัน	เดียว	เช่น	เฉพาะ	เข้า	ถ้า
+ถูก	ถึง	ต้อง	ต่างๆ	ต่าง	ต่อ	ตาม	ตั้งแต่	ตั้ง	ด้าน	ด้วย	อีก	อาจ	ออก	อย่าง	อะไร	อยู่	อยาก	หาก	หลาย	หลังจาก	แต่	เอง	เห็น
+เลย	เริ่ม	เรา	เมื่อ	เพื่อ	เพราะ	เป็นการ	เป็น	หลัง	หรือ	หนึ่ง	ส่วน	ส่ง	สุด	สําหรับ	ว่า	ลง	ร่วม	ราย	ขณะ	ก่อน	ก็	การ	กับ	กัน
+กว่า	กล่าว	จึง	ไว้	ไป	ได้	ให้	ใน	โดย	แห่ง	แล้ว	และ	แรก	แบบ	ๆ	ทั้ง	วัน	เขา	เคย	ไม่	อยาก	เกิน	เกินๆ	เกี่ยวกัน	เกี่ยวกับ
+เกี่ยวข้อง	เกี่ยวเนื่อง	เกี่ยวๆ	เกือบ	เกือบจะ	เกือบๆ	แก	แก่	แก้ไข	ใกล้	ใกล้ๆ	ไกล	ไกลๆ	ขณะเดียวกัน	ขณะใด	ขณะใดๆ	ขณะที่	ขณะนั้น	ขณะนี้	ขณะหนึ่ง	ขวาง
+ขวางๆ	ขั้น	ใคร	ใคร่	ใคร่จะ	ใครๆ	ง่าย	ง่ายๆ	ไง	จง	จด	จน	จนกระทั่ง	จนกว่า	จนขณะนี้	จนตลอด	จนถึง	จนทั่ว	จนบัดนี้	จนเมื่อ	จนแม้	จนแม้น
+จรด	จรดกับ	จริง	จริงจัง	จริงๆ	จริงๆจังๆ	จวน	จวนจะ	จวนเจียน	จวบ	ซึ่งก็	ซึ่งก็คือ	ซึ่งกัน	ซึ่งกันและกัน	ซึ่งได้แก่	ซึ่งๆ	ณ	ด้วย	ด้วยกัน	ด้วยเช่นกัน	ด้วยที่	ด้วยประการฉะนี้
+ด้วยเพราะ	ด้วยว่า	ด้วยเหตุที่	ด้วยเหตุนั้น	ด้วยเหตุนี้	ด้วยเหตุเพราะ	ด้วยเหตุว่า	ด้วยเหมือนกัน	ดั่ง	ดังกล่าว	ดังกับ	ดั่งกับ	ดังกับว่า	ดั่งกับว่า	ดังเก่า
+ดั่งเก่า	ดังเคย	ใดๆ	ได้	ได้แก่	ได้แต่	ได้ที่	ได้มา	ได้รับ	ตน	ตนเอง	ตนฯ	ตรง	ตรงๆ	ตลอด	ตลอดกาล	ตลอดกาลนาน	ตลอดจน	ตลอดถึง	ตลอดทั้ง
+ตลอดทั่ว	ตลอดทั่วถึง	ตลอดทั่วทั้ง	ตลอดปี	ตลอดไป	ตลอดมา	ตลอดระยะเวลา	ตลอดวัน	ตลอดเวลา	ตลอดศก	ต่อ	ต่อกัน	ถึงแก่	ถึงจะ	ถึงบัดนั้น	ถึงบัดนี้
+ถึงเมื่อ	ถึงเมื่อใด	ถึงเมื่อไร	ถึงแม้	ถึงแม้จะ	ถึงแม้ว่า	ถึงอย่างไร	ถือ	ถือว่า	ถูกต้อง	ถูกๆ	เถอะ	เถิด	ทรง	ทว่า	ทั้งคน	ทั้งตัว	ทั้งที	ทั้งที่	ทั้งนั้น	ทั้งนั้นด้วย	ทั้งนั้นเพราะ
+นอก	นอกจากที่	นอกจากนั้น	นอกจากนี้	นอกจากว่า	นอกนั้น	นอกเหนือ	นอกเหนือจาก	น้อย	น้อยกว่า	น้อยๆ	นะ	น่ะ	นักๆ	นั่น	นั่นไง	นั่นเป็น	นั่นแหละ
+นั่นเอง	นั้นๆ	นับ	นับจากนั้น	นับจากนี้	นับตั้งแต่	นับแต่	นับแต่ที่	นับแต่นั้น	เป็นต้น	เป็นต้นไป	เป็นต้นมา	เป็นแต่	เป็นแต่เพียง	เป็นที	เป็นที่	เป็นที่สุด	เป็นเพราะ
+เป็นเพราะว่า	เป็นเพียง	เป็นเพียงว่า	เป็นเพื่อ	เป็นอัน	เป็นอันมาก	เป็นอันว่า	เป็นอันๆ	เป็นอาทิ	เป็นๆ	เปลี่ยน	เปลี่ยนแปลง	เปิด	เปิดเผย	ไป่	ผ่าน	ผ่านๆ
+ผิด	ผิดๆ	ผู้	เพียงเพื่อ	เพียงไร	เพียงไหน	เพื่อที่	เพื่อที่จะ	เพื่อว่า	เพื่อให้	ภาค	ภาคฯ	ภาย	ภายใต้	ภายนอก	ภายใน	ภายภาค	ภายภาคหน้า	ภายหน้า	ภายหลัง
+มอง	มองว่า	มัก	มักจะ	มัน	มันๆ	มั้ย	มั้ยนะ	มั้ยนั่น	มั้ยเนี่ย	มั้ยล่ะ	ยืนนาน	ยืนยง	ยืนยัน	ยืนยาว	เยอะ	เยอะแยะ	เยอะๆ	แยะ	แยะๆ	รวด	รวดเร็ว	ร่วม	รวมกัน	ร่วมกัน
+รวมด้วย	ร่วมด้วย	รวมถึง	รวมทั้ง	ร่วมมือ	รวมๆ	ระยะ	ระยะๆ	ระหว่าง	รับรอง	รึ	รึว่า	รือ	รือว่า	สิ้นกาลนาน	สืบเนื่อง	สุดๆ	สู่	สูง	สูงกว่า	สูงส่ง	สูงสุด	สูงๆ	เสมือนกับ
+เสมือนว่า	เสร็จ	เสร็จกัน	เสร็จแล้ว	เสร็จสมบูรณ์	เสร็จสิ้น	เสีย	เสียก่อน	เสียจน	เสียจนกระทั่ง	เสียจนถึง	เสียด้วย	เสียนั่น	เสียนั่นเอง	เสียนี่	เสียนี่กระไร	เสียยิ่ง
+เสียยิ่งนัก	เสียแล้ว	ใหญ่ๆ	ให้ดี	ให้แด่	ให้ไป	ใหม่	ให้มา	ใหม่ๆ	ไหน	ไหนๆ	อดีต	อนึ่ง	อย่าง	อย่างเช่น	อย่างดี	อย่างเดียว	อย่างใด	อย่างที่	อย่างน้อย	อย่างนั้น
+อย่างนี้	อย่างโน้น	ก็คือ	ก็แค่	ก็จะ	ก็ดี	ก็ได้	ก็ต่อเมื่อ	ก็ตาม	ก็ตามแต่	ก็ตามที	ก็แล้วแต่	กระทั่ง	กระทำ	กระนั้น	กระผม	กลับ	กล่าวคือ	กลุ่ม	กลุ่มก้อน
+กลุ่มๆ	กว้าง	กว้างขวาง	กว้างๆ	ก่อนหน้า	ก่อนหน้านี้	ก่อนๆ	กันดีกว่า	กันดีไหม	กันเถอะ	กันนะ	กันและกัน	กันไหม	กันเอง	กำลัง	กำลังจะ	กำหนด	กู	เก็บ
+เกิด	เกี่ยวข้อง	แก่	แก้ไข	ใกล้	ใกล้ๆ	ข้า	ข้าง	ข้างเคียง	ข้างต้น	ข้างบน	ข้างล่าง	ข้างๆ	ขาด	ข้าพเจ้า	ข้าฯ	เข้าใจ	เขียน	คงจะ	คงอยู่	ครบ	ครบครัน	ครบถ้วน
+ครั้งกระนั้น	ครั้งก่อน	ครั้งครา	ครั้งคราว	ครั้งใด	ครั้งที่	ครั้งนั้น	ครั้งนี้	ครั้งละ	ครั้งหนึ่ง	ครั้งหลัง	ครั้งหลังสุด	ครั้งไหน	ครั้งๆ	ครัน	ครับ	ครา	คราใด	คราที่	ครานั้น	ครานี้	คราหนึ่ง
+คราไหน	คราว	คราวก่อน	คราวใด	คราวที่	คราวนั้น	คราวนี้	คราวโน้น	คราวละ	คราวหน้า	คราวหนึ่ง	คราวหลัง	คราวไหน	คราวๆ	คล้าย	คล้ายกัน	คล้ายกันกับ
+คล้ายกับ	คล้ายกับว่า	คล้ายว่า	ควร	ค่อน	ค่อนข้าง	ค่อนข้างจะ	ค่อยไปทาง	ค่อนมาทาง	ค่อย	ค่อยๆ	คะ	ค่ะ	คำ	คิด	คิดว่า	คุณ	คุณๆ
+เคยๆ	แค่	แค่จะ	แค่นั้น	แค่นี้	แค่เพียง	แค่ว่า	แค่ไหน	ใคร่	ใคร่จะ	ง่าย	ง่ายๆ	จนกว่า	จนแม้	จนแม้น	จังๆ	จวบกับ	จวบจน	จ้ะ	จ๊ะ	จะได้	จัง	จัดการ	จัดงาน	จัดแจง
+จัดตั้ง	จัดทำ	จัดหา	จัดให้	จับ	จ้า	จ๋า	จากนั้น	จากนี้ 	จากนี้ไป	จำ	จำเป็น 	จำพวก	จึงจะ	จึงเป็น	จู่ๆ	ฉะนั้น	ฉะนี้	ฉัน	เฉกเช่น	เฉย	เฉยๆ	ไฉน	ช่วงก่อน
+ช่วงต่อไป	ช่วงถัดไป	ช่วงท้าย	ช่วงที่	ช่วงนั้น	ช่วงนี้	ช่วงระหว่าง	ช่วงแรก	ช่วงหน้า	ช่วงหลัง	ช่วงๆ	ช่วย	ช้า	ช้านาน	ชาว	ช้าๆ	เช่นก่อน	เช่นกัน	เช่นเคย
+เช่นดัง	เช่นดังก่อน	เช่นดังเก่า	เช่นดังที่	เช่นดังว่า	เช่นเดียวกัน	เช่นเดียวกับ	เช่นใด	เช่นที่	เช่นที่เคย	เช่นที่ว่า	เช่นนั้น	เช่นนั้นเอง	เช่นนี้	เช่นเมื่อ	เช่นไร	เชื่อ
+เชื่อถือ	เชื่อมั่น	เชื่อว่า	ใช่	ใช่ไหม	ใช้	ซะ	ซะก่อน	ซะจน	ซะจนกระทั่ง	ซะจนถึง	ซึ่งได้แก่	ด้วยกัน	ด้วยเช่นกัน	ด้วยที่	ด้วยเพราะ	ด้วยว่า	ด้วยเหตุที่	ด้วยเหตุนั้น
+ด้วยเหตุนี้	ด้วยเหตุเพราะ	ด้วยเหตุว่า	ด้วยเหมือนกัน	ดังกล่าว	ดังกับว่า	ดั่งกับว่า	ดังเก่า	ดั่งเก่า	ดั่งเคย	ต่างก็	ต่างหาก	ตามด้วย	ตามแต่	ตามที่
+ตามๆ	เต็มไปด้วย	เต็มไปหมด	เต็มๆ	แต่ก็	แต่ก่อน	แต่จะ	แต่เดิม	แต่ต้อง	แต่ถ้า	แต่ทว่า	แต่ที่	แต่นั้น	แต่เพียง	แต่เมื่อ	แต่ไร	แต่ละ	แต่ว่า	แต่ไหน	แต่อย่างใด	โต
+โตๆ	ใต้	ถ้าจะ	ถ้าหาก	ถึงแก่	ถึงแม้	ถึงแม้จะ	ถึงแม้ว่า	ถึงอย่างไร	ถือว่า	ถูกต้อง	ทว่า	ทั้งนั้นด้วย	ทั้งปวง	ทั้งเป็น	ทั้งมวล	ทั้งสิ้น	ทั้งหมด	ทั้งหลาย	ทั้งๆ	ทัน
+ทันใดนั้น	ทันที	ทันทีทันใด	ทั่ว	ทำไม	ทำไร	ทำให้	ทำๆ	ที	ที่จริง	ที่ซึ่ง	ทีเดียว	ทีใด	ที่ใด	ที่ได้	ทีเถอะ	ที่แท้	ที่แท้จริง	ที่นั้น	ที่นี้	ทีไร	ทีละ	ที่ละ
+ที่แล้ว	ที่ว่า	ที่แห่งนั้น	ที่ไหน	ทีๆ	ที่ๆ	ทุกคน	ทุกครั้ง	ทุกครา	ทุกคราว	ทุกชิ้น	ทุกตัว	ทุกทาง	ทุกที	ทุกที่	ทุกเมื่อ	ทุกวัน	ทุกวันนี้	ทุกสิ่ง	ทุกหน	ทุกแห่ง	ทุกอย่าง
+ทุกอัน	ทุกๆ	เท่า	เท่ากัน	เท่ากับ	เท่าใด	เท่าที่	เท่านั้น	เท่านี้	เท่าไร	เท่าไหร่	แท้	แท้จริง	เธอ	นอกจากว่า	น้อย	น้อยกว่า	น้อยๆ	น่ะ	นั้นไว	นับแต่นี้	นาง
+นางสาว	น่าจะ	นาน	นานๆ	นาย	นำ	นำพา	นำมา	นิด	นิดหน่อย	นิดๆ	นี่	นี่ไง	นี่นา	นี่แน่ะ	นี่แหละ	นี้แหล่	นี่เอง	นี้เอง	นู่น	นู้น	เน้น	เนี่ย
+เนี่ยเอง	ในช่วง	ในที่	ในเมื่อ	ในระหว่าง	บน	บอก	บอกแล้ว	บอกว่า	บ่อย	บ่อยกว่า	บ่อยครั้ง	บ่อยๆ	บัดดล	บัดเดี๋ยวนี้	บัดนั้น	บัดนี้	บ้าง	บางกว่า
+บางขณะ	บางครั้ง	บางครา	บางคราว	บางที	บางที่	บางแห่ง	บางๆ	ปฏิบัติ	ประกอบ	ประการ	ประการฉะนี้	ประการใด	ประการหนึ่ง	ประมาณ	ประสบ	ปรับ
+ปรากฏ	ปรากฏว่า	ปัจจุบัน	ปิด	เป็นด้วย	เป็นดัง	เป็นต้น	เป็นแต่	เป็นเพื่อ	เป็นอัน	เป็นอันมาก	เป็นอาทิ	ผ่านๆ	ผู้	ผู้ใด	เผื่อ	เผื่อจะ	เผื่อที่	เผื่อว่า	ฝ่าย
+ฝ่ายใด	พบว่า	พยายาม	พร้อมกัน	พร้อมกับ	พร้อมด้วย	พร้อมทั้ง	พร้อมที่	พร้อมเพียง	พวก	พวกกัน	พวกกู	พวกแก	พวกเขา	พวกคุณ	พวกฉัน	พวกท่าน
+พวกที่	พวกเธอ	พวกนั้น	พวกนี้	พวกนู้น	พวกโน้น	พวกมัน	พวกมึง	พอ	พอกัน	พอควร	พอจะ	พอดี	พอตัว	พอที	พอที่	พอเพียง	พอแล้ว	พอสม	พอสมควร
+พอเหมาะ	พอๆ	พา	พึง	พึ่ง	พื้นๆ	พูด	เพราะฉะนั้น	เพราะว่า	เพิ่ง	เพิ่งจะ	เพิ่ม	เพิ่มเติม	เพียง	เพียงแค่	เพียงใด	เพียงแต่	เพียงพอ	เพียงเพราะ
+เพื่อว่า	เพื่อให้	ภายใต้	มองว่า	มั๊ย	มากกว่า	มากมาย	มิ	มิฉะนั้น	มิใช่	มิได้	มีแต่	มึง	มุ่ง	มุ่งเน้น	มุ่งหมาย	เมื่อก่อน	เมื่อครั้ง	เมื่อครั้งก่อน
+เมื่อคราวก่อน	เมื่อคราวที่	เมื่อคราว	เมื่อคืน	เมื่อเช้า	เมื่อใด	เมื่อนั้น	เมื่อนี้	เมื่อเย็น	เมื่อไร	เมื่อวันวาน	เมื่อวาน	เมื่อไหร่	แม้	แม้กระทั่ง	แม้แต่	แม้นว่า	แม้ว่า
+ไม่ค่อย	ไม่ค่อยจะ	ไม่ค่อยเป็น	ไม่ใช่	ไม่เป็นไร	ไม่ว่า	ยก	ยกให้	ยอม	ยอมรับ	ย่อม	ย่อย	ยังคง	ยังงั้น	ยังงี้	ยังโง้น	ยังไง	ยังจะ	ยังแต่	ยาก
+ยาว	ยาวนาน	ยิ่ง	ยิ่งกว่า	ยิ่งขึ้น	ยิ่งขึ้นไป	ยิ่งจน	ยิ่งจะ	ยิ่งนัก	ยิ่งเมื่อ	ยิ่งแล้ว	ยิ่งใหญ่	ร่วมกัน	รวมด้วย	ร่วมด้วย	รือว่า	เร็ว	เร็วๆ	เราๆ	เรียก	เรียบ	เรื่อย
+เรื่อยๆ	ไร	ล้วน	ล้วนจน	ล้วนแต่	ละ	ล่าสุด	เล็ก	เล็กน้อย	เล็กๆ	เล่าว่า	แล้วกัน	แล้วแต่	แล้วเสร็จ	วันใด	วันนั้น	วันนี้	วันไหน	สบาย	สมัย	สมัยก่อน
+สมัยนั้น	สมัยนี้	สมัยโน้น	ส่วนเกิน	ส่วนด้อย	ส่วนดี	ส่วนใด	ส่วนที่	ส่วนน้อย	ส่วนนั้น	ส่วนมาก	ส่วนใหญ่	สั้น	สั้นๆ	สามารถ	สำคัญ	สิ่ง
+สิ่งใด	สิ่งนั้น	สิ่งนี้	สิ่งไหน	สิ้น	เสร็จแล้ว	เสียด้วย	เสียแล้ว	แสดง	แสดงว่า	หน	หนอ	หนอย	หน่อย	หมด	หมดกัน	หมดสิ้น	หรือไง	หรือเปล่า	หรือไม่	หรือยัง
+หรือไร	หากแม้	หากแม้น	หากแม้นว่า	หากว่า	หาความ	หาใช่	หารือ	เหตุ	เหตุผล	เหตุนั้น	เหตุนี้	เหตุไร	เห็นแก่	เห็นควร	เห็นจะ	เห็นว่า	เหลือ	เหลือเกิน	เหล่า
+เหล่านั้น	เหล่านี้	แห่งใด	แห่งนั้น	แห่งนี้	แห่งโน้น	แห่งไหน	แหละ	ให้แก่	ใหญ่	ใหญ่โต	อย่างเช่น	อย่างดี	อย่างเดียว	อย่างใด	อย่างที่	อย่างน้อย	อย่างนั้น	อย่างนี้
+อย่างโน้น	อย่างมาก	อย่างยิ่ง	อย่างไร	อย่างไรก็	อย่างไรก็ได้	อย่างไรเสีย	อย่างละ	อย่างหนึ่ง	อย่างไหน	อย่างๆ	อัน	อันจะ	อันใด	อันได้แก่	อันที่
+อันที่จริง	อันที่จะ	อันเนื่องมาจาก	อันละ	อันไหน	อันๆ	อาจจะ	อาจเป็น	อาจเป็นด้วย	อื่น	อื่นๆ	เอ็ง	เอา	ฯ	ฯล	ฯลฯ
+""".split())
\ No newline at end of file
diff --git a/spacy/lang/th/tag_map.py b/spacy/lang/th/tag_map.py
new file mode 100644
index 000000000..e225f7289
--- /dev/null
+++ b/spacy/lang/th/tag_map.py
@@ -0,0 +1,81 @@
+# encoding: utf8
+# data from Korakot Chaovavanich (https://www.facebook.com/photo.php?fbid=390564854695031&set=p.390564854695031&type=3&permPage=1&ifg=1)
+from __future__ import unicode_literals
+
+from ..symbols import *
+
+TAG_MAP = {
+    #NOUN
+    "NOUN":     {POS: NOUN},
+    "NCMN":     {POS: NOUN},
+    "NTTL":     {POS: NOUN},
+    "CNIT":     {POS: NOUN},
+    "CLTV":     {POS: NOUN},
+    "CMTR":     {POS: NOUN},
+    "CFQC":     {POS: NOUN},
+    "CVBL":     {POS: NOUN},
+    #PRON
+    "PRON":     {POS: PRON},
+    "NPRP":     {POS: PRON},
+    # ADJ
+    "ADJ":      {POS: ADJ},
+    "NONM":      {POS: ADJ},
+    "VATT":      {POS: ADJ},
+    "DONM":      {POS: ADJ},
+    # ADV
+    "ADV":      {POS: ADV},
+    "ADVN":      {POS: ADV},
+    "ADVI":      {POS: ADV},
+    "ADVP":      {POS: ADV},
+    "ADVS":      {POS: ADV},
+	# INT
+    "INT":      {POS: INTJ},
+    # PRON
+    "PROPN":    {POS: PROPN},
+    "PPRS":    {POS: PROPN},
+    "PDMN":    {POS: PROPN},
+    "PNTR":    {POS: PROPN},
+    # DET
+    "DET":      {POS: DET},
+    "DDAN":      {POS: DET},
+    "DDAC":      {POS: DET},
+    "DDBQ":      {POS: DET},
+    "DDAQ":      {POS: DET},
+    "DIAC":      {POS: DET},
+    "DIBQ":      {POS: DET},
+    "DIAQ":      {POS: DET},
+    "DCNM":      {POS: DET},
+    # NUM
+    "NUM":      {POS: NUM},
+    "NCNM":      {POS: NUM},
+    "NLBL":      {POS: NUM},
+    "DCNM":      {POS: NUM},
+	# AUX
+    "AUX":      {POS: AUX},
+    "XVBM":      {POS: AUX},
+    "XVAM":      {POS: AUX},
+    "XVMM":      {POS: AUX},
+    "XVBB":      {POS: AUX},
+    "XVAE":      {POS: AUX},
+	# ADP
+    "ADP":      {POS: ADP},
+    "RPRE":      {POS: ADP},
+    # CCONJ
+    "CCONJ":    {POS: CCONJ},
+    "JCRG":    {POS: CCONJ},
+	# SCONJ
+    "SCONJ":    {POS: SCONJ},
+    "PREL":    {POS: SCONJ},
+    "JSBR":    {POS: SCONJ},
+    "JCMP":    {POS: SCONJ},
+    # PART
+    "PART":    {POS: PART},
+    "FIXN":    {POS: PART},
+    "FIXV":    {POS: PART},
+    "EAFF":    {POS: PART},
+    "AITT":    {POS: PART},
+    "NEG":    {POS: PART},
+    # PUNCT
+    "PUNCT":    {POS: PUNCT},
+    "PUNC":    {POS: PUNCT}
+}
\ No newline at end of file
diff --git a/spacy/lang/th/tokenizer_exceptions.py b/spacy/lang/th/tokenizer_exceptions.py
new file mode 100644
index 000000000..7e3967aed
--- /dev/null
+++ b/spacy/lang/th/tokenizer_exceptions.py
@@ -0,0 +1,80 @@
+# encoding: utf8
+from __future__ import unicode_literals
+
+from ..symbols import *
+from ..language_data import PRON_LEMMA
+
+
+TOKENIZER_EXCEPTIONS = {
+    "ม.ค.": [
+        {ORTH: "ม.ค.", LEMMA: "มกราคม"}
+    ],
+    "ก.พ.": [
+        {ORTH: "ก.พ.", LEMMA: "กุมภาพันธ์"}
+    ],
+    "มี.ค.": [
+        {ORTH: "มี.ค.", LEMMA: "มีนาคม"}
+    ],
+    "เม.ย.": [
+        {ORTH: "เม.ย.", LEMMA: "เมษายน"}
+    ],
+    "พ.ค.": [
+        {ORTH: "พ.ค.", LEMMA: "พฤษภาคม"}
+    ],
+    "มิ.ย.": [
+        {ORTH: "มิ.ย.", LEMMA: "มิถุนายน"}
+    ],
+    "ก.ค.": [
+        {ORTH: "ก.ค.", LEMMA: "กรกฎาคม"}
+    ],
+    "ส.ค.": [
+        {ORTH: "ส.ค.", LEMMA: "สิงหาคม"}
+    ],
+    "ก.ย.": [
+        {ORTH: "ก.ย.", LEMMA: "กันยายน"}
+    ],
+    "ต.ค.": [
+        {ORTH: "ต.ค.", LEMMA: "ตุลาคม"}
+    ],
+    "พ.ย.": [
+        {ORTH: "พ.ย.", LEMMA: "พฤศจิกายน"}
+    ],
+    "ธ.ค.": [
+        {ORTH: "ธ.ค.", LEMMA: "ธันวาคม"}
+    ]
+}
+
+
+# exceptions mapped to a single token containing only ORTH property
+# example: {"string": [{ORTH: "string"}]}
+# converted using strings_to_exc() util
+'''
+ORTH_ONLY = [
+    "a.",
+    "b.",
+    "c.",
+    "d.",
+    "e.",
+    "f.",
+    "g.",
+    "h.",
+    "i.",
+    "j.",
+    "k.",
+    "l.",
+    "m.",
+    "n.",
+    "o.",
+    "p.",
+    "q.",
+    "r.",
+    "s.",
+    "t.",
+    "u.",
+    "v.",
+    "w.",
+    "x.",
+    "y.",
+    "z."
+]
+'''
\ No newline at end of file
diff --git a/spacy/tests/conftest.py b/spacy/tests/conftest.py
index f5d65803a..70c78ab9f 100644
--- a/spacy/tests/conftest.py
+++ b/spacy/tests/conftest.py
@@ -12,7 +12,7 @@ from .. import util
 
 
 _languages = ['bn', 'da', 'de', 'en', 'es', 'fi', 'fr', 'he', 'hu', 'id',
-              'it', 'nb', 'nl', 'pl', 'pt', 'sv', 'xx']
+              'it', 'nb', 'nl', 'pl', 'pt', 'sv', 'th','xx']
 _models = {'en': ['en_core_web_sm'],
            'de': ['de_core_news_md'],
            'fr': ['fr_depvec_web_lg'],
@@ -108,6 +108,10 @@ def he_tokenizer():
 def nb_tokenizer():
     return util.get_lang_class('nb').Defaults.create_tokenizer()
 
+@pytest.fixture
+def th_tokenizer():
+    return util.get_lang_class('th').Defaults.create_tokenizer()
+
 
 @pytest.fixture
 def stringstore():
diff --git a/spacy/tests/lang/th/__init__.py b/spacy/tests/lang/th/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/spacy/tests/lang/th/test_tokenizer.py b/spacy/tests/lang/th/test_tokenizer.py
new file mode 100644
index 000000000..f5925da1e
--- /dev/null
+++ b/spacy/tests/lang/th/test_tokenizer.py
@@ -0,0 +1,13 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+import pytest
+
+TOKENIZER_TESTS = [
+        ("คุณรักผมไหม", ['คุณ', 'รัก', 'ผม', 'ไหม'])
+]
+
+@pytest.mark.parametrize('text,expected_tokens', TOKENIZER_TESTS)
+def test_thai_tokenizer(th_tokenizer, text, expected_tokens):
+	tokens = [token.text for token in th_tokenizer(text)]
+	assert tokens == expected_tokens

From 5ee10379db2d567755b48e8cf4881bb2049181b2 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 26 Sep 2017 16:38:08 +0200
Subject: [PATCH 120/213] Port over changes from #1340

---
 spacy/lang/char_classes.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spacy/lang/char_classes.py b/spacy/lang/char_classes.py
index 46e422df2..b10481411 100644
--- a/spacy/lang/char_classes.py
+++ b/spacy/lang/char_classes.py
@@ -29,9 +29,9 @@ _units = ('km km² km³ m m² m³ dm dm² dm³ cm cm² cm³ mm mm² mm³ ha µm
           'kg g mg µg t lb oz m/s km/h kmh mph hPa Pa mbar mb MB kb KB gb GB tb '
           'TB T G M K %')
 _currency = r'\$ £ € ¥ ฿ US\$ C\$ A\$'
-_punct = r'… …… , : ; \! \? ¿ ¡ \( \) \[ \] \{ \} < > _ # \* &'
+_punct = r'… …… , : ; \! \? ¿ ¡ \( \) \[ \] \{ \} < > _ # \* & 。 ？ ！ ， 、 ； ： ～ ·'
 _quotes = r'\' \'\' " ” “ `` ` ‘ ´ ‚ , „ » «'
-_hyphens = '- – — -- ---'
+_hyphens = '- – — -- --- —— ~'
 _other_symbols = r'[\p{So}]'
 
 UNITS = merge_chars(_units)

From adda08fe14479e465668bcf39dcedd238a4be20e Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 26 Sep 2017 16:39:15 +0200
Subject: [PATCH 121/213] Implement like_num getter for Dutch (via #1177)

---
 spacy/lang/nl/__init__.py  |  2 ++
 spacy/lang/nl/lex_attrs.py | 36 ++++++++++++++++++++++++++++++++++++
 2 files changed, 38 insertions(+)
 create mode 100644 spacy/lang/nl/lex_attrs.py

diff --git a/spacy/lang/nl/__init__.py b/spacy/lang/nl/__init__.py
index 7b948f295..98df8d487 100644
--- a/spacy/lang/nl/__init__.py
+++ b/spacy/lang/nl/__init__.py
@@ -2,6 +2,7 @@
 from __future__ import unicode_literals
 
 from .stop_words import STOP_WORDS
+from .lex_attrs import LEX_ATTRS
 
 from ..tokenizer_exceptions import BASE_EXCEPTIONS
 from ..norm_exceptions import BASE_NORMS
@@ -12,6 +13,7 @@ from ...util import update_exc, add_lookups
 
 class DutchDefaults(Language.Defaults):
     lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
+    lex_attr_getters.update(LEX_ATTRS)
     lex_attr_getters[LANG] = lambda text: 'nl'
     lex_attr_getters[NORM] = add_lookups(Language.Defaults.lex_attr_getters[NORM], BASE_NORMS)
 
diff --git a/spacy/lang/nl/lex_attrs.py b/spacy/lang/nl/lex_attrs.py
new file mode 100644
index 000000000..4a9c0bdc3
--- /dev/null
+++ b/spacy/lang/nl/lex_attrs.py
@@ -0,0 +1,36 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+from ...attrs import LIKE_NUM
+
+
+_num_words = set("""
+nul een één twee drie vier vijf zes zeven acht negen tien elf twaalf dertien
+veertien twintig dertig veertig vijftig zestig zeventig tachtig negentig honderd
+duizend miljoen miljard biljoen biljard triljoen triljard
+""".split())
+
+_ordinal_words = set("""
+eerste tweede derde vierde vijfde zesde zevende achtste negende tiende elfde
+twaalfde dertiende veertiende twintigste dertigste veertigste vijftigste
+zestigste zeventigste tachtigste negentigste honderdste duizendste miljoenste
+miljardste biljoenste biljardste triljoenste triljardste
+""".split())
+
+
+def like_num(text):
+    text = text.replace(',', '').replace('.', '')
+    if text.isdigit():
+        return True
+    if text.count('/') == 1:
+        num, denom = text.split('/')
+        if num.isdigit() and denom.isdigit():
+            return True
+    if text in _num_words:
+        return True
+    return False
+
+
+LEX_ATTRS = {
+    LIKE_NUM: like_num
+}

From 15479b3baea8d0f5cb58bf7d22321646ac4513bc Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 26 Sep 2017 16:43:28 +0200
Subject: [PATCH 122/213] Add comment to like_num re: future work

---
 spacy/lang/nl/lex_attrs.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/spacy/lang/nl/lex_attrs.py b/spacy/lang/nl/lex_attrs.py
index 4a9c0bdc3..08b1df3be 100644
--- a/spacy/lang/nl/lex_attrs.py
+++ b/spacy/lang/nl/lex_attrs.py
@@ -19,6 +19,10 @@ miljardste biljoenste biljardste triljoenste triljardste
 
 
 def like_num(text):
+    # This only does the most basic check for whether a token is a digit
+    # or matches one of the number words. In order to handle numbers like
+    # "drieëntwintig", more work is required.
+    # See this discussion: https://github.com/explosion/spaCy/pull/1177
     text = text.replace(',', '').replace('.', '')
     if text.isdigit():
         return True

From bb5c6314029cc598b667542579fad347044422e9 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 26 Sep 2017 16:47:45 +0200
Subject: [PATCH 123/213] Implement like_num getter for French (via #1161)

---
 spacy/lang/fr/__init__.py  |  2 ++
 spacy/lang/fr/lex_attrs.py | 41 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 43 insertions(+)
 create mode 100644 spacy/lang/fr/lex_attrs.py

diff --git a/spacy/lang/fr/__init__.py b/spacy/lang/fr/__init__.py
index a243b6268..06dcf2d45 100644
--- a/spacy/lang/fr/__init__.py
+++ b/spacy/lang/fr/__init__.py
@@ -4,6 +4,7 @@ from __future__ import unicode_literals
 from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS, TOKEN_MATCH
 from .punctuation import TOKENIZER_SUFFIXES, TOKENIZER_INFIXES
 from .stop_words import STOP_WORDS
+from .lex_attrs import LEX_ATTRS
 from .lemmatizer import LOOKUP
 from .syntax_iterators import SYNTAX_ITERATORS
 
@@ -17,6 +18,7 @@ from ...util import update_exc, add_lookups
 
 class FrenchDefaults(Language.Defaults):
     lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
+    lex_attr_getters.update(LEX_ATTRS)
     lex_attr_getters[LANG] = lambda text: 'fr'
     lex_attr_getters[NORM] = add_lookups(Language.Defaults.lex_attr_getters[NORM], BASE_NORMS)
 
diff --git a/spacy/lang/fr/lex_attrs.py b/spacy/lang/fr/lex_attrs.py
new file mode 100644
index 000000000..41c509dff
--- /dev/null
+++ b/spacy/lang/fr/lex_attrs.py
@@ -0,0 +1,41 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+from ...attrs import LIKE_NUM
+
+
+_num_words = set("""
+zero un deux trois quatre cinq six sept huit neuf dix
+onze douze treize quatorze quinze seize dix-sept dix-huit dix-neuf
+vingt trente quanrante cinquante soixante septante quatre-vingt huitante nonante
+cent mille mil million milliard billion quadrillion quintillion
+sextillion septillion octillion nonillion decillion
+""".split())
+
+_ordinal_words = set("""
+premier deuxième second troisième quatrième cinquième sixième septième huitième neuvième dixième
+onzième douzième treizième quatorzième quinzième seizième dix-septième dix-huitième dix-neufième
+vingtième trentième quanrantième cinquantième soixantième septantième quatre-vingtième huitantième nonantième
+centième millième millionnième milliardième billionnième quadrillionnième quintillionnième
+sextillionnième septillionnième octillionnième nonillionnième decillionnième
+""".split())
+
+
+def like_num(text):
+    # Might require more work?
+    # See this discussion: https://github.com/explosion/spaCy/pull/1161
+    text = text.replace(',', '').replace('.', '')
+    if text.isdigit():
+        return True
+    if text.count('/') == 1:
+        num, denom = text.split('/')
+        if num.isdigit() and denom.isdigit():
+            return True
+    if text in _num_words:
+        return True
+    return False
+
+
+LEX_ATTRS = {
+    LIKE_NUM: like_num
+}

From a2bf4cc7bff9508a6bbc3d97aeffaf815f50b662 Mon Sep 17 00:00:00 2001
From: Wannaphong Phatthiyaphaibun <wannaphong@yahoo.com>
Date: Tue, 26 Sep 2017 21:49:43 +0700
Subject: [PATCH 124/213] fix newline in file

---
 spacy/lang/th/__init__.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/spacy/lang/th/__init__.py b/spacy/lang/th/__init__.py
index 4b52cfa25..368fb9ae5 100644
--- a/spacy/lang/th/__init__.py
+++ b/spacy/lang/th/__init__.py
@@ -12,7 +12,6 @@ from ...language import Language
 from ...attrs import LANG, NORM
 from ...util import update_exc, add_lookups
 
-
 class ThaiDefaults(Language.Defaults):
     lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
     lex_attr_getters[LANG] = lambda text: 'th'

From 665cdab58d7d0308054646c11bbe5c7d8eb54964 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 26 Sep 2017 16:51:52 +0200
Subject: [PATCH 125/213] Port over change from #1126

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 5d515f7a1..225d1fb31 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -16,3 +16,4 @@ pytest>=3.0.6,<4.0.0
 mock>=2.0.0,<3.0.0
 msgpack-python
 msgpack-numpy
+html5lib==1.0b8

From b49cc8153a1672ad5bdc275e71e9ef15d633956f Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 26 Sep 2017 10:00:18 -0500
Subject: [PATCH 126/213] Require correct thinc

---
 requirements.txt | 2 +-
 setup.py         | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 225d1fb31..7fa5d72d3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,7 +3,7 @@ pathlib
 numpy>=1.7
 cymem>=1.30,<1.32
 preshed>=1.0.0,<2.0.0
-thinc>=6.8.1,<6.9.0
+thinc>=6.8.2,<6.9.0
 murmurhash>=0.28,<0.29
 plac<1.0.0,>=0.9.6
 six
diff --git a/setup.py b/setup.py
index 0b0d2cc81..8943d7a2e 100755
--- a/setup.py
+++ b/setup.py
@@ -195,7 +195,7 @@ def setup_package():
                 'murmurhash>=0.28,<0.29',
                 'cymem>=1.30,<1.32',
                 'preshed>=1.0.0,<2.0.0',
-                'thinc>=6.8.1,<6.9.0',
+                'thinc>=6.8.2,<6.9.0',
                 'plac<1.0.0,>=0.9.6',
                 'six',
                 'pathlib',

From 2ea27d07f474332486db0a047999870f40ba37a4 Mon Sep 17 00:00:00 2001
From: Wannaphong Phatthiyaphaibun <wannaphong@yahoo.com>
Date: Tue, 26 Sep 2017 22:14:47 +0700
Subject: [PATCH 127/213] fix tokenizer_exceptions in thai

---
 spacy/lang/th/tokenizer_exceptions.py | 39 +--------------------------
 1 file changed, 1 insertion(+), 38 deletions(-)

diff --git a/spacy/lang/th/tokenizer_exceptions.py b/spacy/lang/th/tokenizer_exceptions.py
index 7e3967aed..c31595893 100644
--- a/spacy/lang/th/tokenizer_exceptions.py
+++ b/spacy/lang/th/tokenizer_exceptions.py
@@ -1,9 +1,7 @@
 # encoding: utf8
 from __future__ import unicode_literals
 
-from ..symbols import *
-from ..language_data import PRON_LEMMA
-
+from ...symbols import *
 
 TOKENIZER_EXCEPTIONS = {
     "ม.ค.": [
@@ -43,38 +41,3 @@ TOKENIZER_EXCEPTIONS = {
         {ORTH: "ธ.ค.", LEMMA: "ธันวาคม"}
     ]
 }
-
-
-# exceptions mapped to a single token containing only ORTH property
-# example: {"string": [{ORTH: "string"}]}
-# converted using strings_to_exc() util
-'''
-ORTH_ONLY = [
-    "a.",
-    "b.",
-    "c.",
-    "d.",
-    "e.",
-    "f.",
-    "g.",
-    "h.",
-    "i.",
-    "j.",
-    "k.",
-    "l.",
-    "m.",
-    "n.",
-    "o.",
-    "p.",
-    "q.",
-    "r.",
-    "s.",
-    "t.",
-    "u.",
-    "v.",
-    "w.",
-    "x.",
-    "y.",
-    "z."
-]
-'''
\ No newline at end of file

From a63f790b8c45f14254d665029f2e5fbcf431a533 Mon Sep 17 00:00:00 2001
From: Wannaphong Phatthiyaphaibun <wannaphong@yahoo.com>
Date: Tue, 26 Sep 2017 22:28:57 +0700
Subject: [PATCH 128/213] fix thai tag_map

---
 spacy/lang/th/tag_map.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spacy/lang/th/tag_map.py b/spacy/lang/th/tag_map.py
index e225f7289..40e5ac44c 100644
--- a/spacy/lang/th/tag_map.py
+++ b/spacy/lang/th/tag_map.py
@@ -2,7 +2,7 @@
 # data from Korakot Chaovavanich (https://www.facebook.com/photo.php?fbid=390564854695031&set=p.390564854695031&type=3&permPage=1&ifg=1)
 from __future__ import unicode_literals
 
-from ..symbols import *
+from ...symbols import *
 
 TAG_MAP = {
     #NOUN
@@ -78,4 +78,4 @@ TAG_MAP = {
     # PUNCT
     "PUNCT":    {POS: PUNCT},
     "PUNC":    {POS: PUNCT}
-}
\ No newline at end of file
+}

From 7fdfb78141a90d1f27ebd50406d0ba3591a1253f Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 26 Sep 2017 17:34:52 +0200
Subject: [PATCH 129/213] Add version option to cli.train

---
 spacy/cli/train.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 6178ecb3b..5e13037fd 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -41,11 +41,12 @@ numpy.random.seed(0)
     no_parser=("Don't train parser", "flag", "P", bool),
     no_entities=("Don't train NER", "flag", "N", bool),
     gold_preproc=("Use gold preprocessing", "flag", "G", bool),
+    version=("Model version", "option", "v", str),
     meta_path=("Optional path to meta.json. All relevant properties will be overwritten.", "option", "m", Path)
 )
 def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
           use_gpu=-1, vectors=None, no_tagger=False, no_parser=False, no_entities=False,
-          gold_preproc=False, meta_path=None):
+          gold_preproc=False, version="0.0.0", meta_path=None):
     """
     Train a model. Expects data in spaCy's JSON format.
     """
@@ -126,7 +127,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                 meta['pipeline'] = pipeline
                 meta['spacy_version'] = '>=%s' % about.__version__
                 meta.setdefault('name', 'model%d' % i)
-                meta.setdefault('version', '0.0.0')
+                meta.setdefault('version', version)
 
                 with meta_loc.open('w') as file_:
                     file_.write(json_dumps(meta))

From 3d5046c499d6e61492820293865c6b7af0e26fd0 Mon Sep 17 00:00:00 2001
From: Wannaphong Phatthiyaphaibun <wannaphong@yahoo.com>
Date: Tue, 26 Sep 2017 22:41:20 +0700
Subject: [PATCH 130/213] fix import in th

---
 spacy/lang/th/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/lang/th/__init__.py b/spacy/lang/th/__init__.py
index 368fb9ae5..b6bdb658f 100644
--- a/spacy/lang/th/__init__.py
+++ b/spacy/lang/th/__init__.py
@@ -6,7 +6,7 @@ from .tag_map import TAG_MAP
 from .stop_words import STOP_WORDS
 
 from ..tokenizer_exceptions import BASE_EXCEPTIONS
-from ..tokens import Doc
+from ...tokens import Doc
 from ..norm_exceptions import BASE_NORMS
 from ...language import Language
 from ...attrs import LANG, NORM

From 1ff62eaee7ad2be5aa5a0a2e95bd00945b2b732b Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 26 Sep 2017 17:59:34 +0200
Subject: [PATCH 131/213] Fix option shortcut to avoid conflict

---
 spacy/cli/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 5e13037fd..4ce86d31e 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -41,7 +41,7 @@ numpy.random.seed(0)
     no_parser=("Don't train parser", "flag", "P", bool),
     no_entities=("Don't train NER", "flag", "N", bool),
     gold_preproc=("Use gold preprocessing", "flag", "G", bool),
-    version=("Model version", "option", "v", str),
+    version=("Model version", "option", "V", str),
     meta_path=("Optional path to meta.json. All relevant properties will be overwritten.", "option", "m", Path)
 )
 def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,

From 7b5263ffa44a4d20d983b117257e72a89dd1f6ff Mon Sep 17 00:00:00 2001
From: Wannaphong Phatthiyaphaibun <wannaphong@yahoo.com>
Date: Tue, 26 Sep 2017 23:54:15 +0700
Subject: [PATCH 132/213] fix thai test

---
 spacy/tests/conftest.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/spacy/tests/conftest.py b/spacy/tests/conftest.py
index 70c78ab9f..b33a7c008 100644
--- a/spacy/tests/conftest.py
+++ b/spacy/tests/conftest.py
@@ -110,6 +110,7 @@ def nb_tokenizer():
 
 @pytest.fixture
 def th_tokenizer():
+    pythainlp = pytest.importorskip("pythainlp")
     return util.get_lang_class('th').Defaults.create_tokenizer()
 
 

From b5dd7e7cc4f10f179e3ad1a0a60c498585e8d86a Mon Sep 17 00:00:00 2001
From: Reza Gharibi <reza.gharibi.rg@gmail.com>
Date: Wed, 27 Sep 2017 03:55:28 +0330
Subject: [PATCH 133/213] Fix typo

---
 website/docs/usage/_spacy-101/_vocab.jade | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/usage/_spacy-101/_vocab.jade b/website/docs/usage/_spacy-101/_vocab.jade
index cff0b106e..3063262d5 100644
--- a/website/docs/usage/_spacy-101/_vocab.jade
+++ b/website/docs/usage/_spacy-101/_vocab.jade
@@ -112,4 +112,4 @@ p
     |  only works if you actually #[em know] that the document contains that
     |  word. To prevent this problem, spaCy will also export the #[code Vocab]
     |  when you save a #[code Doc] or #[code nlp] object. This will give you
-    |  the object and its encoded annotations, plus they "key" to decode it.
+    |  the object and its encoded annotations, plus the "key" to decode it.

From fa1844b132684daf0119383548f0513f94d9dda2 Mon Sep 17 00:00:00 2001
From: Reza Gharibi <reza.gharibi.rg@gmail.com>
Date: Wed, 27 Sep 2017 03:55:54 +0330
Subject: [PATCH 134/213] Fix typo

---
 website/docs/usage/_spacy-101/_language-data.jade | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/docs/usage/_spacy-101/_language-data.jade b/website/docs/usage/_spacy-101/_language-data.jade
index aaca10ebb..1f75b47e8 100644
--- a/website/docs/usage/_spacy-101/_language-data.jade
+++ b/website/docs/usage/_spacy-101/_language-data.jade
@@ -22,7 +22,7 @@ p
 
 +aside-code.
     from spacy.lang.en import English
-    from spacy.lang.en import German
+    from spacy.lang.de import German
 
     nlp_en = English() # includes English data
     nlp_de = German() # includes German data

From 0461b8215816dc30fa87cb16af7dc03334b2857d Mon Sep 17 00:00:00 2001
From: Reza Gharibi <reza.gharibi.rg@gmail.com>
Date: Wed, 27 Sep 2017 03:56:20 +0330
Subject: [PATCH 135/213] Fix typos

---
 website/docs/usage/spacy-101.jade | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/docs/usage/spacy-101.jade b/website/docs/usage/spacy-101.jade
index a54e5cf66..ac3e808b3 100644
--- a/website/docs/usage/spacy-101.jade
+++ b/website/docs/usage/spacy-101.jade
@@ -65,7 +65,7 @@ p
         |  not designed specifically for chat bots, and only provides the
         |  underlying text processing capabilities.
     +item #[strong spaCy is not research software].
-        |  It's is built on the latest research, but it's designed to get
+        |  It's built on the latest research, but it's designed to get
         |  things done. This leads to fairly different design decisions than
         |  #[+a("https://github./nltk/nltk") NLTK]
         |  or #[+a("https://stanfordnlp.github.io/CoreNLP/") CoreNLP], which were
@@ -87,7 +87,7 @@ p
 
 +aside
     |  If one of spaCy's functionalities #[strong needs a model], it means that
-    |  you need to have one our the available
+    |  you need to have one of the available
     |  #[+a("/docs/usage/models") statistical models] installed. Models are used
     |  to #[strong predict] linguistic annotations – for example, if a word is
     |  a verb or a noun.

From 983201a83a2b69174d693cd850be90e80c914168 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 27 Sep 2017 11:43:58 -0500
Subject: [PATCH 136/213] Fix hard-coded vector width

---
 spacy/pipeline.pyx | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index 717a1bd3f..f7e05ece7 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -553,9 +553,10 @@ class NeuralLabeller(NeuralTagger):
                         self.labels[label] = len(self.labels)
         print(len(self.labels))
         if self.model is True:
+            token_vector_width = util.env_opt('token_vector_width')
             self.model = chain(
                 tok2vec,
-                Softmax(len(self.labels), 128)
+                Softmax(len(self.labels), token_vector_width)
             )
         link_vectors_to_models(self.vocab)
 

From 66c388ee01a10218ebba7a174ccf20481c34f0d2 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 27 Sep 2017 11:44:16 -0500
Subject: [PATCH 137/213] Remove unhelpful multitask objectives

---
 spacy/pipeline.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index f7e05ece7..1a12107b7 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -779,7 +779,7 @@ cdef class NeuralDependencyParser(NeuralParser):
     TransitionSystem = ArcEager
 
     def init_multitask_objectives(self, gold_tuples, pipeline, **cfg):
-        for target in ['dep', 'ent']:
+        for target in []:
             labeller = NeuralLabeller(self.vocab, target=target)
             tok2vec = self.model[0]
             labeller.begin_training(gold_tuples, pipeline=pipeline, tok2vec=tok2vec)

From 1a37a2c0a0b41b5b719a6d62b11d85c0c139baed Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 27 Sep 2017 11:48:07 -0500
Subject: [PATCH 138/213] Update training defaults

---
 spacy/_ml.py               | 2 +-
 spacy/syntax/nn_parser.pyx | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 2e95aa55b..22a501f0f 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -505,7 +505,7 @@ def getitem(i):
     return layerize(getitem_fwd)
 
 def build_tagger_model(nr_class, **cfg):
-    embed_size = util.env_opt('embed_size', 4000)
+    embed_size = util.env_opt('embed_size', 1000)
     if 'token_vector_width' in cfg:
         token_vector_width = cfg['token_vector_width']
     else:
diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 99099cad8..830aac551 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -240,12 +240,12 @@ cdef class Parser:
     Base class of the DependencyParser and EntityRecognizer.
     """
     @classmethod
-    def Model(cls, nr_class, token_vector_width=128, hidden_width=300, depth=1, **cfg):
+    def Model(cls, nr_class, token_vector_width=128, hidden_width=200, depth=1, **cfg):
         depth = util.env_opt('parser_hidden_depth', depth)
         token_vector_width = util.env_opt('token_vector_width', token_vector_width)
         hidden_width = util.env_opt('hidden_width', hidden_width)
         parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2)
-        embed_size = util.env_opt('embed_size', 4000)
+        embed_size = util.env_opt('embed_size', 1000)
         tok2vec = Tok2Vec(token_vector_width, embed_size,
                           pretrained_dims=cfg.get('pretrained_dims', 0))
         tok2vec = chain(tok2vec, flatten)

From dcb86bdc4363a115974a4d0748a2d899094a14eb Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 27 Sep 2017 11:48:19 -0500
Subject: [PATCH 139/213] Default batch size to 32

---
 spacy/cli/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 4ce86d31e..53426febe 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -82,7 +82,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                                   util.env_opt('dropout_to', 0.2),
                                   util.env_opt('dropout_decay', 0.0))
     batch_sizes = util.compounding(util.env_opt('batch_from', 1),
-                                   util.env_opt('batch_to', 64),
+                                   util.env_opt('batch_to', 32),
                                    util.env_opt('batch_compound', 1.001))
     corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
     n_train_words = corpus.count_train()

From 542ebfa49864b5a46af1eed22f535747061f4bc5 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 27 Sep 2017 18:54:37 -0500
Subject: [PATCH 140/213] Improve defaults

---
 spacy/cli/train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 53426febe..dfa755b8e 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -44,7 +44,7 @@ numpy.random.seed(0)
     version=("Model version", "option", "V", str),
     meta_path=("Optional path to meta.json. All relevant properties will be overwritten.", "option", "m", Path)
 )
-def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
+def train(cmd, lang, output_dir, train_data, dev_data, n_iter=10, n_sents=0,
           use_gpu=-1, vectors=None, no_tagger=False, no_parser=False, no_entities=False,
           gold_preproc=False, version="0.0.0", meta_path=None):
     """
@@ -82,7 +82,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
                                   util.env_opt('dropout_to', 0.2),
                                   util.env_opt('dropout_decay', 0.0))
     batch_sizes = util.compounding(util.env_opt('batch_from', 1),
-                                   util.env_opt('batch_to', 32),
+                                   util.env_opt('batch_to', 16),
                                    util.env_opt('batch_compound', 1.001))
     corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
     n_train_words = corpus.count_train()

From ac8481a7b086880225229e6d69d8a3335accd3ba Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 28 Sep 2017 08:05:31 -0500
Subject: [PATCH 141/213] Print NER loss

---
 spacy/cli/train.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index dfa755b8e..d973effb6 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -94,7 +94,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=10, n_sents=0,
     optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
     nlp._optimizer = None
 
-    print("Itn.\tLoss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
+    print("Itn.\tP.Loss\tN.Loss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
     try:
         train_docs = corpus.train_docs(nlp, projectivize=True, noise_level=0.0,
                                        gold_preproc=gold_preproc, max_length=0)
@@ -158,12 +158,14 @@ def print_progress(itn, losses, dev_scores, wps=0.0):
                 'ents_p', 'ents_r', 'ents_f', 'wps']:
         scores[col] = 0.0
     scores['dep_loss'] = losses.get('parser', 0.0)
+    scores['ner_loss'] = losses.get('ner', 0.0)
     scores['tag_loss'] = losses.get('tagger', 0.0)
     scores.update(dev_scores)
     scores['wps'] = wps
     tpl = '\t'.join((
         '{:d}',
         '{dep_loss:.3f}',
+        '{ner_loss:.3f}',
         '{uas:.3f}',
         '{ents_p:.3f}',
         '{ents_r:.3f}',

From f6330d69e65b779368827a3d99128ab10880f0d2 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 28 Sep 2017 08:07:41 -0500
Subject: [PATCH 142/213] Default embed size to 7000

---
 spacy/_ml.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 22a501f0f..62fc7543f 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -505,7 +505,7 @@ def getitem(i):
     return layerize(getitem_fwd)
 
 def build_tagger_model(nr_class, **cfg):
-    embed_size = util.env_opt('embed_size', 1000)
+    embed_size = util.env_opt('embed_size', 7000)
     if 'token_vector_width' in cfg:
         token_vector_width = cfg['token_vector_width']
     else:

From 158e177cae44f72382ea83162b6830cf8189abea Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 28 Sep 2017 08:25:23 -0500
Subject: [PATCH 143/213] Fix default embed size

---
 spacy/syntax/nn_parser.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 830aac551..7b45db0d8 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -245,7 +245,7 @@ cdef class Parser:
         token_vector_width = util.env_opt('token_vector_width', token_vector_width)
         hidden_width = util.env_opt('hidden_width', hidden_width)
         parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2)
-        embed_size = util.env_opt('embed_size', 1000)
+        embed_size = util.env_opt('embed_size', 7000)
         tok2vec = Tok2Vec(token_vector_width, embed_size,
                           pretrained_dims=cfg.get('pretrained_dims', 0))
         tok2vec = chain(tok2vec, flatten)

From cdb2d83e168b8602bcaa98b1a8f2842908eaad49 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Thu, 28 Sep 2017 18:47:13 -0500
Subject: [PATCH 144/213] Pass dropout in parser

---
 spacy/syntax/nn_parser.pyx | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 7b45db0d8..1efdc4474 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -533,7 +533,7 @@ cdef class Parser:
 
         states, golds, max_steps = self._init_gold_batch(docs, golds)
         (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
-                                                                            0.0)
+                                                                            drop)
         todo = [(s, g) for (s, g) in zip(states, golds)
                 if not s.is_final() and g is not None]
         if not todo:
@@ -598,7 +598,7 @@ cdef class Parser:
             self.moves.preprocess_gold(gold)
 
         cuda_stream = get_cuda_stream()
-        (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream, 0.0)
+        (tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream, drop)
 
         states_d_scores, backprops = _beam_utils.update_beam(self.moves, self.nr_feature, 500,
                                         states, golds,
@@ -685,7 +685,7 @@ cdef class Parser:
         tok2vec, lower, upper = self.model
         tokvecs, bp_tokvecs = tok2vec.begin_update(docs, drop=dropout)
         state2vec = precompute_hiddens(len(docs), tokvecs,
-                        lower, stream, drop=dropout)
+                                       lower, stream, drop=0.0)
         return (tokvecs, bp_tokvecs), state2vec, upper
 
     nr_feature = 8

From 7d77dc490fb2f8cf5232690f3775e77cdadc59ee Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Fri, 29 Sep 2017 20:52:28 +0200
Subject: [PATCH 145/213] Always compare lowercase package names

Otherwise, is_package will return False if model name contains
uppercase characters. See this issue:
https://support.prodi.gy/t/saving-a-trained-ner-model-as-a-loadable-modu
le/46/6
---
 spacy/util.py                |   4 +-
 website/usage/spacy-101.jade | 300 +++++++++++++++++++++++++++++++++++
 2 files changed, 303 insertions(+), 1 deletion(-)
 create mode 100644 website/usage/spacy-101.jade

diff --git a/spacy/util.py b/spacy/util.py
index 429d9bae5..911970831 100644
--- a/spacy/util.py
+++ b/spacy/util.py
@@ -181,9 +181,10 @@ def is_package(name):
     name (unicode): Name of package.
     RETURNS (bool): True if installed package, False if not.
     """
+    name = name.lower()  # compare package name against lowercase name
     packages = pkg_resources.working_set.by_key.keys()
     for package in packages:
-        if package.replace('-', '_') == name:
+        if package.lower().replace('-', '_') == name:
             return True
     return False
 
@@ -194,6 +195,7 @@ def get_package_path(name):
     name (unicode): Package name.
     RETURNS (Path): Path to installed package.
     """
+    name = name.lower()  # use lowercase version to be safe
     # Here we're importing the module just to find it. This is worryingly
     # indirect, but it's otherwise very difficult to find the package.
     pkg = importlib.import_module(name)
diff --git a/website/usage/spacy-101.jade b/website/usage/spacy-101.jade
new file mode 100644
index 000000000..a57137674
--- /dev/null
+++ b/website/usage/spacy-101.jade
@@ -0,0 +1,300 @@
+//- 💫 DOCS > USAGE > SPACY 101
+
+include ../_includes/_mixins
+
+p
+    |  Whether you're new to spaCy, or just want to brush up on some
+    |  NLP basics and implementation details – this page should have you covered.
+    |  Each section will explain one of spaCy's features in simple terms and
+    |  with examples or illustrations. Some sections will also reappear across
+    |  the usage guides as a quick introduction.
+
++aside("Help us improve the docs")
+    |  Did you spot a mistake or come across explanations that
+    |  are unclear? We always appreciate improvement
+    |  #[+a(gh("spaCy") + "/issues") suggestions] or
+    |  #[+a(gh("spaCy") + "/pulls") pull requests]. You can find a "Suggest
+    |  edits" link at the bottom of each page that points you to the source.
+
++h(2, "whats-spacy") What's spaCy?
+
++grid.o-no-block
+    +grid-col("half")
+        p
+            |  spaCy is a #[strong free, open-source library] for advanced
+            |  #[strong Natural Language Processing] (NLP) in Python.
+
+        p
+            |  If you're working with a lot of text, you'll eventually want to
+            |  know more about it. For example, what's it about? What do the
+            |  words mean in context? Who is doing what to whom? What companies
+            |  and products are mentioned? Which texts are similar to each other?
+
+        p
+            |  spaCy is designed specifically for #[strong production use] and
+            |  helps you build applications that process and "understand"
+            |  large volumes of text. It can be used to build
+            |  #[strong information extraction] or
+            |  #[strong natural language understanding] systems, or to
+            |  pre-process text for #[strong deep learning].
+
+    +table-of-contents
+        +item #[+a("#features") Features]
+        +item #[+a("#annotations") Linguistic annotations]
+        +item #[+a("#annotations-token") Tokenization]
+        +item #[+a("#annotations-pos-deps") POS tags and dependencies]
+        +item #[+a("#annotations-ner") Named entities]
+        +item #[+a("#vectors-similarity") Word vectors and similarity]
+        +item #[+a("#pipelines") Pipelines]
+        +item #[+a("#vocab") Vocab, hashes and lexemes]
+        +item #[+a("#serialization") Serialization]
+        +item #[+a("#training") Training]
+        +item #[+a("#language-data") Language data]
+        +item #[+a("#lightning-tour") Lightning tour]
+        +item #[+a("#architecture") Architecture]
+        +item #[+a("#community") Community & FAQ]
+
++h(3, "what-spacy-isnt") What spaCy isn't
+
++list
+    +item #[strong spaCy is not a platform or "an API"].
+        |  Unlike a platform, spaCy does not provide a software as a service, or
+        |  a web application. It's an open-source library designed to help you
+        |  build NLP applications, not a consumable service.
+    +item #[strong spaCy is not an out-of-the-box chat bot engine].
+        |  While spaCy can be used to power conversational applications, it's
+        |  not designed specifically for chat bots, and only provides the
+        |  underlying text processing capabilities.
+    +item #[strong spaCy is not research software].
+        |  It's built on the latest research, but it's designed to get
+        |  things done. This leads to fairly different design decisions than
+        |  #[+a("https://github./nltk/nltk") NLTK]
+        |  or #[+a("https://stanfordnlp.github.io/CoreNLP/") CoreNLP], which were
+        |  created as platforms for teaching and research. The main difference
+        |  is that spaCy is integrated and opinionated. spaCy tries to avoid asking
+        |  the user to choose between multiple algorithms that deliver equivalent
+        |  functionality. Keeping the menu small lets spaCy deliver generally better
+        |  performance and developer experience.
+    +item #[strong spaCy is not a company].
+        |  It's an open-source library. Our company publishing spaCy and other
+        |  software is called #[+a(COMPANY_URL, true) Explosion AI].
+
++section("features")
+    +h(2, "features") Features
+
+    p
+        |  In the documentation, you'll come across mentions of spaCy's
+        |  features and capabilities. Some of them refer to linguistic concepts,
+        |  while others are related to more general machine learning
+        |  functionality.
+
+    +aside
+        |  If one of spaCy's functionalities #[strong needs a model], it means
+        |  that you need to have one of the available
+        |  #[+a("/models") statistical models] installed. Models are used
+        |  to #[strong predict] linguistic annotations – for example, if a word
+        |  is a verb or a noun.
+
+    +table(["Name", "Description", "Needs model"])
+        +row
+            +cell #[strong Tokenization]
+            +cell Segmenting text into words, punctuations marks etc.
+            +cell #[+procon("con")]
+
+        +row
+            +cell #[strong Part-of-speech] (POS) #[strong Tagging]
+            +cell Assigning word types to tokens, like verb or noun.
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Dependency Parsing]
+            +cell
+                |  Assigning syntactic dependency labels, describing the
+                |  relations between individual tokens, like subject or object.
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Lemmatization]
+            +cell
+                |  Assigning the base forms of words. For example, the lemma of
+                |  "was" is "be", and the lemma of "rats" is "rat".
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Sentence Boundary Detection] (SBD)
+            +cell Finding and segmenting individual sentences.
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Named Entity Recongition] (NER)
+            +cell
+                |  Labelling named "real-world" objects, like persons, companies
+                |  or locations.
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Similarity]
+            +cell
+                |  Comparing words, text spans and documents and how similar
+                |  they are to each other.
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Text Classification]
+            +cell
+                |  Assigning categories or labels to a whole document, or parts
+                |  of a document.
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Rule-based Matching]
+            +cell
+                |  Finding sequences of tokens based on their texts and
+                |  linguistic annotations, similar to regular expressions.
+            +cell #[+procon("con")]
+
+        +row
+            +cell #[strong Training]
+            +cell Updating and improving a statistical model's predictions.
+            +cell #[+procon("neutral")]
+
+        +row
+            +cell #[strong Serialization]
+            +cell Saving objects to files or byte strings.
+            +cell #[+procon("neutral")]
+
+    +h(2, "annotations") Linguistic annotations
+
+    p
+        |  spaCy provides a variety of linguistic annotations to give you
+        |  #[strong insights into a text&apos;s grammatical structure]. This
+        |  includes the word types, like the parts of speech, and how the words
+        |  are related to each other. For example, if you're analysing text, it
+        |  makes a huge difference whether a noun is the subject of a sentence,
+        |  or the object – or whether "google" is used as a verb, or refers to
+        |  the website or company in a specific context.
+
+    p
+        |  Once you've downloaded and installed a #[+a("/usage/models") model],
+        |  you can load it via #[+api("spacy#load") #[code spacy.load()]]. This will
+        |  return a #[code Language] object contaning all components and data needed
+        |  to process text. We usually call it #[code nlp]. Calling the #[code nlp]
+        |  object on a string of text will return a processed #[code Doc]:
+
+    +code.
+        import spacy
+
+        nlp = spacy.load('en')
+        doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
+
+    p
+        |  Even though a #[code Doc] is processed – e.g. split into individual words
+        |  and annotated – it still holds #[strong all information of the original text],
+        |  like whitespace characters. You can always get the offset of a token into the
+        |  original string, or reconstruct the original by joining the tokens and their
+        |  trailing whitespace. This way, you'll never lose any information
+        |  when processing text with spaCy.
+
+    +h(3, "annotations-token") Tokenization
+
+    include _spacy-101/_tokenization
+
+    +infobox
+        |  To learn more about how spaCy's tokenization rules work in detail,
+        |  how to #[strong customise and replace] the default tokenizer and how to
+        |  #[strong add language-specific data], see the usage guides on
+        |  #[+a("/usage/adding-languages") adding languages] and
+        |  #[+a("/usage/linguistic-features#tokenization") customising the tokenizer].
+
+    +h(3, "annotations-pos-deps") Part-of-speech tags and dependencies
+        +tag-model("dependency parse")
+
+    include _spacy-101/_pos-deps
+
+    +infobox
+        |  To learn more about #[strong part-of-speech tagging] and rule-based
+        |  morphology, and how to #[strong navigate and use the parse tree]
+        |  effectively, see the usage guides on
+        |  #[+a("/usage/linguistic-features#pos-tagging") part-of-speech tagging] and
+        |  #[+a("/usage/linguistic-features#dependency-parse") using the dependency parse].
+
+    +h(3, "annotations-ner") Named Entities
+        +tag-model("named entities")
+
+    include _spacy-101/_named-entities
+
+    +infobox
+        |  To learn more about entity recognition in spaCy, how to
+        |  #[strong add your own entities] to a document and how to
+        |  #[strong train and update] the entity predictions of a model, see the
+        |  usage guides on
+        |  #[+a("/usage/linguistic-features#named-entities") named entity recognition] and
+        |  #[+a("/usage/training#ner") training the named entity recognizer].
+
+    +h(2, "vectors-similarity") Word vectors and similarity
+        +tag-model("vectors")
+
+    include _spacy-101/_similarity
+
+    include _spacy-101/_word-vectors
+
+    +infobox
+        |  To learn more about word vectors, how to #[strong customise them] and
+        |  how to load #[strong your own vectors] into spaCy, see the usage
+        |  guide on
+        |  #[+a("/usage/word-vectors-similarities") using word vectors and semantic similarities].
+
+    +h(2, "pipelines") Pipelines
+
+    include _spacy-101/_pipelines
+
+    +infobox
+        |  To learn more about #[strong how processing pipelines work] in detail,
+        |  how to enable and disable their components, and how to
+        |  #[strong create your own], see the usage guide on
+        |  #[+a("/usage/processing-pipelines") language processing pipelines].
+
+    +h(2, "vocab") Vocab, hashes and lexemes
+
+    include _spacy-101/_vocab
+
+    +h(2, "serialization") Serialization
+
+    include _spacy-101/_serialization
+
+    +infobox
+        |  To learn more about how to #[strong save and load your own models],
+        |  see the usage guide on
+        |  #[+a("/usage/training#saving-loading") saving and loading].
+
+    +h(2, "training") Training
+
+    include _spacy-101/_training
+
+    +infobox
+        |  To learn more about #[strong training and updating] models, how to create
+        |  training data and how to improve spaCy's named entity recognition models,
+        |  see the usage guides on #[+a("/usage/training") training].
+
+    +h(2, "language-data") Language data
+
+    include _spacy-101/_language-data
+
+    +infobox
+        |  To learn more about the individual components of the language data and
+        |  how to #[strong add a new language] to spaCy in preparation for training
+        |  a language model, see the usage guide on
+        |  #[+a("/usage/adding-languages") adding languages].
+
+
++section("lightning-tour")
+    +h(2, "lightning-tour") Lightning tour
+    include _spacy-101/_lightning-tour
+
++section("architecture")
+    +h(2, "architecture") Architecture
+    include _spacy-101/_architecture
+
++section("community-faq")
+    +h(2, "community") Community & FAQ
+    include _spacy-101/_community-faq

From fd1a9225d80a667bfd88c5d21bdaeaf156003cf2 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Fri, 29 Sep 2017 20:52:56 +0200
Subject: [PATCH 146/213] Handle conversion of pipeline components correctly

Allow both comma and comma + whitespace as separators
---
 spacy/cli/package.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/spacy/cli/package.py b/spacy/cli/package.py
index 7019819a7..5ffc493c3 100644
--- a/spacy/cli/package.py
+++ b/spacy/cli/package.py
@@ -105,8 +105,11 @@ def generate_pipeline():
            "parser, ner. For more information, see the docs on processing pipelines.",
            title="Enter your model's pipeline components")
     pipeline = util.get_raw_input("Pipeline components", True)
-    replace = {'True': True, 'False': False}
-    return replace[pipeline] if pipeline in replace else pipeline.split(', ')
+    subs = {'True': True, 'False': False}
+    if pipeline in subs:
+        return subs[pipeline]
+    else:
+        return [p.strip() for p in pipeline.split(',')]
 
 
 def validate_meta(meta, keys):

From 153c2589d40a7dd127864b0e68efe20182d0941e Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Fri, 29 Sep 2017 20:53:36 +0200
Subject: [PATCH 147/213] Revert "Always compare lowercase package names"

This reverts commit 7d77dc490fb2f8cf5232690f3775e77cdadc59ee.
---
 spacy/util.py                |   4 +-
 website/usage/spacy-101.jade | 300 -----------------------------------
 2 files changed, 1 insertion(+), 303 deletions(-)
 delete mode 100644 website/usage/spacy-101.jade

diff --git a/spacy/util.py b/spacy/util.py
index 911970831..429d9bae5 100644
--- a/spacy/util.py
+++ b/spacy/util.py
@@ -181,10 +181,9 @@ def is_package(name):
     name (unicode): Name of package.
     RETURNS (bool): True if installed package, False if not.
     """
-    name = name.lower()  # compare package name against lowercase name
     packages = pkg_resources.working_set.by_key.keys()
     for package in packages:
-        if package.lower().replace('-', '_') == name:
+        if package.replace('-', '_') == name:
             return True
     return False
 
@@ -195,7 +194,6 @@ def get_package_path(name):
     name (unicode): Package name.
     RETURNS (Path): Path to installed package.
     """
-    name = name.lower()  # use lowercase version to be safe
     # Here we're importing the module just to find it. This is worryingly
     # indirect, but it's otherwise very difficult to find the package.
     pkg = importlib.import_module(name)
diff --git a/website/usage/spacy-101.jade b/website/usage/spacy-101.jade
deleted file mode 100644
index a57137674..000000000
--- a/website/usage/spacy-101.jade
+++ /dev/null
@@ -1,300 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101
-
-include ../_includes/_mixins
-
-p
-    |  Whether you're new to spaCy, or just want to brush up on some
-    |  NLP basics and implementation details – this page should have you covered.
-    |  Each section will explain one of spaCy's features in simple terms and
-    |  with examples or illustrations. Some sections will also reappear across
-    |  the usage guides as a quick introduction.
-
-+aside("Help us improve the docs")
-    |  Did you spot a mistake or come across explanations that
-    |  are unclear? We always appreciate improvement
-    |  #[+a(gh("spaCy") + "/issues") suggestions] or
-    |  #[+a(gh("spaCy") + "/pulls") pull requests]. You can find a "Suggest
-    |  edits" link at the bottom of each page that points you to the source.
-
-+h(2, "whats-spacy") What's spaCy?
-
-+grid.o-no-block
-    +grid-col("half")
-        p
-            |  spaCy is a #[strong free, open-source library] for advanced
-            |  #[strong Natural Language Processing] (NLP) in Python.
-
-        p
-            |  If you're working with a lot of text, you'll eventually want to
-            |  know more about it. For example, what's it about? What do the
-            |  words mean in context? Who is doing what to whom? What companies
-            |  and products are mentioned? Which texts are similar to each other?
-
-        p
-            |  spaCy is designed specifically for #[strong production use] and
-            |  helps you build applications that process and "understand"
-            |  large volumes of text. It can be used to build
-            |  #[strong information extraction] or
-            |  #[strong natural language understanding] systems, or to
-            |  pre-process text for #[strong deep learning].
-
-    +table-of-contents
-        +item #[+a("#features") Features]
-        +item #[+a("#annotations") Linguistic annotations]
-        +item #[+a("#annotations-token") Tokenization]
-        +item #[+a("#annotations-pos-deps") POS tags and dependencies]
-        +item #[+a("#annotations-ner") Named entities]
-        +item #[+a("#vectors-similarity") Word vectors and similarity]
-        +item #[+a("#pipelines") Pipelines]
-        +item #[+a("#vocab") Vocab, hashes and lexemes]
-        +item #[+a("#serialization") Serialization]
-        +item #[+a("#training") Training]
-        +item #[+a("#language-data") Language data]
-        +item #[+a("#lightning-tour") Lightning tour]
-        +item #[+a("#architecture") Architecture]
-        +item #[+a("#community") Community & FAQ]
-
-+h(3, "what-spacy-isnt") What spaCy isn't
-
-+list
-    +item #[strong spaCy is not a platform or "an API"].
-        |  Unlike a platform, spaCy does not provide a software as a service, or
-        |  a web application. It's an open-source library designed to help you
-        |  build NLP applications, not a consumable service.
-    +item #[strong spaCy is not an out-of-the-box chat bot engine].
-        |  While spaCy can be used to power conversational applications, it's
-        |  not designed specifically for chat bots, and only provides the
-        |  underlying text processing capabilities.
-    +item #[strong spaCy is not research software].
-        |  It's built on the latest research, but it's designed to get
-        |  things done. This leads to fairly different design decisions than
-        |  #[+a("https://github./nltk/nltk") NLTK]
-        |  or #[+a("https://stanfordnlp.github.io/CoreNLP/") CoreNLP], which were
-        |  created as platforms for teaching and research. The main difference
-        |  is that spaCy is integrated and opinionated. spaCy tries to avoid asking
-        |  the user to choose between multiple algorithms that deliver equivalent
-        |  functionality. Keeping the menu small lets spaCy deliver generally better
-        |  performance and developer experience.
-    +item #[strong spaCy is not a company].
-        |  It's an open-source library. Our company publishing spaCy and other
-        |  software is called #[+a(COMPANY_URL, true) Explosion AI].
-
-+section("features")
-    +h(2, "features") Features
-
-    p
-        |  In the documentation, you'll come across mentions of spaCy's
-        |  features and capabilities. Some of them refer to linguistic concepts,
-        |  while others are related to more general machine learning
-        |  functionality.
-
-    +aside
-        |  If one of spaCy's functionalities #[strong needs a model], it means
-        |  that you need to have one of the available
-        |  #[+a("/models") statistical models] installed. Models are used
-        |  to #[strong predict] linguistic annotations – for example, if a word
-        |  is a verb or a noun.
-
-    +table(["Name", "Description", "Needs model"])
-        +row
-            +cell #[strong Tokenization]
-            +cell Segmenting text into words, punctuations marks etc.
-            +cell #[+procon("con")]
-
-        +row
-            +cell #[strong Part-of-speech] (POS) #[strong Tagging]
-            +cell Assigning word types to tokens, like verb or noun.
-            +cell #[+procon("pro")]
-
-        +row
-            +cell #[strong Dependency Parsing]
-            +cell
-                |  Assigning syntactic dependency labels, describing the
-                |  relations between individual tokens, like subject or object.
-            +cell #[+procon("pro")]
-
-        +row
-            +cell #[strong Lemmatization]
-            +cell
-                |  Assigning the base forms of words. For example, the lemma of
-                |  "was" is "be", and the lemma of "rats" is "rat".
-            +cell #[+procon("pro")]
-
-        +row
-            +cell #[strong Sentence Boundary Detection] (SBD)
-            +cell Finding and segmenting individual sentences.
-            +cell #[+procon("pro")]
-
-        +row
-            +cell #[strong Named Entity Recongition] (NER)
-            +cell
-                |  Labelling named "real-world" objects, like persons, companies
-                |  or locations.
-            +cell #[+procon("pro")]
-
-        +row
-            +cell #[strong Similarity]
-            +cell
-                |  Comparing words, text spans and documents and how similar
-                |  they are to each other.
-            +cell #[+procon("pro")]
-
-        +row
-            +cell #[strong Text Classification]
-            +cell
-                |  Assigning categories or labels to a whole document, or parts
-                |  of a document.
-            +cell #[+procon("pro")]
-
-        +row
-            +cell #[strong Rule-based Matching]
-            +cell
-                |  Finding sequences of tokens based on their texts and
-                |  linguistic annotations, similar to regular expressions.
-            +cell #[+procon("con")]
-
-        +row
-            +cell #[strong Training]
-            +cell Updating and improving a statistical model's predictions.
-            +cell #[+procon("neutral")]
-
-        +row
-            +cell #[strong Serialization]
-            +cell Saving objects to files or byte strings.
-            +cell #[+procon("neutral")]
-
-    +h(2, "annotations") Linguistic annotations
-
-    p
-        |  spaCy provides a variety of linguistic annotations to give you
-        |  #[strong insights into a text&apos;s grammatical structure]. This
-        |  includes the word types, like the parts of speech, and how the words
-        |  are related to each other. For example, if you're analysing text, it
-        |  makes a huge difference whether a noun is the subject of a sentence,
-        |  or the object – or whether "google" is used as a verb, or refers to
-        |  the website or company in a specific context.
-
-    p
-        |  Once you've downloaded and installed a #[+a("/usage/models") model],
-        |  you can load it via #[+api("spacy#load") #[code spacy.load()]]. This will
-        |  return a #[code Language] object contaning all components and data needed
-        |  to process text. We usually call it #[code nlp]. Calling the #[code nlp]
-        |  object on a string of text will return a processed #[code Doc]:
-
-    +code.
-        import spacy
-
-        nlp = spacy.load('en')
-        doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
-
-    p
-        |  Even though a #[code Doc] is processed – e.g. split into individual words
-        |  and annotated – it still holds #[strong all information of the original text],
-        |  like whitespace characters. You can always get the offset of a token into the
-        |  original string, or reconstruct the original by joining the tokens and their
-        |  trailing whitespace. This way, you'll never lose any information
-        |  when processing text with spaCy.
-
-    +h(3, "annotations-token") Tokenization
-
-    include _spacy-101/_tokenization
-
-    +infobox
-        |  To learn more about how spaCy's tokenization rules work in detail,
-        |  how to #[strong customise and replace] the default tokenizer and how to
-        |  #[strong add language-specific data], see the usage guides on
-        |  #[+a("/usage/adding-languages") adding languages] and
-        |  #[+a("/usage/linguistic-features#tokenization") customising the tokenizer].
-
-    +h(3, "annotations-pos-deps") Part-of-speech tags and dependencies
-        +tag-model("dependency parse")
-
-    include _spacy-101/_pos-deps
-
-    +infobox
-        |  To learn more about #[strong part-of-speech tagging] and rule-based
-        |  morphology, and how to #[strong navigate and use the parse tree]
-        |  effectively, see the usage guides on
-        |  #[+a("/usage/linguistic-features#pos-tagging") part-of-speech tagging] and
-        |  #[+a("/usage/linguistic-features#dependency-parse") using the dependency parse].
-
-    +h(3, "annotations-ner") Named Entities
-        +tag-model("named entities")
-
-    include _spacy-101/_named-entities
-
-    +infobox
-        |  To learn more about entity recognition in spaCy, how to
-        |  #[strong add your own entities] to a document and how to
-        |  #[strong train and update] the entity predictions of a model, see the
-        |  usage guides on
-        |  #[+a("/usage/linguistic-features#named-entities") named entity recognition] and
-        |  #[+a("/usage/training#ner") training the named entity recognizer].
-
-    +h(2, "vectors-similarity") Word vectors and similarity
-        +tag-model("vectors")
-
-    include _spacy-101/_similarity
-
-    include _spacy-101/_word-vectors
-
-    +infobox
-        |  To learn more about word vectors, how to #[strong customise them] and
-        |  how to load #[strong your own vectors] into spaCy, see the usage
-        |  guide on
-        |  #[+a("/usage/word-vectors-similarities") using word vectors and semantic similarities].
-
-    +h(2, "pipelines") Pipelines
-
-    include _spacy-101/_pipelines
-
-    +infobox
-        |  To learn more about #[strong how processing pipelines work] in detail,
-        |  how to enable and disable their components, and how to
-        |  #[strong create your own], see the usage guide on
-        |  #[+a("/usage/processing-pipelines") language processing pipelines].
-
-    +h(2, "vocab") Vocab, hashes and lexemes
-
-    include _spacy-101/_vocab
-
-    +h(2, "serialization") Serialization
-
-    include _spacy-101/_serialization
-
-    +infobox
-        |  To learn more about how to #[strong save and load your own models],
-        |  see the usage guide on
-        |  #[+a("/usage/training#saving-loading") saving and loading].
-
-    +h(2, "training") Training
-
-    include _spacy-101/_training
-
-    +infobox
-        |  To learn more about #[strong training and updating] models, how to create
-        |  training data and how to improve spaCy's named entity recognition models,
-        |  see the usage guides on #[+a("/usage/training") training].
-
-    +h(2, "language-data") Language data
-
-    include _spacy-101/_language-data
-
-    +infobox
-        |  To learn more about the individual components of the language data and
-        |  how to #[strong add a new language] to spaCy in preparation for training
-        |  a language model, see the usage guide on
-        |  #[+a("/usage/adding-languages") adding languages].
-
-
-+section("lightning-tour")
-    +h(2, "lightning-tour") Lightning tour
-    include _spacy-101/_lightning-tour
-
-+section("architecture")
-    +h(2, "architecture") Architecture
-    include _spacy-101/_architecture
-
-+section("community-faq")
-    +h(2, "community") Community & FAQ
-    include _spacy-101/_community-faq

From 8dbe49ecb8cfe2edd932a10d418e4be5466700ba Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Fri, 29 Sep 2017 20:55:17 +0200
Subject: [PATCH 148/213] Always compare lowercase package names

Otherwise, is_package will return False if model name contains
uppercase characters. See this issue:
https://support.prodi.gy/t/saving-a-trained-ner-model-as-a-loadable-modu
le/46/6
---
 spacy/util.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/spacy/util.py b/spacy/util.py
index 429d9bae5..911970831 100644
--- a/spacy/util.py
+++ b/spacy/util.py
@@ -181,9 +181,10 @@ def is_package(name):
     name (unicode): Name of package.
     RETURNS (bool): True if installed package, False if not.
     """
+    name = name.lower()  # compare package name against lowercase name
     packages = pkg_resources.working_set.by_key.keys()
     for package in packages:
-        if package.replace('-', '_') == name:
+        if package.lower().replace('-', '_') == name:
             return True
     return False
 
@@ -194,6 +195,7 @@ def get_package_path(name):
     name (unicode): Package name.
     RETURNS (Path): Path to installed package.
     """
+    name = name.lower()  # use lowercase version to be safe
     # Here we're importing the module just to find it. This is worryingly
     # indirect, but it's otherwise very difficult to find the package.
     pkg = importlib.import_module(name)

From 69c7c642c2c3a9c51d0b7c1c1e5e67848f9b7953 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 1 Oct 2017 14:04:32 -0500
Subject: [PATCH 149/213] Add spacy evaluate

---
 spacy/__main__.py     |  3 +-
 spacy/cli/__init__.py |  1 +
 spacy/cli/evaluate.py | 93 +++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 96 insertions(+), 1 deletion(-)
 create mode 100644 spacy/cli/evaluate.py

diff --git a/spacy/__main__.py b/spacy/__main__.py
index d02242d68..0ec96e4a1 100644
--- a/spacy/__main__.py
+++ b/spacy/__main__.py
@@ -7,7 +7,7 @@ if __name__ == '__main__':
     import plac
     import sys
     from spacy.cli import download, link, info, package, train, convert, model
-    from spacy.cli import profile
+    from spacy.cli import profile, evaluate
     from spacy.util import prints
 
     commands = {
@@ -15,6 +15,7 @@ if __name__ == '__main__':
         'link': link,
         'info': info,
         'train': train,
+        'evaluate': evaluate,
         'convert': convert,
         'package': package,
         'model': model,
diff --git a/spacy/cli/__init__.py b/spacy/cli/__init__.py
index e58c94642..ebe185f24 100644
--- a/spacy/cli/__init__.py
+++ b/spacy/cli/__init__.py
@@ -4,5 +4,6 @@ from .link import link
 from .package import package
 from .profile import profile
 from .train import train
+from .evaluate import evaluate
 from .convert import convert
 from .model import model
diff --git a/spacy/cli/evaluate.py b/spacy/cli/evaluate.py
new file mode 100644
index 000000000..209660529
--- /dev/null
+++ b/spacy/cli/evaluate.py
@@ -0,0 +1,93 @@
+# coding: utf8
+from __future__ import unicode_literals, division, print_function
+
+import plac
+import json
+from collections import defaultdict
+import cytoolz
+from pathlib import Path
+import dill
+import tqdm
+from thinc.neural._classes.model import Model
+from thinc.neural.optimizers import linear_decay
+from timeit import default_timer as timer
+import random
+import numpy.random
+
+from ..tokens.doc import Doc
+from ..scorer import Scorer
+from ..gold import GoldParse, merge_sents
+from ..gold import GoldCorpus, minibatch
+from ..util import prints
+from .. import util
+from .. import about
+from .. import displacy
+from ..compat import json_dumps
+
+random.seed(0)
+numpy.random.seed(0)
+
+
+@plac.annotations(
+    model=("Model name or path", "positional", None, str),
+    data_path=("Location of JSON-formatted evaluation data", "positional", None, str),
+    gold_preproc=("Use gold preprocessing", "flag", "G", bool),
+)
+def evaluate(cmd, model, data_path, gold_preproc=False):
+    """
+    Train a model. Expects data in spaCy's JSON format.
+    """
+    util.set_env_log(True)
+    data_path = util.ensure_path(data_path)
+    if not data_path.exists():
+        prints(data_path, title="Evaluation data not found", exits=1)
+    corpus = GoldCorpus(data_path, data_path)
+    nlp = util.load_model(model)
+    scorer = nlp.evaluate(list(corpus.dev_docs(nlp, gold_preproc=gold_preproc)))
+    print_results(scorer)
+
+
+def _render_parses(i, to_render):
+    to_render[0].user_data['title'] = "Batch %d" % i
+    with Path('/tmp/entities.html').open('w') as file_:
+        html = displacy.render(to_render[:5], style='ent', page=True)
+        file_.write(html)
+    with Path('/tmp/parses.html').open('w') as file_:
+        html = displacy.render(to_render[:5], style='dep', page=True)
+        file_.write(html)
+
+
+def print_progress(itn, losses, dev_scores, wps=0.0):
+    scores = {}
+    for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc',
+                'ents_p', 'ents_r', 'ents_f', 'wps']:
+        scores[col] = 0.0
+    scores['dep_loss'] = losses.get('parser', 0.0)
+    scores['ner_loss'] = losses.get('ner', 0.0)
+    scores['tag_loss'] = losses.get('tagger', 0.0)
+    scores.update(dev_scores)
+    scores['wps'] = wps
+    tpl = '\t'.join((
+        '{:d}',
+        '{dep_loss:.3f}',
+        '{ner_loss:.3f}',
+        '{uas:.3f}',
+        '{ents_p:.3f}',
+        '{ents_r:.3f}',
+        '{ents_f:.3f}',
+        '{tags_acc:.3f}',
+        '{token_acc:.3f}',
+        '{wps:.1f}'))
+    print(tpl.format(itn, **scores))
+
+
+def print_results(scorer):
+    results = {
+        'TOK': '%.2f' % scorer.token_acc,
+        'POS': '%.2f' % scorer.tags_acc,
+        'UAS': '%.2f' % scorer.uas,
+        'LAS': '%.2f' % scorer.las,
+        'NER P': '%.2f' % scorer.ents_p,
+        'NER R': '%.2f' % scorer.ents_r,
+        'NER F': '%.2f' % scorer.ents_f}
+    util.print_table(results, title="Results")

From 2cf0f4622fb5efc57c622b5d37c771e8042bed3d Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 1 Oct 2017 14:05:32 -0500
Subject: [PATCH 150/213] Fix loading of models with pre-trained vectors

---
 spacy/vocab.pyx | 1 +
 1 file changed, 1 insertion(+)

diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx
index 0a420849c..21c2a1165 100644
--- a/spacy/vocab.pyx
+++ b/spacy/vocab.pyx
@@ -324,6 +324,7 @@ cdef class Vocab:
             self.lexemes_from_bytes(file_.read())
         if self.vectors is not None:
             self.vectors.from_disk(path, exclude='strings.json')
+        link_vectors_to_models(self)
         return self
 
     def to_bytes(self, **exclude):

From b776f48e58586ee27d27cf8a1a8e316b8c73c42c Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Sun, 1 Oct 2017 21:58:45 +0200
Subject: [PATCH 151/213] Fix typo

---
 spacy/vocab.pyx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx
index 21c2a1165..a5f8bf6ad 100644
--- a/spacy/vocab.pyx
+++ b/spacy/vocab.pyx
@@ -262,7 +262,7 @@ cdef class Vocab:
         Words can be looked up by string or int ID.
 
         RETURNS:
-            A word vector. Size and shape determed by the
+            A word vector. Size and shape determined by the
             vocab.vectors instance. Usually, a numpy ndarray
             of shape (300,) and dtype float32.
 

From 97c409b6021c131f66ef4f90ff7ec47f99bea2f2 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 1 Oct 2017 22:10:33 +0200
Subject: [PATCH 152/213] Add docstrings for spacy.vectors

---
 spacy/vectors.pyx | 55 +++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 53 insertions(+), 2 deletions(-)

diff --git a/spacy/vectors.pyx b/spacy/vectors.pyx
index 346421153..dcb31da83 100644
--- a/spacy/vectors.pyx
+++ b/spacy/vectors.pyx
@@ -16,7 +16,16 @@ from .compat import basestring_
 
 
 cdef class Vectors:
-    '''Store, save and load word vectors.'''
+    '''Store, save and load word vectors.
+    
+    Vectors data is kept in the vectors.data attribute, which should be an
+    instance of numpy.ndarray (for CPU vectors)
+    or cupy.ndarray (for GPU vectors).
+
+    vectors.key2row is a dictionary mapping word hashes to rows
+    in the vectors.data table. The array `vectors.keys` keeps
+    the keys in order, such that keys[vectors.key2row[key]] == key.
+    '''
     cdef public object data
     cdef readonly StringStore strings
     cdef public object key2row
@@ -24,7 +33,36 @@ cdef class Vectors:
     cdef public int i
 
     def __init__(self, strings, data_or_width=0):
-        self.strings = StringStore()
+        '''Create a new vector store.
+        
+        To keep the vector table empty, pass data_or_width=0:
+
+            >>> empty_vectors = Vectors(StringStore())
+
+        To create the vector table, and add vectors one-by-one:
+
+            >>> my_vector_data = {
+            ...   'dog': numpy.random.uniform(-1, 1, (300,)),
+            ...   'cat': numpy.random.uniform(-1, 1, (300,)),
+            ...   'orange': numpy.random.uniform(-1, 1, (300,)),
+            ...  }
+            >>> strings = StringStore()
+            >>> for word in my_vector_data.keys():
+            ...   strings.add(word)
+            >>> vectors = Vectors(strings, 300)
+            >>> for word in strings:
+            ...     vectors[word] = preset_vectors[word]
+
+        To set the vector values directly on initialization:
+
+            >>> my_vector_table = numpy.zeros((3, 300), dtype='f')
+            >>> strings = StringStore()
+            >>> for key in my_vectors.keys():
+            ...   strings.add(key)
+            >>> for i, word in enumerate(strings):
+            ...   my_vectors_table[i] = my_vectors[word]
+            >>> vectors = Vectors(strings, my_vector_table)
+        '''
         if isinstance(data_or_width, int):
             self.data = data = numpy.zeros((len(strings), data_or_width),
                                            dtype='f')
@@ -39,6 +77,11 @@ cdef class Vectors:
         return (Vectors, (self.strings, self.data))
 
     def __getitem__(self, key):
+        '''Get a vector by key. If key is a string, it is hashed
+        to an integer ID using the vectors.strings table.
+
+        If the integer key is not found in the table, a KeyError is raised.
+        '''
         if isinstance(key, basestring):
             key = self.strings[key]
         i = self.key2row[key]
@@ -48,23 +91,30 @@ cdef class Vectors:
             return self.data[i]
 
     def __setitem__(self, key, vector):
+        '''Set a vector for the given key. If key is a string, it is hashed
+        to an integer ID using the vectors.strings table.
+        '''
         if isinstance(key, basestring):
             key = self.strings.add(key)
         i = self.key2row[key]
         self.data[i] = vector
 
     def __iter__(self):
+        '''Yield vectors from the table.'''
         yield from self.data
 
     def __len__(self):
+        '''Return the number of vectors that have been assigned.'''
         return self.i
 
     def __contains__(self, key):
+        '''Check whether a key has a vector entry in the table.'''
         if isinstance(key, basestring_):
             key = self.strings[key]
         return key in self.key2row
 
     def add(self, key, vector=None):
+        '''Add a key to the table, optionally setting a vector value as well.'''
         if isinstance(key, basestring_):
             key = self.strings.add(key)
         if key not in self.key2row:
@@ -82,6 +132,7 @@ cdef class Vectors:
         return i
 
     def items(self):
+        '''Iterate over (string key, vector) pairs, in order.'''
         for i, string in enumerate(self.strings):
             yield string, self.data[i]
 

From 38286b6f07ec829c956e4c2a881ba526d135b5ff Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 1 Oct 2017 23:40:02 +0200
Subject: [PATCH 153/213] Add example loadig Fast Text vectors

---
 examples/chainer_sentiment.py | 322 ----------------------------------
 examples/vectors_fast_text.py |  30 ++++
 2 files changed, 30 insertions(+), 322 deletions(-)
 delete mode 100644 examples/chainer_sentiment.py
 create mode 100644 examples/vectors_fast_text.py

diff --git a/examples/chainer_sentiment.py b/examples/chainer_sentiment.py
deleted file mode 100644
index 747ef508a..000000000
--- a/examples/chainer_sentiment.py
+++ /dev/null
@@ -1,322 +0,0 @@
-'''WIP --- Doesn't work well yet'''
-import plac
-import random
-import six
-
-import cProfile
-import pstats
-
-import pathlib
-import cPickle as pickle
-from itertools import izip
-
-import spacy
-
-import cytoolz
-import cupy as xp
-import cupy.cuda
-import chainer.cuda
-
-import chainer.links as L
-import chainer.functions as F
-from chainer import Chain, Variable, report
-import chainer.training
-import chainer.optimizers
-from chainer.training import extensions
-from chainer.iterators import SerialIterator
-from chainer.datasets import TupleDataset
-
-
-class SentimentAnalyser(object):
-    @classmethod
-    def load(cls, path, nlp, max_length=100):
-        raise NotImplementedError
-        #with (path / 'config.json').open() as file_:
-        #    model = model_from_json(file_.read())
-        #with (path / 'model').open('rb') as file_:
-        #    lstm_weights = pickle.load(file_)
-        #embeddings = get_embeddings(nlp.vocab)
-        #model.set_weights([embeddings] + lstm_weights)
-        #return cls(model, max_length=max_length)
-
-    def __init__(self, model, max_length=100):
-        self._model = model
-        self.max_length = max_length
-
-    def __call__(self, doc):
-        X = get_features([doc], self.max_length)
-        y = self._model.predict(X)
-        self.set_sentiment(doc, y)
-
-    def pipe(self, docs, batch_size=1000, n_threads=2):
-        for minibatch in cytoolz.partition_all(batch_size, docs):
-            minibatch = list(minibatch)
-            sentences = []
-            for doc in minibatch:
-                sentences.extend(doc.sents)
-            Xs = get_features(sentences, self.max_length)
-            ys = self._model.predict(Xs)
-            for sent, label in zip(sentences, ys):
-                sent.doc.sentiment += label - 0.5
-            for doc in minibatch:
-                yield doc
-
-    def set_sentiment(self, doc, y):
-        doc.sentiment = float(y[0])
-        # Sentiment has a native slot for a single float.
-        # For arbitrary data storage, there's:
-        # doc.user_data['my_data'] = y
-
-
-class Classifier(Chain):
-    def __init__(self, predictor):
-        super(Classifier, self).__init__(predictor=predictor)
-
-    def __call__(self, x, t):
-        y = self.predictor(x)
-        loss = F.softmax_cross_entropy(y, t)
-        accuracy = F.accuracy(y, t)
-        report({'loss': loss, 'accuracy': accuracy}, self)
-        return loss
-
-
-class SentimentModel(Chain):
-    def __init__(self, nlp, shape, **settings):
-        Chain.__init__(self,
-            embed=_Embed(shape['nr_vector'], shape['nr_dim'], shape['nr_hidden'],
-                set_vectors=lambda arr: set_vectors(arr, nlp.vocab)),
-            encode=_Encode(shape['nr_hidden'], shape['nr_hidden']),
-            attend=_Attend(shape['nr_hidden'], shape['nr_hidden']),
-            predict=_Predict(shape['nr_hidden'], shape['nr_class']))
-        self.to_gpu(0)
-
-    def __call__(self, sentence):
-        return self.predict(
-                  self.attend(
-                      self.encode(
-                          self.embed(sentence))))
-
-
-class _Embed(Chain):
-    def __init__(self, nr_vector, nr_dim, nr_out, set_vectors=None):
-        Chain.__init__(self,
-            embed=L.EmbedID(nr_vector, nr_dim, initialW=set_vectors),
-            project=L.Linear(None, nr_out, nobias=True))
-        self.embed.W.volatile = False
-
-    def __call__(self, sentence):
-        return [self.project(self.embed(ts)) for ts in F.transpose(sentence)]
-
-
-class _Encode(Chain):
-    def __init__(self, nr_in, nr_out):
-        Chain.__init__(self,
-            fwd=L.LSTM(nr_in, nr_out),
-            bwd=L.LSTM(nr_in, nr_out),
-            mix=L.Bilinear(nr_out, nr_out, nr_out))
-
-    def __call__(self, sentence):
-        self.fwd.reset_state()
-        fwds = map(self.fwd, sentence)
-        self.bwd.reset_state()
-        bwds = reversed(map(self.bwd, reversed(sentence)))
-        return [F.elu(self.mix(f, b)) for f, b in zip(fwds, bwds)]
-
-
-class _Attend(Chain):
-    def __init__(self, nr_in, nr_out):
-        Chain.__init__(self)
-
-    def __call__(self, sentence):
-        sent = sum(sentence)
-        return sent
-
-
-class _Predict(Chain):
-    def __init__(self, nr_in, nr_out):
-        Chain.__init__(self,
-            l1=L.Linear(nr_in, nr_in),
-            l2=L.Linear(nr_in, nr_out))
-
-    def __call__(self, vector):
-        vector = self.l1(vector)
-        vector = F.elu(vector)
-        vector = self.l2(vector)
-        return vector
-
-
-class SentenceDataset(TupleDataset):
-    def __init__(self, nlp, texts, labels, max_length):
-        self.max_length = max_length
-        sents, labels = self._get_labelled_sentences(
-            nlp.pipe(texts, batch_size=5000, n_threads=3),
-            labels)
-        TupleDataset.__init__(self,
-            get_features(sents, max_length),
-            labels)
-
-    def __getitem__(self, index):
-        batches = [dataset[index] for dataset in self._datasets]
-        if isinstance(index, slice):
-            length = len(batches[0])
-            returns = [tuple([batch[i] for batch in batches])
-                       for i in six.moves.range(length)]
-            return returns
-        else:
-            return tuple(batches)
-
-    def _get_labelled_sentences(self, docs, doc_labels):
-        labels = []
-        sentences = []
-        for doc, y in izip(docs, doc_labels):
-            for sent in doc.sents:
-                sentences.append(sent)
-                labels.append(y)
-        return sentences, xp.asarray(labels, dtype='i')
-
-
-class DocDataset(TupleDataset):
-    def __init__(self, nlp, texts, labels):
-        self.max_length = max_length
-        DatasetMixin.__init__(self,
-            get_features(
-                nlp.pipe(texts, batch_size=5000, n_threads=3), self.max_length),
-            labels)
-
-def read_data(data_dir, limit=0):
-    examples = []
-    for subdir, label in (('pos', 1), ('neg', 0)):
-        for filename in (data_dir / subdir).iterdir():
-            with filename.open() as file_:
-                text = file_.read()
-            examples.append((text, label))
-    random.shuffle(examples)
-    if limit >= 1:
-        examples = examples[:limit]
-    return zip(*examples) # Unzips into two lists
-
-
-def get_features(docs, max_length):
-    docs = list(docs)
-    Xs = xp.zeros((len(docs), max_length), dtype='i')
-    for i, doc in enumerate(docs):
-        j = 0
-        for token in doc:
-            if token.has_vector and not token.is_punct and not token.is_space:
-                Xs[i, j] = token.norm
-                j += 1
-                if j >= max_length:
-                    break
-    return Xs
-
-
-def set_vectors(vectors, vocab):
-    for lex in vocab:
-        if lex.has_vector and (lex.rank+1) < vectors.shape[0]:
-            lex.norm = lex.rank+1
-            vectors[lex.rank + 1] = lex.vector
-        else:
-            lex.norm = 0
-    return vectors
-
-
-def train(train_texts, train_labels, dev_texts, dev_labels,
-        lstm_shape, lstm_settings, lstm_optimizer, batch_size=100, nb_epoch=5,
-        by_sentence=True):
-    nlp = spacy.load('en', entity=False)
-    if 'nr_vector' not in lstm_shape:
-        lstm_shape['nr_vector'] = max(lex.rank+1 for lex in nlp.vocab if lex.has_vector)
-    if 'nr_dim' not in lstm_shape:
-        lstm_shape['nr_dim'] = nlp.vocab.vectors_length
-    print("Make model")
-    model = Classifier(SentimentModel(nlp, lstm_shape, **lstm_settings))
-    print("Parsing texts...")
-    if by_sentence:
-        train_data = SentenceDataset(nlp, train_texts, train_labels, lstm_shape['max_length'])
-        dev_data = SentenceDataset(nlp, dev_texts, dev_labels, lstm_shape['max_length'])
-    else:
-        train_data = DocDataset(nlp, train_texts, train_labels)
-        dev_data = DocDataset(nlp, dev_texts, dev_labels)
-    train_iter = SerialIterator(train_data, batch_size=batch_size,
-                                shuffle=True, repeat=True)
-    dev_iter = SerialIterator(dev_data, batch_size=batch_size,
-                              shuffle=False, repeat=False)
-    optimizer = chainer.optimizers.Adam()
-    optimizer.setup(model)
-    updater = chainer.training.StandardUpdater(train_iter, optimizer, device=0)
-    trainer = chainer.training.Trainer(updater, (1, 'epoch'), out='result')
-
-    trainer.extend(extensions.Evaluator(dev_iter, model, device=0))
-    trainer.extend(extensions.LogReport())
-    trainer.extend(extensions.PrintReport([
-        'epoch', 'main/accuracy', 'validation/main/accuracy']))
-    trainer.extend(extensions.ProgressBar())
-    
-    trainer.run()
-
-
-def evaluate(model_dir, texts, labels, max_length=100):
-    def create_pipeline(nlp):
-        '''
-        This could be a lambda, but named functions are easier to read in Python.
-        '''
-        return [nlp.tagger, nlp.parser, SentimentAnalyser.load(model_dir, nlp,
-                                                               max_length=max_length)]
-    
-    nlp = spacy.load('en')
-    nlp.pipeline = create_pipeline(nlp)
-
-    correct = 0
-    i = 0 
-    for doc in nlp.pipe(texts, batch_size=1000, n_threads=4):
-        correct += bool(doc.sentiment >= 0.5) == bool(labels[i])
-        i += 1
-    return float(correct) / i
-
-
-@plac.annotations(
-    train_dir=("Location of training file or directory"),
-    dev_dir=("Location of development file or directory"),
-    model_dir=("Location of output model directory",),
-    is_runtime=("Demonstrate run-time usage", "flag", "r", bool),
-    nr_hidden=("Number of hidden units", "option", "H", int),
-    max_length=("Maximum sentence length", "option", "L", int),
-    dropout=("Dropout", "option", "d", float),
-    learn_rate=("Learn rate", "option", "e", float),
-    nb_epoch=("Number of training epochs", "option", "i", int),
-    batch_size=("Size of minibatches for training LSTM", "option", "b", int),
-    nr_examples=("Limit to N examples", "option", "n", int)
-)
-def main(model_dir, train_dir, dev_dir,
-         is_runtime=False,
-         nr_hidden=64, max_length=100, # Shape
-         dropout=0.5, learn_rate=0.001, # General NN config
-         nb_epoch=5, batch_size=32, nr_examples=-1):  # Training params
-    model_dir = pathlib.Path(model_dir)
-    train_dir = pathlib.Path(train_dir)
-    dev_dir = pathlib.Path(dev_dir)
-    if is_runtime:
-        dev_texts, dev_labels = read_data(dev_dir)
-        acc = evaluate(model_dir, dev_texts, dev_labels, max_length=max_length)
-        print(acc)
-    else:
-        print("Read data")
-        train_texts, train_labels = read_data(train_dir, limit=nr_examples)
-        dev_texts, dev_labels = read_data(dev_dir, limit=nr_examples)
-        print("Using GPU 0")
-        #chainer.cuda.get_device(0).use()
-        train_labels = xp.asarray(train_labels, dtype='i')
-        dev_labels = xp.asarray(dev_labels, dtype='i')
-        lstm = train(train_texts, train_labels, dev_texts, dev_labels,
-                     {'nr_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 2,
-                      'nr_vector': 5000},
-                      {'dropout': 0.5, 'lr': learn_rate},
-                      {},
-                      nb_epoch=nb_epoch, batch_size=batch_size)
-
-
-if __name__ == '__main__':
-    #cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")
-    #s = pstats.Stats("Profile.prof")
-    #s.strip_dirs().sort_stats("time").print_stats()
-    plac.call(main)
diff --git a/examples/vectors_fast_text.py b/examples/vectors_fast_text.py
new file mode 100644
index 000000000..9aa9fda56
--- /dev/null
+++ b/examples/vectors_fast_text.py
@@ -0,0 +1,30 @@
+'''Load vectors for a language trained using FastText
+
+https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md
+'''
+from __future__ import unicode_literals
+import plac
+import numpy
+
+import spacy.language
+
+
+def main(vectors_loc):
+    nlp = spacy.language.Language()
+
+    with open(vectors_loc, 'rb') as file_:
+        header = file_.readline()
+        nr_row, nr_dim = header.split()
+        nlp.vocab.clear_vectors(int(nr_dim))
+        for line in file_:
+            line = line.decode('utf8')
+            pieces = line.split() 
+            word = pieces[0]
+            vector = numpy.asarray([float(v) for v in pieces[1:]], dtype='f')
+            nlp.vocab.set_vector(word, vector)
+    doc = nlp(u'class colspan')
+    print(doc[0].similarity(doc[1]))
+
+
+if __name__ == '__main__':
+    plac.call(main)

From b2a8b9be77506d5bb5c03885d44893a85044b6a6 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Sun, 1 Oct 2017 17:00:34 -0500
Subject: [PATCH 154/213] Fix inconsistency of Vectors class API

---
 spacy/vectors.pyx | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/spacy/vectors.pyx b/spacy/vectors.pyx
index 346421153..da0489bbf 100644
--- a/spacy/vectors.pyx
+++ b/spacy/vectors.pyx
@@ -24,7 +24,12 @@ cdef class Vectors:
     cdef public int i
 
     def __init__(self, strings, data_or_width=0):
-        self.strings = StringStore()
+        if isinstance(strings, StringStore):
+            self.strings = strings
+        else:
+            self.strings = StringStore()
+            for string in strings:
+                self.strings.add(string)
         if isinstance(data_or_width, int):
             self.data = data = numpy.zeros((len(strings), data_or_width),
                                            dtype='f')
@@ -82,7 +87,8 @@ cdef class Vectors:
         return i
 
     def items(self):
-        for i, string in enumerate(self.strings):
+        for i, key in enumerate(self.keys):
+            string = self.strings[key]
             yield string, self.data[i]
 
     @property

From 4896ce33200352248b48590510e47bbe1b5b8e41 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 2 Oct 2017 00:09:14 +0200
Subject: [PATCH 155/213] Remove misleading comment

---
 spacy/cli/convert.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/cli/convert.py b/spacy/cli/convert.py
index fef6753e6..89615bbe8 100644
--- a/spacy/cli/convert.py
+++ b/spacy/cli/convert.py
@@ -14,7 +14,7 @@ from ..util import prints
 CONVERTERS = {
     '.conllu': conllu2json,
     '.conll': conllu2json,
-    '.iob': iob2json
+    '.iob': iob2json,
 }
 
 

From 31681d20e038fb0b318a9479856da473b0e0e926 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 2 Oct 2017 16:50:26 +0200
Subject: [PATCH 156/213] Fix concatenation in iob2json converter

---
 spacy/cli/converters/iob2json.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/spacy/cli/converters/iob2json.py b/spacy/cli/converters/iob2json.py
index 4849345e9..4d456fa57 100644
--- a/spacy/cli/converters/iob2json.py
+++ b/spacy/cli/converters/iob2json.py
@@ -1,5 +1,6 @@
 # coding: utf8
 from __future__ import unicode_literals
+from cytoolz import partition_all, concat
 
 from ...compat import json_dumps, path2str
 from ...util import prints
@@ -10,22 +11,24 @@ def iob2json(input_path, output_path, n_sents=10, *a, **k):
     """
     Convert IOB files into JSON format for use with train cli.
     """
-    # TODO: This isn't complete yet -- need to map from IOB to
-    # BILUO
     with input_path.open('r', encoding='utf8') as file_:
-        docs = read_iob(file_)
+        if n_sents:
+            lines = [' '.join(para) for para in partition_all(n_sents, file_)]
+        else:
+            lines = file_
+    sentences = read_iob(lines)
 
     output_filename = input_path.parts[-1].replace(".iob", ".json")
     output_file = output_path / output_filename
     with output_file.open('w', encoding='utf-8') as f:
-        f.write(json_dumps(docs))
-    prints("Created %d documents" % len(docs),
+        f.write(json_dumps(sentences))
+    prints("Created %d documents" % len(sentences),
            title="Generated output file %s" % path2str(output_file))
 
 
-def read_iob(file_):
+def read_iob(raw_sents):
     sentences = []
-    for line in file_:
+    for line in raw_sents:
         if not line.strip():
             continue
         tokens = [t.split('|') for t in line.split()]

From f942903429b33b920c18ed7f9c4fe4715733d55f Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 2 Oct 2017 17:02:10 +0200
Subject: [PATCH 157/213] Improve sentence merging in iob2json

---
 spacy/cli/converters/iob2json.py | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/spacy/cli/converters/iob2json.py b/spacy/cli/converters/iob2json.py
index 4d456fa57..74bc22ada 100644
--- a/spacy/cli/converters/iob2json.py
+++ b/spacy/cli/converters/iob2json.py
@@ -12,17 +12,13 @@ def iob2json(input_path, output_path, n_sents=10, *a, **k):
     Convert IOB files into JSON format for use with train cli.
     """
     with input_path.open('r', encoding='utf8') as file_:
-        if n_sents:
-            lines = [' '.join(para) for para in partition_all(n_sents, file_)]
-        else:
-            lines = file_
-    sentences = read_iob(lines)
-
+        sentences = read_iob(file_)
+    docs = merge_sentences(sentences, n_sents)
     output_filename = input_path.parts[-1].replace(".iob", ".json")
     output_file = output_path / output_filename
     with output_file.open('w', encoding='utf-8') as f:
-        f.write(json_dumps(sentences))
-    prints("Created %d documents" % len(sentences),
+        f.write(json_dumps(docs))
+    prints("Created %d documents" % len(docs),
            title="Generated output file %s" % path2str(output_file))
 
 
@@ -46,3 +42,15 @@ def read_iob(raw_sents):
     paragraphs = [{'sentences': [sent]} for sent in sentences]
     docs = [{'id': 0, 'paragraphs': [para]} for para in paragraphs]
     return docs
+
+def merge_sentences(docs, n_sents):
+    counter = 0
+    merged = []
+    for group in partition_all(n_sents, docs):
+        group = list(group)
+        first = group.pop(0)
+        to_extend = first['paragraphs'][0]['sentences']
+        for sent in group[1:]:
+            to_extend.extend(sent['paragraphs'][0]['sentences'])
+        merged.append(first)
+    return merged

From c617d288d8fb9636f9dd077f8393c9e1d2d8626a Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 2 Oct 2017 17:20:19 +0200
Subject: [PATCH 158/213] Update pipeline component names in spaCy train

---
 spacy/cli/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index d973effb6..2096bf0a1 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -69,7 +69,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=10, n_sents=0,
         prints("Expected dict but got: {}".format(type(meta)),
                title="Not a valid meta.json format", exits=1)
 
-    pipeline = ['tags', 'dependencies', 'entities']
+    pipeline = ['tagger', 'parser', 'ner']
     if no_tagger and 'tags' in pipeline: pipeline.remove('tags')
     if no_parser and 'dependencies' in pipeline: pipeline.remove('dependencies')
     if no_entities and 'entities' in pipeline: pipeline.remove('entities')

From 8902df44de0a4b6fb4b4d23a3e3cb1d4088db492 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Mon, 2 Oct 2017 21:07:23 +0200
Subject: [PATCH 159/213] Fix component disabling during training

---
 spacy/cli/train.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 2096bf0a1..651fafb05 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -70,9 +70,9 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=10, n_sents=0,
                title="Not a valid meta.json format", exits=1)
 
     pipeline = ['tagger', 'parser', 'ner']
-    if no_tagger and 'tags' in pipeline: pipeline.remove('tags')
-    if no_parser and 'dependencies' in pipeline: pipeline.remove('dependencies')
-    if no_entities and 'entities' in pipeline: pipeline.remove('entities')
+    if no_tagger and 'tagger' in pipeline: pipeline.remove('tagger')
+    if no_parser and 'parser' in pipeline: pipeline.remove('parser')
+    if no_entities and 'ner' in pipeline: pipeline.remove('ner')
 
     # Take dropout and batch size as generators of values -- dropout
     # starts high and decays sharply, to force the optimizer to explore.

From 3e1b971b16d8c0a11b1f6215513046f4b8c01304 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 14:14:52 +0200
Subject: [PATCH 160/213] Update CSS

---
 website/assets/css/_base/_animations.sass     |   7 +
 website/assets/css/_base/_fonts.sass          |  44 ++----
 website/assets/css/_base/_grid.sass           |  10 +-
 website/assets/css/_base/_layout.sass         |   4 +
 website/assets/css/_base/_objects.sass        |  79 +++++++++-
 website/assets/css/_base/_reset.sass          |   7 +-
 website/assets/css/_base/_utilities.sass      | 149 ++++++++++++++----
 website/assets/css/_components/_asides.sass   |   6 +-
 website/assets/css/_components/_buttons.sass  |  41 ++++-
 website/assets/css/_components/_chat.sass     |  27 ++--
 website/assets/css/_components/_code.sass     |  29 ++--
 website/assets/css/_components/_landing.sass  |  39 ++++-
 website/assets/css/_components/_lists.sass    |   5 +-
 website/assets/css/_components/_misc.sass     |   5 +-
 .../assets/css/_components/_navigation.sass   |  26 +--
 website/assets/css/_components/_progress.sass |  24 +++
 .../assets/css/_components/_quickstart.sass   |  10 +-
 website/assets/css/_components/_sidebar.sass  |  65 +++++++-
 website/assets/css/_components/_tables.sass   |   3 +-
 website/assets/css/_components/_tooltips.sass |  18 ++-
 website/assets/css/_mixins.sass               |  17 +-
 website/assets/css/_variables.sass            |  25 +--
 website/assets/css/style.sass                 |   1 +
 website/assets/css/style_red.sass             |   4 -
 24 files changed, 496 insertions(+), 149 deletions(-)
 create mode 100644 website/assets/css/_components/_progress.sass
 delete mode 100644 website/assets/css/style_red.sass

diff --git a/website/assets/css/_base/_animations.sass b/website/assets/css/_base/_animations.sass
index 376ac5c2f..5c82a4fcc 100644
--- a/website/assets/css/_base/_animations.sass
+++ b/website/assets/css/_base/_animations.sass
@@ -19,3 +19,10 @@
 
     to
         transform: translate3d(0, 0, 0)
+
+
+//- Element rotates
+
+@keyframes rotate
+    to
+        transform: rotate(360deg)
diff --git a/website/assets/css/_base/_fonts.sass b/website/assets/css/_base/_fonts.sass
index be113798c..c1af115a7 100644
--- a/website/assets/css/_base/_fonts.sass
+++ b/website/assets/css/_base/_fonts.sass
@@ -1,41 +1,27 @@
 //- 💫 CSS > BASE > FONTS
 
-//  Source Sans Pro
+//  HK Grotesk
 
 @font-face
-    font-family: "Source Sans Pro"
+    font-family: "HK Grotesk"
     font-style: normal
-    font-weight: 400
-    src: url("/assets/fonts/sourcesanspro-regular.eot")
-    src: url("/assets/fonts/sourcesanspro-regular.eot?#iefix") format("embedded-opentype"), url("/assets/fonts/sourcesanspro-regular.woff2") format("woff2"), url("/assets/fonts/sourcesanspro-regular.woff") format("woff"), url("/assets/fonts/sourcesanspro-regular.ttf") format("truetype"), url("/assets/fonts/sourcesanspro-regular.svg#source_sans_proregular") format("svg")
+    font-weight: 500
+    src: url("/assets/fonts/hkgrotesk-semibold.woff2") format("woff2"), url("/assets/fonts/hkgrotesk-semibold.woff") format("woff")
 
 @font-face
-    font-family: "Source Sans Pro"
+    font-family: "HK Grotesk"
     font-style: italic
-    font-weight: 400
-    src: url("/assets/fonts/sourcesanspro-italic.eot")
-    src: url("/assets/fonts/sourcesanspro-italic.eot?#iefix") format("embedded-opentype"), url("/assets/fonts/sourcesanspro-italic.woff2") format("woff2"), url("/assets/fonts/sourcesanspro-italic.woff") format("woff"), url("/assets/fonts/sourcesanspro-italic.ttf") format("truetype"), url("/assets/fonts/sourcesanspro-italic.svg#source_sans_proitalic") format("svg")
+    font-weight: 500
+    src: url("/assets/fonts/hkgrotesk-semibolditalic.woff2") format("woff2"), url("/assets/fonts/hkgrotesk-semibolditalic.woff") format("woff")
 
 @font-face
-    font-family: "Source Sans Pro"
-    font-style: normal
-    font-weight: 700
-    src: url("/assets/fonts/sourcesanspro-bold.eot")
-    src: url("/assets/fonts/sourcesanspro-bold.eot?#iefix") format("embedded-opentype"), url("/assets/fonts/sourcesanspro-bold.woff2") format("woff2"), url("/assets/fonts/sourcesanspro-bold.woff") format("woff"), url("/assets/fonts/sourcesanspro-bold.ttf") format("truetype"), url("/assets/fonts/sourcesanspro-bold.svg#source_sans_probold") format("svg")
-
-@font-face
-    font-family: "Source Sans Pro"
-    font-style: italic
-    font-weight: 700
-    src: url("/assets/fonts/sourcesanspro-bolditalic.eot")
-    src: url("/assets/fonts/sourcesanspro-bolditalic.eot?#iefix") format("embedded-opentype"), url("/assets/fonts/sourcesanspro-bolditalic.woff2") format("woff2"), url("/assets/fonts/sourcesanspro-bolditalic.woff") format("woff"), url("/assets/fonts/sourcesanspro-bolditalic.ttf") format("truetype"), url("/assets/fonts/sourcesanspro-bolditalic.svg#source_sans_probold_italic") format("svg")
-
-
-//  Source Code Pro
-
-@font-face
-    font-family: "Source Code Pro"
+    font-family: "HK Grotesk"
     font-style: normal
     font-weight: 600
-    src: url("/assets/fonts/sourcecodepro-semibold.eot")
-    src: url("/assets/fonts/sourcecodepro-semibold.eot?#iefix") format("embedded-opentype"), url("/assets/fonts/sourcecodepro-semibold.woff") format("woff"), url("/assets/fonts/sourcecodepro-semibold.ttf") format("truetype"), url("/assets/fonts/sourcecodepro-semibold.svg#sourcecodepro_semibold") format("svg")
+    src: url("/assets/fonts/hkgrotesk-bold.woff2") format("woff2"), url("/assets/fonts/hkgrotesk-bold.woff") format("woff")
+
+@font-face
+    font-family: "HK Grotesk"
+    font-style: italic
+    font-weight: 600
+    src: url("/assets/fonts/hkgrotesk-bolditalic.woff2") format("woff2"), url("/assets/fonts/hkgrotesk-bolditalic.woff") format("woff")
diff --git a/website/assets/css/_base/_grid.sass b/website/assets/css/_base/_grid.sass
index 3feda696d..536c657db 100644
--- a/website/assets/css/_base/_grid.sass
+++ b/website/assets/css/_base/_grid.sass
@@ -15,6 +15,15 @@
         align-items: center
         justify-content: center
 
+    &.o-grid--vcenter
+        align-items: center
+
+    &.o-grid--space
+        justify-content: space-between
+
+    &.o-grid--nowrap
+        flex-wrap: nowrap
+
 
 //- Grid column
 
@@ -22,7 +31,6 @@
     $grid-gutter: 2rem
 
     margin-top: $grid-gutter
-    overflow: hidden
 
     @include breakpoint(min, lg)
         display: flex
diff --git a/website/assets/css/_base/_layout.sass b/website/assets/css/_base/_layout.sass
index 8828651c6..1b725fdbf 100644
--- a/website/assets/css/_base/_layout.sass
+++ b/website/assets/css/_base/_layout.sass
@@ -12,6 +12,7 @@ body
     animation: fadeIn 0.25s ease
     background: $color-back
     color: $color-front
+    //scroll-behavior: smooth
 
 
 //- Paragraphs
@@ -19,6 +20,9 @@ body
 p
     @extend .o-block, .u-text
 
+p:empty
+    margin-bottom: 0
+
 
 //- Links
 
diff --git a/website/assets/css/_base/_objects.sass b/website/assets/css/_base/_objects.sass
index 635e9cde3..8494ee36a 100644
--- a/website/assets/css/_base/_objects.sass
+++ b/website/assets/css/_base/_objects.sass
@@ -43,12 +43,25 @@
     position: relative
     padding: 2.5rem 0
     overflow: auto
+    background: $color-subtle-light
+
+    .o-main &
+        border-top-left-radius: $border-radius
 
 
 //- Blocks
 
+.o-section
+    width: 100%
+    max-width: 100%
+
+    &:not(:last-child)
+        margin-bottom: 7rem
+        padding-bottom: 4rem
+        border-bottom: 1px dotted $color-subtle
+
 .o-block
-    margin-bottom: 3rem
+    margin-bottom: 4rem
 
 .o-block-small
     margin-bottom: 2rem
@@ -58,17 +71,18 @@
 
 .o-card
     background: $color-back
-    border-radius: 2px
-    border: 1px solid $color-subtle
-    padding: 3rem 2.5%
-
+    border-radius: $border-radius
+    box-shadow: $box-shadow
 
 //- Box
 
 .o-box
-    background: $color-theme-light
+    background: $color-subtle-light
     padding: 2rem
-    border-left: 4px solid $color-theme
+    border-radius: $border-radius
+
+.o-box__logos
+    padding-bottom: 1rem
 
 
 //- Icons
@@ -77,7 +91,14 @@
     vertical-align: middle
 
     &.o-icon--inline
-        margin: 0 0.5rem 0 0.25rem
+        margin: 0 0.5rem 0 0.1rem
+
+.o-emoji
+    margin-right: 0.75rem
+    vertical-align: text-bottom
+
+.o-badge
+    border-radius: 1em
 
 
 //- SVG
@@ -102,3 +123,45 @@
     fill: currentColor
     vertical-align: middle
     margin: 0 0.5rem
+
+
+//- Embeds
+
+.o-chart
+    max-width: 100%
+
+.cp_embed_iframe
+    border: 1px solid $color-subtle
+    border-radius: $border-radius
+
+
+//- Form fields
+
+.o-field
+    background: $color-back
+    padding: 0 0.25em
+    border-radius: 2em
+    border: 1px solid $color-subtle
+    margin-bottom: 0.25rem
+
+.o-field__input,
+.o-field__button
+    padding: 0 0.35em
+
+.o-field__input
+    width: 100%
+
+.o-field__select
+     background: transparent
+     color: $color-dark
+     height: 1.4em
+     border: none
+     text-align-last: center
+
+.o-empty:empty:before
+    @include size(1em)
+    border-radius: 50%
+    content: ""
+    display: inline-block
+    background: $color-red
+    vertical-align: middle
diff --git a/website/assets/css/_base/_reset.sass b/website/assets/css/_base/_reset.sass
index 1d9d9ffbe..0ff1432d0 100644
--- a/website/assets/css/_base/_reset.sass
+++ b/website/assets/css/_base/_reset.sass
@@ -1,6 +1,6 @@
 //- 💫 CSS > BASE > RESET
 
-*
+*, *:before, *:after
     box-sizing: border-box
     padding: 0
     margin: 0
@@ -94,7 +94,10 @@ ul, ol
 
 input, button
     appearance: none
+    background: transparent
 
 button
-    background: transparent
     cursor: pointer
+
+progress
+    appearance: none
diff --git a/website/assets/css/_base/_utilities.sass b/website/assets/css/_base/_utilities.sass
index 46c3e84d9..e2ba552b7 100644
--- a/website/assets/css/_base/_utilities.sass
+++ b/website/assets/css/_base/_utilities.sass
@@ -2,38 +2,53 @@
 
 //- Text
 
+.u-text,
+.u-text-small,
+.u-text-tiny
+    font-family: $font-primary
+
 .u-text
-    font: 1.5rem/#{1.55} $font-primary
+    font-size: 1.35rem
+    line-height: 1.5
 
 .u-text-small
-    font: 1.4rem/#{1.375} $font-primary
+    font-size: 1.3rem
+    line-height: 1.375
 
 .u-text-tiny
-    font: 1.1rem/#{1.375} $font-primary
-
+    font-size: 1.1rem
+    line-height: 1.375
 
 //- Labels & Tags
 
 .u-text-label
-    font: normal 600 1.4rem/#{1.5} $font-code
+    font: normal 600 1.4rem/#{1.5} $font-secondary
     text-transform: uppercase
 
+    &.u-text-label--light,
     &.u-text-label--dark
         display: inline-block
+        border-radius: 1em
+        padding: 0 1rem 0.15rem
+
+    &.u-text-label--dark
         background: $color-dark
         box-shadow: inset 1px 1px 1px rgba($color-front, 0.25)
         color: $color-back
-        padding: 0 0.75rem
         margin: 1.5rem 0 0 2rem
-        border-radius: 2px
+
+    &.u-text-label--light
+        background: $color-back
+        color: $color-theme
+        margin-bottom: 1rem
 
 .u-text-tag
     display: inline-block
-    font: 600 1.1rem/#{1} $font-code
+    font: 600 1.1rem/#{1} $font-secondary
     background: $color-theme
     color: $color-back
-    padding: 0.15em 0.25em
-    border-radius: 2px
+    padding: 0.15em 0.5em 0.35em
+    border-radius: 1em
     text-transform: uppercase
     vertical-align: middle
 
@@ -45,7 +60,7 @@
 //- Headings
 
 .u-heading
-    margin-bottom: 2rem
+    margin-bottom: 1em
 
     @include breakpoint(max, md)
         word-wrap: break-word
@@ -53,12 +68,29 @@
     &:not(:first-child)
         padding-top: 3.5rem
 
+    &.u-heading--title:after
+        content: ""
+        display: block
+        width: 10%
+        min-width: 6rem
+        height: 6px
+        background: $color-theme
+        margin-top: 3rem
+
 .u-heading-0
-    font: normal bold 7rem/#{1} $font-primary
+    font: normal 600 7rem/#{1} $font-secondary
+
+    @include breakpoint(max, sm)
+        font-size: 6rem
+
 
 @each $level, $size in $headings
     .u-heading-#{$level}
-        font: normal bold #{$size}rem/#{1.25} $font-primary
+        font: normal 500 #{$size}rem/#{1.1} $font-secondary
+
+.u-heading__teaser
+    margin-top: 2rem
+    font-weight: normal
 
 
 //- Links
@@ -66,31 +98,59 @@
 .u-link
     color: $color-theme
     border-bottom: 1px solid
+    transition: color 0.2s ease
+
+    &:hover
+        color: $color-theme-dark
+
+.u-hide-link.u-hide-link
+    border: none
+    color: inherit
+
+    &:hover
+        color: inherit
 
 .u-permalink
     position: relative
 
+    &:before
+        content: "\00b6"
+        font-size: 0.9em
+        font-weight: normal
+        color: $color-subtle
+        @include position(absolute, top, left, 0.15em, -2.85rem)
+        opacity: 0
+        transition: opacity 0.2s ease
+
+    &:hover:before
+        opacity: 1
+
+    &:active:before
+        color: $color-theme
+
     &:target
         display: inline-block
-        padding-top: $nav-height * 1.25
 
-        & + *
-            margin-top: $nav-height * 1.25
+        &:before
+            bottom: 0.15em
+            top: initial
 
-.u-permalink__icon
-    @include position(absolute, bottom, left, 0.35em, -2.75rem)
-    @include size(1.5rem)
-    color: $color-subtle
 
-    .u-permalink:hover &
-        color: $color-subtle-dark
+[id]:target
+    padding-top: $nav-height * 1.25
 
-    .u-permalink:active &
-        color: $color-theme
 
 
 //- Layout
 
+.u-float-left
+    float: left
+    margin-right: 1rem
+
+.u-float-right
+    float: right
+    margin-left: 1rem
+
 .u-text-center
     text-align: center
 
@@ -104,14 +164,20 @@
     padding: 0.5em 0.75em
 
 .u-padding-medium
-    padding: 2.5rem
+    padding: 1.8rem
 
 .u-inline-block
     display: inline-block
 
+.u-flex-full
+    flex: 1
+
 .u-nowrap
     white-space: nowrap
 
+.u-wrap
+    white-space: pre-wrap
+
 .u-break.u-break
     word-wrap: break-word
     white-space: initial
@@ -123,13 +189,10 @@
     border: 1px solid $color-subtle
     border-radius: 2px
 
-.u-border-bottom
-    border: 1px solid $color-subtle
-
 .u-border-dotted
-    border-top: 1px dotted $color-subtle
+    border-bottom: 1px dotted $color-subtle
 
-@each $name, $color in (theme: $color-theme, subtle: $color-subtle-dark, light: $color-back, red: $color-red, green: $color-green, yellow: $color-yellow)
+@each $name, $color in (theme: $color-theme, dark: $color-dark, subtle: $color-subtle-dark, light: $color-back, red: $color-red, green: $color-green, yellow: $color-yellow)
     .u-color-#{$name}
         color: $color
 
@@ -145,6 +208,32 @@
     background: $pattern
 
 
+//- Loaders
+
+.u-loading,
+[data-loading]
+    $spinner-size: 75px
+    $spinner-bar: 8px
+
+    position: relative
+
+    & > *
+        opacity: 0.35
+
+    &:before
+        @include position(absolute, top, left, 0, 0)
+        @include size($spinner-size)
+        right: 0
+        bottom: 0
+        margin: auto
+        content: ""
+        border: $spinner-bar solid $color-subtle
+        border-right: $spinner-bar solid $color-theme
+        border-radius: 50%
+        animation: rotate 1s linear infinite
+        z-index: 10
+
+
 //- Hidden elements
 
 .u-hidden
diff --git a/website/assets/css/_components/_asides.sass b/website/assets/css/_components/_asides.sass
index d5b5c64e3..c59590c29 100644
--- a/website/assets/css/_components/_asides.sass
+++ b/website/assets/css/_components/_asides.sass
@@ -10,6 +10,8 @@
 
 .c-aside__content
     background: $color-front
+    border-top-left-radius: $border-radius
+    border-bottom-left-radius: $border-radius
     z-index: 10
 
     @include breakpoint(min, md)
@@ -21,12 +23,12 @@
         &:after
             $triangle-size: 2rem
 
-            @include position(absolute, bottom, left, -$triangle-size / 2, 0)
+            @include position(absolute, bottom, left, -$triangle-size / 2, $border-radius / 2)
             @include size(0)
             border-color: transparent
             border-style: solid
             border-top-color: $color-dark
-            border-width: $triangle-size / 2 0 0 $triangle-size
+            border-width: $triangle-size / 2 0 0 calc(#{$triangle-size} - #{$border-radius / 2})
             content: ""
 
     @include breakpoint(max, sm)
diff --git a/website/assets/css/_components/_buttons.sass b/website/assets/css/_components/_buttons.sass
index f753e15bf..d3ff4b037 100644
--- a/website/assets/css/_components/_buttons.sass
+++ b/website/assets/css/_components/_buttons.sass
@@ -3,23 +3,50 @@
 .c-button
     display: inline-block
     font-weight: bold
-    padding: 0.75em 1em
+    padding: 0.8em 1.1em 1em
     margin-bottom: 1px
-    border: 2px solid
-    border-radius: 2px
+    border: 2px solid $color-theme
+    border-radius: 2em
     text-align: center
-    transition: background 0.25s ease
+    transition: background-color, color 0.25s ease
+
+    &:hover
+        border-color: $color-theme-dark
+
+    &.c-button--small
+        font-size: 1.1rem
+        padding: 0.65rem 1.1rem 0.825rem
 
     &.c-button--primary
         background: $color-theme
         color: $color-back
-        border-color: $color-theme
 
         &:hover
             background: $color-theme-dark
-            border-color: $color-theme-dark
 
     &.c-button--secondary
         background: $color-back
         color: $color-theme
-        border-color: $color-theme
+
+        &:hover
+            color: $color-theme-dark
+
+    &.c-button--secondary-light
+        background: transparent
+        color: $color-back
+        border-color: $color-back
+
+.c-icon-button
+    @include size(35px)
+    background: $color-subtle-light
+    color: $color-subtle-dark
+    border-radius: 50%
+    padding: 0.5rem
+    transition: color 0.2s ease
+
+    &:hover
+        color: $color-theme
+
+    &.c-icon-button--right
+        float: right
+        margin-left: 3rem
diff --git a/website/assets/css/_components/_chat.sass b/website/assets/css/_components/_chat.sass
index 2a1e5cc3d..659f80364 100644
--- a/website/assets/css/_components/_chat.sass
+++ b/website/assets/css/_components/_chat.sass
@@ -24,9 +24,9 @@
         transform: translateX(110%)
 
     &:before
-        @include position(absolute, top, left, 1rem, 2rem)
+        @include position(absolute, top, left, 1.25rem, 2rem)
         content: attr(data-title)
-        font: bold 1.4rem $font-code
+        font: bold 1.4rem $font-secondary
         text-transform: uppercase
         color: $color-back
 
@@ -88,13 +88,18 @@
             background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij48cGF0aCBmaWxsPSIjZmZmIiBkPSJNMTguOTg0IDYuNDIybC01LjU3OCA1LjU3OCA1LjU3OCA1LjU3OC0xLjQwNiAxLjQwNi01LjU3OC01LjU3OC01LjU3OCA1LjU3OC0xLjQwNi0xLjQwNiA1LjU3OC01LjU3OC01LjU3OC01LjU3OCAxLjQwNi0xLjQwNiA1LjU3OCA1LjU3OCA1LjU3OC01LjU3OHoiPjwvcGF0aD48L3N2Zz4=)
 
 .c-chat__button
-    @include position(fixed, bottom, right, 0, 2rem)
-    padding: 1rem 1.5rem
-    background: $color-front
+    @include position(fixed, bottom, right, 1.5rem, 1.5rem)
+    z-index: 5
     color: $color-back
-    border-top-left-radius: 4px
-    border-top-right-radius: 4px
-    z-index: 20
-    border-color: $color-theme
-    border-style: solid
-    border-width: 1px 1px 0 1px
+    background: $color-front
+    border-radius: 1em
+    padding: 0.5rem 1.15rem 0.35rem
+    opacity: 0.7
+    transition: opacity 0.2s ease
+
+    &:hover
+        opacity: 1
+
+
+.gitter-open-chat-button
+    display: none
diff --git a/website/assets/css/_components/_code.sass b/website/assets/css/_components/_code.sass
index 036c5358f..f83e96d29 100644
--- a/website/assets/css/_components/_code.sass
+++ b/website/assets/css/_components/_code.sass
@@ -4,9 +4,9 @@
 
 .c-code-block
     background: $color-front
-    color: $color-back
+    color: darken($color-back, 20)
     padding: 0.75em 0
-    border-radius: 2px
+    border-radius: $border-radius
     overflow: auto
     width: 100%
     max-width: 100%
@@ -16,6 +16,8 @@
     &.c-code-block--has-icon
         padding: 0
         display: flex
+        border-top-left-radius: 0
+        border-bottom-left-radius: 0
 
 .c-code-block__icon
     padding: 0 0 0 1rem
@@ -43,17 +45,26 @@
         opacity: 0.5
 
 
+//- Code
+
+code
+    -webkit-font-smoothing: subpixel-antialiased
+    -moz-osx-font-smoothing: auto
+
+
 //- Inline code
 
+*:not(a):not(.c-code-block) > code
+    color: $color-dark
+
 *:not(.c-code-block) > code
-    font: normal 600 0.8em/#{1} $font-code
-    background: darken($color-theme-light, 5)
-    box-shadow: 1px 1px 0 rgba($color-front, 0.05)
-    text-shadow: 1px 1px 0 rgba($color-back, 0.5)
-    color: $color-front
-    padding: 0.1em 0.5em
+    font-size: 90%
+    background-color: $color-subtle-light
+    padding: 0.2rem 0.4rem
+    border-radius: 0.25rem
+    font-family: $font-code
+    white-space: nowrap
     margin: 0
-    border-radius: 1px
     box-decoration-break: clone
     white-space: nowrap
 
diff --git a/website/assets/css/_components/_landing.sass b/website/assets/css/_components/_landing.sass
index af1521d10..4c15e4a39 100644
--- a/website/assets/css/_components/_landing.sass
+++ b/website/assets/css/_components/_landing.sass
@@ -2,12 +2,11 @@
 
 .c-landing
     background: $color-theme
-    padding-top: 5rem
+    padding-top: $nav-height * 1.5
     width: 100%
 
 .c-landing__wrapper
     background: $pattern
-    padding-bottom: 6rem
     width: 100%
 
 .c-landing__content
@@ -15,9 +14,45 @@
     width: 100%
     min-height: 573px
 
+.c-landing__headlines
+    position: relative
+    top: -1.5rem
+    left: 1rem
+
 .c-landing__title
     color: $color-back
     text-align: center
+    margin-bottom: 0.75rem
+
+.c-landing__blocks
+    @include breakpoint(min, sm)
+        position: relative
+        top: -25rem
+        margin-bottom: -25rem
+
+.c-landing__card
+    padding: 3rem 2.5rem
+
+.c-landing__banner
+    background: $color-theme
+
+.c-landing__banner__content
+    @include breakpoint(min, md)
+        border: 4px solid
+        padding: 1rem 6.5rem 2rem 4rem
+
+
+.c-landing__banner__text
+    font-weight: 500
+
+    strong
+        font-weight: 800
+
+    p
+        font-size: 1.5rem
+
+    @include breakpoint(min, md)
+        padding-top: 7rem
 
 .c-landing__badge
     transform: rotate(7deg)
diff --git a/website/assets/css/_components/_lists.sass b/website/assets/css/_components/_lists.sass
index 48a5e92c8..2a933c95e 100644
--- a/website/assets/css/_components/_lists.sass
+++ b/website/assets/css/_components/_lists.sass
@@ -9,6 +9,8 @@
 
             .c-list__item:before
                 content: counter(li, #{$counter}) '.'
+                font-size: 1em
+                padding-right: 1rem
 
 
 //- List Item
@@ -21,13 +23,14 @@
     &:before
         content: '\25CF'
         display: inline-block
-        font-size: 1em
+        font-size: 0.6em
         font-weight: bold
         padding-right: 1.25rem
         margin-left: -3.75rem
         text-align: right
         width: 2.5rem
         counter-increment: li
+        box-sizing: content-box
 
 
 //- List icon
diff --git a/website/assets/css/_components/_misc.sass b/website/assets/css/_components/_misc.sass
index 3bd9bd6b6..8167c94b2 100644
--- a/website/assets/css/_components/_misc.sass
+++ b/website/assets/css/_components/_misc.sass
@@ -3,9 +3,8 @@
 .x-terminal
     background: $color-subtle-light
     color: $color-front
-    padding: 4px
-    border: 1px dotted $color-subtle
-    border-radius: 5px
+    padding: $border-radius
+    border-radius: 1em
     width: 100%
 
 .x-terminal__icons
diff --git a/website/assets/css/_components/_navigation.sass b/website/assets/css/_components/_navigation.sass
index 5b7275f92..0e4af8267 100644
--- a/website/assets/css/_components/_navigation.sass
+++ b/website/assets/css/_components/_navigation.sass
@@ -1,22 +1,21 @@
 //- 💫 CSS > COMPONENTS > NAVIGATION
 
 .c-nav
-    @include position(absolute, top, left, 0, 0)
+    @include position(fixed, top, left, 0, 0)
     @include size(100%, $nav-height)
     background: $color-back
     color: $color-theme
     align-items: center
     display: flex
     justify-content: space-between
+    flex-flow: row wrap
     padding: 0 2rem 0 1rem
-    z-index: 20
+    z-index: 30
     width: 100%
-    border-bottom: 1px solid $color-subtle
+    box-shadow: $box-shadow
 
-    &.c-nav--theme
-        background: $color-theme
-        color: $color-back
-        border-bottom: none
+    //@include breakpoint(min, md)
+    //    position: fixed
 
     &.is-fixed
         animation: slideInDown 0.5s ease-in-out
@@ -28,12 +27,21 @@
     justify-content: flex-end
     flex-flow: row nowrap
     border-color: inherit
+    flex: 1
 
 .c-nav__menu__item
     display: flex
     align-items: center
     height: 100%
     text-transform: uppercase
+    font-family: $font-secondary
+    font-size: 1.6rem
+    font-weight: bold
+    color: $color-theme
 
-    &:not(:last-child)
-        margin-right: 1em
+    &:not(:first-child)
+        margin-left: 2em
+
+    &.is-active
+        color: $color-dark
+        pointer-events: none
diff --git a/website/assets/css/_components/_progress.sass b/website/assets/css/_components/_progress.sass
new file mode 100644
index 000000000..bbab0fddd
--- /dev/null
+++ b/website/assets/css/_components/_progress.sass
@@ -0,0 +1,24 @@
+//- 💫 CSS > COMPONENTS > PROGRESS
+
+.c-progress
+    display: block
+    flex: 105%
+    width: 105%
+    height: 3px
+    color: $color-theme
+    background: transparent
+    border: none
+    position: absolute
+    bottom: 0
+    left: -2.5%
+
+    &::-webkit-progress-bar
+        background: $color-back
+        border-radius: none
+
+    &::-webkit-progress-value
+        background: $color-theme
+        border-radius: none
+
+    &::-moz-progress-bar
+        background: $color-theme
diff --git a/website/assets/css/_components/_quickstart.sass b/website/assets/css/_components/_quickstart.sass
index 1e7d0761a..6b02b3128 100644
--- a/website/assets/css/_components/_quickstart.sass
+++ b/website/assets/css/_components/_quickstart.sass
@@ -1,14 +1,17 @@
 //- 💫 CSS > COMPONENTS > QUICKSTART
 
 .c-quickstart
-    border: 1px solid $color-subtle
-    border-radius: 2px
+    border-radius: $border-radius
     display: none
     background: $color-subtle-light
 
     &:not([style]) + .c-quickstart__info
         display: none
 
+    .c-code-block
+        border-top-left-radius: 0
+        border-top-right-radius: 0
+
 .c-quickstart__content
     padding: 2rem 3rem
 
@@ -72,7 +75,6 @@
     flex: 100%
 
 .c-quickstart__legend
-    color: $color-subtle-dark
     margin-right: 2rem
     padding-top: 0.75rem
     flex: 1 1 35%
@@ -95,4 +97,4 @@
         padding: 1.5rem 0
 
 .c-quickstart__code
-    font-size: 1.6rem
+    font-size: 1.4rem
diff --git a/website/assets/css/_components/_sidebar.sass b/website/assets/css/_components/_sidebar.sass
index d88588341..be3e34147 100644
--- a/website/assets/css/_components/_sidebar.sass
+++ b/website/assets/css/_components/_sidebar.sass
@@ -3,16 +3,15 @@
 //- Sidebar container
 
 .c-sidebar
-    background: $color-subtle-light
     overflow-y: auto
 
     @include breakpoint(min, md)
         @include position(fixed, top, left, 0, 0)
-        @include size($sidebar-width, 100vh)
+        @include size($sidebar-width, calc(100vh - 3px))
+        @include scroll-shadow($color-back, $color-front, $nav-height)
         flex: 0 0 $sidebar-width
         padding: calc(#{$nav-height} + 1.5rem) 0 0
         z-index: 10
-        border-right: 1px solid $color-subtle
 
     @include breakpoint(max, sm)
         flex: 100%
@@ -27,7 +26,7 @@
 
 .c-sidebar__section
     & > *
-        padding: 0 2rem
+        padding: 0 2rem 0.35rem
 
     @include breakpoint(max, sm)
         flex: 1 1 0
@@ -38,7 +37,59 @@
         &:not(:last-child)
             border-right: 1px solid $color-subtle
 
-    .is-active
+.c-sidebar__item
+    color: $color-theme
+
+    &:hover
+        color: $color-theme-dark
+
+    & > .is-active
         font-weight: bold
-        color: $color-theme
-        background: rgba($color-subtle, 0.4)
+        color: $color-dark
+        margin-top: 1rem
+
+
+//- Sidebar subsections
+
+$crumb-bullet: 14px
+$crumb-bar: 2px
+
+.c-sidebar__crumb
+    display: block
+    padding-top: 1rem
+    padding-left: 1rem
+    position: relative
+
+.c-sidebar__crumb__item
+    margin-bottom: $crumb-bullet / 2
+    position: relative
+    padding-left: 2rem
+    color: $color-theme
+    font-size: 1.2rem
+
+    &:hover
+        color: $color-theme-dark
+
+    &:after
+        @include size($crumb-bullet)
+        @include position(absolute, top, left, $crumb-bullet / 4, 0)
+        content: ""
+        border-radius: 50%
+        background: $color-theme
+        z-index: 10
+
+    &:not(:last-child):before
+        @include size($crumb-bar, 100%)
+        @include position(absolute, top, left, $crumb-bullet, ($crumb-bullet - $crumb-bar) / 2)
+        content: ""
+        background: $color-subtle
+
+    &:first-child:before
+        height: calc(100% + #{$crumb-bullet * 2})
+        top: -$crumb-bullet / 2
+
+    &.is-active
+        color: $color-dark
+
+        &:after
+            background: $color-dark
diff --git a/website/assets/css/_components/_tables.sass b/website/assets/css/_components/_tables.sass
index cbc861803..1878e2c5e 100644
--- a/website/assets/css/_components/_tables.sass
+++ b/website/assets/css/_components/_tables.sass
@@ -9,7 +9,7 @@
 //- Table row
 
 .c-table__row
-    &:nth-child(odd)
+    &:nth-child(odd):not(.c-table__row--head)
         background: rgba($color-subtle-light, 0.35)
 
     &.c-table__row--foot
@@ -38,7 +38,6 @@
 .c-table__head-cell
     font-weight: bold
     color: $color-theme
-    background: $color-back
     padding: 1rem 0.5rem
     border-bottom: 2px solid $color-theme
 
diff --git a/website/assets/css/_components/_tooltips.sass b/website/assets/css/_components/_tooltips.sass
index e68f2875c..f9284dcdb 100644
--- a/website/assets/css/_components/_tooltips.sass
+++ b/website/assets/css/_components/_tooltips.sass
@@ -4,24 +4,34 @@
     position: relative
 
     @include breakpoint(min, sm)
+        &[data-tooltip-style="code"]:before
+            -webkit-font-smoothing: subpixel-antialiased
+            -moz-osx-font-smoothing: auto
+            padding: 0.35em 0.85em 0.45em
+            font: normal 1rem/#{1.25} $font-code
+            white-space: nowrap
+            min-width: auto
+
         &:before
             @include position(absolute, top, left, 125%, 50%)
             display: inline-block
             content: attr(data-tooltip)
             background: $color-front
-            border-radius: 2px
+            border-radius: $border-radius
             border: 1px solid rgba($color-subtle-dark, 0.5)
             color: $color-back
-            font: normal 1.3rem/#{1.25} $font-primary
+            font: normal 1.2rem/#{1.25} $font-primary
             text-transform: none
+            text-align: left
             opacity: 0
-            padding: 0.5em 0.75em
             transform: translateX(-50%) translateY(-2px)
             transition: opacity 0.1s ease-out, transform 0.1s ease-out
             visibility: hidden
-            min-width: 200px
             max-width: 300px
+            min-width: 200px
+            padding: 0.75em 1em 1em
             z-index: 200
+            white-space: pre-wrap
 
         &:hover:before
             opacity: 1
diff --git a/website/assets/css/_mixins.sass b/website/assets/css/_mixins.sass
index e7e7a3432..641f6e148 100644
--- a/website/assets/css/_mixins.sass
+++ b/website/assets/css/_mixins.sass
@@ -42,8 +42,8 @@
 // $scroll-shadow-side       - side to cover shadow (left or right)
 // $scroll-shadow-background - original background color to match
 
-@mixin scroll-shadow-base($scroll-shadow-color)
-    background: radial-gradient(left, ellipse, rgba(0,0,0, .2) 0%, rgba(0,0,0, 0) 75%) 0 center, radial-gradient(right, ellipse, rgba(0,0,0, .2) 0%, rgba(0,0,0, 0) 75%) 100% center
+@mixin scroll-shadow-base($scroll-shadow-color, $scroll-shadow-intensity: 0.2)
+    background: radial-gradient(ellipse at 0 50%, rgba($scroll-shadow-color, $scroll-shadow-intensity) 0%, rgba(0,0,0,0) 75%) 0 center, radial-gradient(ellipse at 100% 50%, rgba($scroll-shadow-color, $scroll-shadow-intensity) 0%, transparent 75%) 100% center
     background-attachment: scroll, scroll
     background-repeat: no-repeat
     background-size: 10px 100%, 10px 100%
@@ -58,3 +58,16 @@
     background-image: linear-gradient(to #{$scroll-gradient-direction}, rgba($scroll-shadow-background, 1) 50%, rgba($scroll-shadow-background, 0) 100%)
     background-repeat: no-repeat
     background-size: 20px 100%
+
+
+// Full vertical scroll shadows
+// adapted from: https://codepen.io/laustdeleuran/pen/DBaAu
+
+@mixin scroll-shadow($background-color, $shadow-color, $shadow-offset: 0, $shadow-intensity: 0.4, $cover-size: 40px, $shadow-size: 15px)
+    background: linear-gradient($background-color 30%, rgba($background-color,0)) 0 $shadow-offset, linear-gradient(rgba($background-color,0), $background-color 70%) 0 100%, radial-gradient(50% 0, farthest-side, rgba($shadow-color,$shadow-intensity), rgba($shadow-color,0)) 0 $shadow-offset, radial-gradient(50% 100%,farthest-side, rgba($shadow-color,$shadow-intensity), rgba($shadow-color,0)) 0 100%
+
+    background: linear-gradient($background-color 30%, rgba($background-color,0)) 0 $shadow-offset, linear-gradient(rgba($background-color,0), $background-color 70%) 0 100%, radial-gradient(farthest-side at 50% 0, rgba($shadow-color,$shadow-intensity), rgba($shadow-color,0)) -20px $shadow-offset, radial-gradient(farthest-side at 50% 100%, rgba($shadow-color, $shadow-intensity), rgba($shadow-color,0)) 0 100%
+    background-repeat: no-repeat
+    background-color: $background-color
+    background-size: 100% $cover-size, 100% $cover-size, 100% $shadow-size, 100% $shadow-size
+    background-attachment: local, local, scroll, scroll
diff --git a/website/assets/css/_variables.sass b/website/assets/css/_variables.sass
index 3ccf36f06..4fafbfca5 100644
--- a/website/assets/css/_variables.sass
+++ b/website/assets/css/_variables.sass
@@ -4,47 +4,48 @@
 
 $type-base: 11px
 
-$nav-height: 45px
+$nav-height: 55px
 $content-width: 1250px
-$sidebar-width: 200px
-$aside-width: 30vw
+$sidebar-width: 235px
+$aside-width: 27.5vw
 $aside-padding: 25px
+$border-radius: 6px
 
 $logo-width: 85px
 $logo-height: 27px
 
 $grid: ( quarter: 4, third: 3, half: 2, two-thirds: 1.5, three-quarters: 1.33 )
 $breakpoints: ( sm: 768px, md: 992px, lg: 1200px )
-$headings: (1: 3, 2: 2.6, 3: 2, 4: 1.8, 5: 1.5)
-
+$headings: (1: 4.4, 2: 3.4, 3: 2.6, 4: 2.2, 5: 1.8)
 
 //  Fonts
 
-$font-primary: "Source Sans Pro", Tahoma, Geneva, sans-serif !default
-$font-code: 'Source Code Pro', Consolas, 'Andale Mono', Menlo, Monaco, Courier, monospace !default
-
+$font-primary: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol" !default
+$font-secondary: "HK Grotesk" !default
+$font-code: Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace !default
 
 // Colors
 
-$colors: ( blue: #09a3d5, red: #d9515d, green: #08c35e )
+$colors: ( blue: #09a3d5, green: #05b083 )
 
 $color-back: #fff !default
 $color-front: #1a1e23 !default
 $color-dark: lighten($color-front, 20) !default
 
 $color-theme: map-get($colors, $theme)
-$color-theme-dark: darken(map-get($colors, $theme), 5)
+$color-theme-dark: darken(map-get($colors, $theme), 10)
 $color-theme-light: rgba($color-theme, 0.05)
 
 $color-subtle: #ddd !default
 $color-subtle-light: #f6f6f6 !default
 $color-subtle-dark: #949e9b !default
 
-$color-red: #d9515d
-$color-green: #3ec930
+$color-red: #ef476f
+$color-green: #7ddf64
 $color-yellow: #f4c025
 
 $syntax-highlighting: ( comment: #949e9b, tag: #b084eb, number: #b084eb, selector: #ffb86c, operator: #ff2c6d, function: #35b3dc, keyword: #ff2c6d, regex: #f4c025 )
 
 $pattern: $color-theme url("/assets/img/pattern_#{$theme}.jpg") center top repeat
 $pattern-overlay: transparent url("/assets/img/pattern_landing.jpg") center -138px no-repeat
+$box-shadow: 0 1px 5px rgba(0, 0, 0, 0.2)
diff --git a/website/assets/css/style.sass b/website/assets/css/style.sass
index eaf7cdf70..47cf3f1b5 100644
--- a/website/assets/css/style.sass
+++ b/website/assets/css/style.sass
@@ -30,6 +30,7 @@ $theme: blue !default
 @import _components/lists
 @import _components/misc
 @import _components/navigation
+@import _components/progress
 @import _components/sidebar
 @import _components/tables
 @import _components/quickstart
diff --git a/website/assets/css/style_red.sass b/website/assets/css/style_red.sass
deleted file mode 100644
index 83fe330b9..000000000
--- a/website/assets/css/style_red.sass
+++ /dev/null
@@ -1,4 +0,0 @@
-//- 💫 STYLESHEET (RED)
-
-$theme: red
-@import style

From 7d01d7411b8e5ed8f8abd3c1ee9287147070057a Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 14:15:36 +0200
Subject: [PATCH 161/213] Update web fonts

---
 website/assets/fonts/hkgrotesk-bold.woff      |  Bin 0 -> 31224 bytes
 website/assets/fonts/hkgrotesk-bold.woff2     |  Bin 0 -> 24676 bytes
 .../assets/fonts/hkgrotesk-bolditalic.woff    |  Bin 0 -> 33256 bytes
 .../assets/fonts/hkgrotesk-bolditalic.woff2   |  Bin 0 -> 26248 bytes
 website/assets/fonts/hkgrotesk-semibold.woff  |  Bin 0 -> 31812 bytes
 website/assets/fonts/hkgrotesk-semibold.woff2 |  Bin 0 -> 25092 bytes
 .../fonts/hkgrotesk-semibolditalic.woff       |  Bin 0 -> 34196 bytes
 .../fonts/hkgrotesk-semibolditalic.woff2      |  Bin 0 -> 26908 bytes
 .../assets/fonts/sourcecodepro-semibold.eot   |  Bin 24976 -> 0 bytes
 .../assets/fonts/sourcecodepro-semibold.svg   |  244 ----
 .../assets/fonts/sourcecodepro-semibold.ttf   |  Bin 55544 -> 0 bytes
 .../assets/fonts/sourcecodepro-semibold.woff  |  Bin 27952 -> 0 bytes
 website/assets/fonts/sourcesanspro-bold.eot   |  Bin 89026 -> 0 bytes
 website/assets/fonts/sourcesanspro-bold.svg   | 1031 ----------------
 website/assets/fonts/sourcesanspro-bold.ttf   |  Bin 88820 -> 0 bytes
 website/assets/fonts/sourcesanspro-bold.woff  |  Bin 33612 -> 0 bytes
 website/assets/fonts/sourcesanspro-bold.woff2 |  Bin 26800 -> 0 bytes
 .../assets/fonts/sourcesanspro-bolditalic.eot |  Bin 55994 -> 0 bytes
 .../assets/fonts/sourcesanspro-bolditalic.svg |  840 -------------
 .../assets/fonts/sourcesanspro-bolditalic.ttf |  Bin 55764 -> 0 bytes
 .../fonts/sourcesanspro-bolditalic.woff       |  Bin 28960 -> 0 bytes
 .../fonts/sourcesanspro-bolditalic.woff2      |  Bin 22492 -> 0 bytes
 website/assets/fonts/sourcesanspro-italic.eot |  Bin 56574 -> 0 bytes
 website/assets/fonts/sourcesanspro-italic.svg |  852 --------------
 website/assets/fonts/sourcesanspro-italic.ttf |  Bin 56360 -> 0 bytes
 .../assets/fonts/sourcesanspro-italic.woff    |  Bin 28844 -> 0 bytes
 .../assets/fonts/sourcesanspro-italic.woff2   |  Bin 22488 -> 0 bytes
 .../assets/fonts/sourcesanspro-regular.eot    |  Bin 90326 -> 0 bytes
 .../assets/fonts/sourcesanspro-regular.svg    | 1039 -----------------
 .../assets/fonts/sourcesanspro-regular.ttf    |  Bin 90112 -> 0 bytes
 .../assets/fonts/sourcesanspro-regular.woff   |  Bin 34096 -> 0 bytes
 .../assets/fonts/sourcesanspro-regular.woff2  |  Bin 27292 -> 0 bytes
 32 files changed, 4006 deletions(-)
 create mode 100755 website/assets/fonts/hkgrotesk-bold.woff
 create mode 100755 website/assets/fonts/hkgrotesk-bold.woff2
 create mode 100755 website/assets/fonts/hkgrotesk-bolditalic.woff
 create mode 100755 website/assets/fonts/hkgrotesk-bolditalic.woff2
 create mode 100755 website/assets/fonts/hkgrotesk-semibold.woff
 create mode 100755 website/assets/fonts/hkgrotesk-semibold.woff2
 create mode 100755 website/assets/fonts/hkgrotesk-semibolditalic.woff
 create mode 100755 website/assets/fonts/hkgrotesk-semibolditalic.woff2
 delete mode 100644 website/assets/fonts/sourcecodepro-semibold.eot
 delete mode 100644 website/assets/fonts/sourcecodepro-semibold.svg
 delete mode 100644 website/assets/fonts/sourcecodepro-semibold.ttf
 delete mode 100644 website/assets/fonts/sourcecodepro-semibold.woff
 delete mode 100644 website/assets/fonts/sourcesanspro-bold.eot
 delete mode 100644 website/assets/fonts/sourcesanspro-bold.svg
 delete mode 100644 website/assets/fonts/sourcesanspro-bold.ttf
 delete mode 100644 website/assets/fonts/sourcesanspro-bold.woff
 delete mode 100644 website/assets/fonts/sourcesanspro-bold.woff2
 delete mode 100644 website/assets/fonts/sourcesanspro-bolditalic.eot
 delete mode 100644 website/assets/fonts/sourcesanspro-bolditalic.svg
 delete mode 100644 website/assets/fonts/sourcesanspro-bolditalic.ttf
 delete mode 100644 website/assets/fonts/sourcesanspro-bolditalic.woff
 delete mode 100644 website/assets/fonts/sourcesanspro-bolditalic.woff2
 delete mode 100644 website/assets/fonts/sourcesanspro-italic.eot
 delete mode 100644 website/assets/fonts/sourcesanspro-italic.svg
 delete mode 100644 website/assets/fonts/sourcesanspro-italic.ttf
 delete mode 100644 website/assets/fonts/sourcesanspro-italic.woff
 delete mode 100644 website/assets/fonts/sourcesanspro-italic.woff2
 delete mode 100644 website/assets/fonts/sourcesanspro-regular.eot
 delete mode 100644 website/assets/fonts/sourcesanspro-regular.svg
 delete mode 100644 website/assets/fonts/sourcesanspro-regular.ttf
 delete mode 100644 website/assets/fonts/sourcesanspro-regular.woff
 delete mode 100644 website/assets/fonts/sourcesanspro-regular.woff2

diff --git a/website/assets/fonts/hkgrotesk-bold.woff b/website/assets/fonts/hkgrotesk-bold.woff
new file mode 100755
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From a8ff8423bb2ecc0b6da3536aeddcf261d80be00c Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 14:17:41 +0200
Subject: [PATCH 162/213] Update image assets, icons and SVGs

Move SVG sprite to Jade file and include in template. Only use SVG
symbols for logos.
---
 website/_includes/_svg.jade                   | 157 ++++++++++++++++++
 website/_layout.jade                          |   1 +
 .../assets/img/{docs => }/architecture.svg    |  34 ++--
 .../img/{docs => }/displacy_jupyter.jpg       | Bin
 website/assets/img/graphics.svg               |  84 ----------
 website/assets/img/icons.svg                  |  43 -----
 .../assets/img/{docs => }/language_data.svg   |  31 ++--
 website/assets/img/logo.svg                   |   2 +-
 website/assets/img/logos/chartbeat.png        | Bin 2761 -> 0 bytes
 website/assets/img/logos/chattermill.png      | Bin 6351 -> 0 bytes
 website/assets/img/logos/cytora.png           | Bin 3028 -> 0 bytes
 website/assets/img/logos/duedil.png           | Bin 4254 -> 0 bytes
 website/assets/img/logos/indico.png           | Bin 1668 -> 0 bytes
 website/assets/img/logos/kip.png              | Bin 1770 -> 0 bytes
 website/assets/img/logos/quora.png            | Bin 4705 -> 0 bytes
 website/assets/img/logos/signaln.png          | Bin 3321 -> 0 bytes
 website/assets/img/logos/socrata.png          | Bin 5121 -> 0 bytes
 website/assets/img/logos/stitchfix.png        | Bin 1993 -> 0 bytes
 website/assets/img/logos/synapsify.png        | Bin 2532 -> 0 bytes
 website/assets/img/logos/turi.png             | Bin 2915 -> 0 bytes
 website/assets/img/logos/wayblazer.png        | Bin 2306 -> 0 bytes
 website/assets/img/logos/wonderflow.png       | Bin 2626 -> 0 bytes
 website/assets/img/pattern_green.jpg          | Bin 221731 -> 232481 bytes
 website/assets/img/{docs => }/pipeline.svg    |   6 +-
 .../{showcase => resources}/displacy-ent.jpg  | Bin
 .../img/{showcase => resources}/displacy.jpg  | Bin
 website/assets/img/resources/neuralcoref.jpg  | Bin 0 -> 31380 bytes
 .../img/{showcase => resources}/sense2vec.jpg | Bin
 website/assets/img/showcase/foxtype.jpg       | Bin 27435 -> 0 bytes
 website/assets/img/showcase/indico.jpg        | Bin 33559 -> 0 bytes
 website/assets/img/showcase/kip.jpg           | Bin 28937 -> 0 bytes
 website/assets/img/showcase/laice.jpg         | Bin 9263 -> 0 bytes
 website/assets/img/showcase/textanalysis.jpg  | Bin 16789 -> 0 bytes
 website/assets/img/showcase/truthbot.jpg      | Bin 24521 -> 0 bytes
 website/assets/img/social/preview_101.jpg     | Bin 388748 -> 383381 bytes
 website/assets/img/social/preview_alpha.jpg   | Bin 382786 -> 386720 bytes
 website/assets/img/social/preview_docs.jpg    | Bin 258021 -> 0 bytes
 .../assets/img/{docs => }/tokenization.svg    |   4 +-
 .../assets/img/{docs => }/training-loop.svg   |   4 +-
 website/assets/img/{docs => }/training.svg    |   4 +-
 .../img/{docs => }/vocab_stringstore.svg      |   8 +-
 41 files changed, 208 insertions(+), 170 deletions(-)
 create mode 100644 website/_includes/_svg.jade
 rename website/assets/img/{docs => }/architecture.svg (91%)
 rename website/assets/img/{docs => }/displacy_jupyter.jpg (100%)
 delete mode 100644 website/assets/img/graphics.svg
 delete mode 100644 website/assets/img/icons.svg
 rename website/assets/img/{docs => }/language_data.svg (88%)
 delete mode 100644 website/assets/img/logos/chartbeat.png
 delete mode 100644 website/assets/img/logos/chattermill.png
 delete mode 100644 website/assets/img/logos/cytora.png
 delete mode 100644 website/assets/img/logos/duedil.png
 delete mode 100644 website/assets/img/logos/indico.png
 delete mode 100644 website/assets/img/logos/kip.png
 delete mode 100644 website/assets/img/logos/quora.png
 delete mode 100644 website/assets/img/logos/signaln.png
 delete mode 100644 website/assets/img/logos/socrata.png
 delete mode 100644 website/assets/img/logos/stitchfix.png
 delete mode 100644 website/assets/img/logos/synapsify.png
 delete mode 100644 website/assets/img/logos/turi.png
 delete mode 100644 website/assets/img/logos/wayblazer.png
 delete mode 100644 website/assets/img/logos/wonderflow.png
 rename website/assets/img/{docs => }/pipeline.svg (92%)
 rename website/assets/img/{showcase => resources}/displacy-ent.jpg (100%)
 rename website/assets/img/{showcase => resources}/displacy.jpg (100%)
 create mode 100644 website/assets/img/resources/neuralcoref.jpg
 rename website/assets/img/{showcase => resources}/sense2vec.jpg (100%)
 delete mode 100644 website/assets/img/showcase/foxtype.jpg
 delete mode 100644 website/assets/img/showcase/indico.jpg
 delete mode 100644 website/assets/img/showcase/kip.jpg
 delete mode 100644 website/assets/img/showcase/laice.jpg
 delete mode 100644 website/assets/img/showcase/textanalysis.jpg
 delete mode 100644 website/assets/img/showcase/truthbot.jpg
 delete mode 100644 website/assets/img/social/preview_docs.jpg
 rename website/assets/img/{docs => }/tokenization.svg (98%)
 rename website/assets/img/{docs => }/training-loop.svg (95%)
 rename website/assets/img/{docs => }/training.svg (95%)
 rename website/assets/img/{docs => }/vocab_stringstore.svg (94%)

diff --git a/website/_includes/_svg.jade b/website/_includes/_svg.jade
new file mode 100644
index 000000000..f9d7a2b53
--- /dev/null
+++ b/website/_includes/_svg.jade
@@ -0,0 +1,157 @@
+//- 💫 INCLUDES > SVG
+
+svg(style="position: absolute; visibility: hidden; width: 0; height: 0;" width="0" height="0" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink")
+    defs
+        //- UI icons
+
+        symbol#svg_github(viewBox="0 0 27 32")
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+
+        symbol#svg_circle(viewBox="0 0 18 18")
+            ellipse(rx="9" ry="9" cx="9" cy="9")
+
+        symbol#svg_chat(viewBox="0 0 30 30")
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+
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+        symbol#svg_reject(viewBox="0 0 24 24")
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+
+        symbol#svg_accept(viewBox="0 0 24 24")
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+
+        symbol#svg_markdown(viewBox="0 0 32 32")
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+
+        symbol#svg_course(viewBox="0 0 20 20")
+            path(d="M3.302 12.238c0.464 1.879 1.054 2.701 3.022 3.562 1.969 0.86 2.904 1.8 3.676 1.8s1.648-0.822 3.616-1.684c1.969-0.861 1.443-1.123 1.907-3.002l-5.523 2.686-6.698-3.362zM19.511 7.336l-8.325-4.662c-0.652-0.365-1.72-0.365-2.372 0l-8.326 4.662c-0.652 0.365-0.652 0.963 0 1.328l8.325 4.662c0.652 0.365 1.72 0.365 2.372 0l5.382-3.014-5.836-1.367c-0.225 0.055-0.472 0.086-0.731 0.086-1.052 0-1.904-0.506-1.904-1.131 0-0.627 0.853-1.133 1.904-1.133 0.816 0 1.51 0.307 1.78 0.734l6.182 2.029 1.549-0.867c0.651-0.364 0.651-0.962 0-1.327zM16.967 16.17c-0.065 0.385 1.283 1.018 1.411-0.107 0.579-5.072-0.416-6.531-0.416-6.531l-1.395 0.781c0-0.001 1.183 1.125 0.4 5.857z")
+
+        symbol#svg_jupyter(viewBox="245 20 270 270")
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+
+
+        //- Logos
+
+        symbol#svg_spacy(viewBox="0 0 675 215")
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+
+        symbol#svg_explosion(viewBox="0 0 500 500")
+            path(fill="currentColor" d="M111.7 74.9L91.2 93.1l9.1 10.2 17.8-15.8 7.4 8.4-17.8 15.8 10.1 11.4 20.6-18.2 7.7 8.7-30.4 26.9-41.9-47.3 30.3-26.9 7.6 8.6zM190.8 59.6L219 84.3l-14.4 4.5-20.4-18.2-6.4 26.6-14.4 4.5 8.9-36.4-26.9-24.1 14.3-4.5L179 54.2l5.7-25.2 14.3-4.5-8.2 35.1zM250.1 21.2l27.1 3.4c6.1.8 10.8 3.1 14 7.2 3.2 4.1 4.5 9.2 3.7 15.5-.8 6.3-3.2 11-7.4 14.1-4.1 3.1-9.2 4.3-15.3 3.5L258 63.2l-2.8 22.3-13-1.6 7.9-62.7zm11.5 13l-2.2 17.5 12.6 1.6c5.1.6 9.1-2 9.8-7.6.7-5.6-2.5-9.2-7.6-9.9l-12.6-1.6zM329.1 95.4l23.8 13.8-5.8 10L312 98.8l31.8-54.6 11.3 6.6-26 44.6zM440.5 145c-1.3 8.4-5.9 15.4-13.9 21.1s-16.2 7.7-24.6 6.1c-8.4-1.6-15.3-6.3-20.8-14.1-5.5-7.9-7.6-16-6.4-24.4 1.3-8.5 6-15.5 14-21.1 8-5.6 16.2-7.7 24.5-6 8.4 1.6 15.4 6.3 20.9 14.2 5.5 7.6 7.6 15.7 6.3 24.2zM412 119c-5.1-.8-10.3.6-15.6 4.4-5.2 3.7-8.4 8.1-9.4 13.2-1 5.2.2 10.1 3.5 14.8 3.4 4.8 7.5 7.5 12.7 8.2 5.2.8 10.4-.7 15.6-4.4 5.3-3.7 8.4-8.1 9.4-13.2 1.1-5.1-.1-9.9-3.4-14.7-3.4-4.8-7.6-7.6-12.8-8.3zM471.5 237.9c-2.8 4.8-7.1 7.6-13 8.7l-2.6-13.1c5.3-.9 8.1-5 7.2-11-.9-5.8-4.3-8.8-8.9-8.2-2.3.3-3.7 1.4-4.5 3.3-.7 1.9-1.4 5.2-1.7 10.1-.8 7.5-2.2 13.1-4.3 16.9-2.1 3.9-5.7 6.2-10.9 7-6.3.9-11.3-.5-15.2-4.4-3.9-3.8-6.3-9-7.3-15.7-1.1-7.4-.2-13.7 2.6-18.8 2.8-5.1 7.4-8.2 13.7-9.2l2.6 13c-5.6 1.1-8.7 6.6-7.7 13.4 1 6.6 3.9 9.5 8.6 8.8 4.4-.7 5.7-4.5 6.7-14.1.3-3.5.7-6.2 1.1-8.4.4-2.2 1.2-4.4 2.2-6.8 2.1-4.7 6-7.2 11.8-8.1 5.4-.8 10.3.4 14.5 3.7 4.2 3.3 6.9 8.5 8 15.6.9 6.9-.1 12.6-2.9 17.3zM408.6 293.5l2.4-12.9 62 11.7-2.4 12.9-62-11.7zM419.6 396.9c-8.3 2-16.5.3-24.8-5-8.2-5.3-13.2-12.1-14.9-20.5-1.6-8.4.1-16.6 5.3-24.6 5.2-8.1 11.9-13.1 20.2-15.1 8.4-1.9 16.6-.3 24.9 5 8.2 5.3 13.2 12.1 14.8 20.5 1.7 8.4 0 16.6-5.2 24.7-5.2 8-12 13-20.3 15zm13.4-36.3c-1.2-5.1-4.5-9.3-9.9-12.8s-10.6-4.7-15.8-3.7-9.3 4-12.4 8.9-4.1 9.8-2.8 14.8c1.2 5.1 4.5 9.3 9.9 12.8 5.5 3.5 10.7 4.8 15.8 3.7 5.1-.9 9.2-3.8 12.3-8.7s4.1-9.9 2.9-15zM303.6 416.5l9.6-5.4 43.3 20.4-19.2-34 11.4-6.4 31 55-9.6 5.4-43.4-20.5 19.2 34.1-11.3 6.4-31-55zM238.2 468.8c-49 0-96.9-17.4-134.8-49-38.3-32-64-76.7-72.5-125.9-2-11.9-3.1-24-3.1-35.9 0-36.5 9.6-72.6 27.9-104.4 2.1-3.6 6.7-4.9 10.3-2.8 3.6 2.1 4.9 6.7 2.8 10.3-16.9 29.5-25.9 63.1-25.9 96.9 0 11.1 1 22.3 2.9 33.4 7.9 45.7 31.8 87.2 67.3 116.9 35.2 29.3 79.6 45.5 125.1 45.5 11.1 0 22.3-1 33.4-2.9 4.1-.7 8 2 8.7 6.1.7 4.1-2 8-6.1 8.7-11.9 2-24 3.1-36 3.1z")
+
+
+        //- Machine learning & NLP libraries
+
+        symbol#svg_tensorflow(viewBox="0 0 31 33")
+            path(d="M17.3 5v5l8.7 5v-5zM0 10v5l4.3 2.5v-5zm13 2.5L8.7 15v15l4.3 2.5v-10l4.3 2.5v-5L13 17.5z" fill="#e55b2d")
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+            path(d="M17.3 0L0 10l4.3 2.5 13-7.5 8.7 5 4.3-2.5zm0 15L13 17.5l4.3 2.5 4.4-2.5z" fill="#f8bf3c")
+
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+
+
+        //- spaCy users
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+            path(d="M208 0v44.4c-3.5 0-6.5.4-9.4-.1-4.1-.8-5.5.9-6.6 4.5-13.9 45-28 89.9-42 134.8-3.2 10.3-6.3 20.7-9.8 30.9-.5 1.4-2.5 3.3-3.8 3.3-22.5.2-45 .1-67.8.1-.5-1.4-1.1-2.7-1.6-4.1-17.4-55-34.8-110.1-52-165.1-1.2-3.7-2.7-5.1-6.7-4.5-2.6.5-5.5-.1-8.3-.2V0h94v44.3H74.9c10.5 41.1 20.9 81.7 31.3 122.3.3 0 .6.1 1 .1 11.2-40.6 22.4-81.3 33.8-122.5h-18.9V0H208z M356 58.3h63.2c.6 7.4 1.2 14.7 1.9 22.2 3.8-4.4 7-8.9 11-12.4 17.9-15.4 38.5-18.4 60.2-10.4 16.4 6.1 23.4 19.6 23.7 36.5.4 24.1.2 48.3.2 72.5v6.6l12.9.6v43.7h-70.8V212v-92.5c0-8.4-2.9-12.7-9.3-14.8-6.7-2.2-13.6 0-18.2 6-1.1 1.4-1.9 3.1-2.7 4.8-.5 1.2-1 2.6-1 3.8-.1 17.9 0 35.8 0 54.2h9.7v44.1H356v-43.9h12.3v-70.8h-12.2c-.1-15.2-.1-29.7-.1-44.6zM741.9 102.4h-10.8v-44c.8-.1 1.6-.3 2.4-.3h66.6v115.6H813v43.9h-65.5v-16.5c-2.9 3.1-4.9 5.4-7.2 7.5-15.9 14.1-43.9 17.9-62.9 8.3-14.5-7.3-21.7-19.7-22.3-35.4-.9-24.3-.7-48.6-1-72.9v-6.3h-12.7v-44H712v5.6c0 29.3-.1 58.6.1 88 0 4.1.7 8.3 2 12.2 2 5.9 7 8.9 13.2 8.7 6.1-.2 10.5-3.1 12.6-8.8.8-2.2 1.7-4.5 1.7-6.7.4-18.1.3-36.3.3-54.9z M345.7 149h-98.5c-.2 9.1.1 17.6 4.5 25.4 3.6 6.5 9.6 8.9 16.8 8.6 7.2-.3 12.9-3.3 15.9-10.1 1.3-3 2.1-6.2 3.3-9.6h54.6c-2.2 17.5-8.9 32.3-22.9 43.3-9.9 7.8-21.4 12-33.8 13.8-16.7 2.5-33.2 1.8-49.4-3.4-21.7-7-36.3-21.4-43-43-7.3-23.3-7.6-47 .1-70.3 9.4-28.7 30.1-44.2 59.5-48.6 13.2-2 26.3-1.1 39.1 2.4 29.9 8.1 45.9 28.7 50.8 58.4 1.8 10.6 2 21.5 3 33.1zm-96.9-30.8H287c.5-8.5-.7-16.1-8.2-20.9-6.8-4.3-14.3-4.7-21.2-.4-7.7 4.9-8.7 12.8-8.8 21.3zM1114 148.9h-98.2c-.2 9-.2 17.6 4.3 25.4 3.8 6.7 9.9 9.1 17.3 8.7 7.4-.4 13.1-3.8 15.9-10.9 1.1-2.8 1.8-5.7 2.8-8.8h54.7c-3.5 32.1-26 53.9-59.4 57.6-15.6 1.7-30.9 1-46-3.7-22.3-7-37.2-21.7-44-44-6.9-23-7.2-46.3.3-69.3 9.5-28.9 30.3-44.5 59.9-48.8 13.9-2 27.6-.9 41 3.1 27.5 8.3 43 27.6 48.1 55.2 2.1 11.4 2.2 23.1 3.3 35.5zm-96.4-30.8h38c.1-16-7.7-24.9-20.6-23.9-11.9.9-19.2 11-17.4 23.9z M535.6 58h18c0-10.6.4-20.9-.1-31.2-.3-5.4 1.5-7.4 6.8-8.5 15.2-3.1 30.2-6.7 46-10.3v50h25.6v44.7h-25c-.2 1.8-.4 3.3-.4 4.7v51.5c0 1.8.2 3.7.4 5.5 1.3 9.8 8.2 14.9 18 13.3 1.6-.3 3.2-.6 5.6-1v27.7c0 12.9 0 12.9-12.7 14.9-13.6 2.2-27.1 2.9-40.7-.3-19.1-4.6-27.8-15.5-27.9-35.3V103h-13.7c.1-15.3.1-29.8.1-45zM826.2 217.6v-43.9h12.7v-70.9h-12.6V58.3h62.1l1.9 25.3 2-4.4c5.1-12.9 14.4-20.7 28.3-22.2 6.7-.7 13.6-.1 20.3.3 1.2.1 3.4 2 3.4 3.1.2 15.8 0 31.6 0 47.5 0 .3-.3.6-.6 1.1-7.6 0-15.5-1-23.1.2-16.2 2.6-23.8 12-24.5 28.5-.2 5.8-.2 11.7-.3 17.5v18.2h18v44.3c-29.1-.1-58.1-.1-87.6-.1z")
+            path(fill="#ED1E25" d="M1237 .3c8.5 1.4 17.1 2.2 25.4 4.3 34.3 8.6 51.7 50.6 33.5 80.3-4.4 7.2-10.5 12.4-17.7 16.5-3.2 1.8-6.4 3.5-10.3 5.5 2 .8 3.4 1.6 4.9 2 23.7 6.9 34.2 24.4 35.9 47.6 2.4 31.9-17.7 55.7-49.6 59.6-9.9 1.2-19.9 1.9-29.9 1.9-31.7.2-63.3.1-95 .1h-5.8v-43.8h18.9V44.4H1128V.2c36.3.1 72.7.1 109 .1zm-32.3 128.8c0 14.9-.1 28.5.1 42.2 0 .9 2 2.7 3 2.7 8.3 0 16.7 0 24.9-.7 6.1-.5 11.7-2.8 15.1-8.4 8-13.2.4-31.6-14.7-34.2-9-1.6-18.4-1.1-28.4-1.6zm.2-40.5c8.7-.5 16.9-.2 24.8-1.6 9.6-1.7 16.2-11 16.3-21.2 0-10.2-5.9-19.7-14.7-21.3-8.5-1.5-17.4-1.4-26.4-2v46.1z M1743 103.3c-7.5-.1-15-.4-22.4-.2-1.1 0-3.2 1.9-3.2 3-.2 18.8-.6 37.7.1 56.5.4 12.3 7.9 17.4 20 15.2 1-.2 2-.2 3.2-.3.2 1.2.5 2.3.5 3.4 0 10.8 0 21.7.1 32.5 0 2.4-.3 4.2-3.1 4.7-16.5 2.7-32.9 5.1-49.6 1.2-18.7-4.4-27.7-14.3-28.1-33.4-.5-25.5-.2-51-.3-76.5V103h-6.4c-8.3-.1-7.3.9-7.4-7.6V58.5h18.4c0-10.1-.1-19.8 0-29.4.1-10.6-1.5-8.2 8.7-10.7 14.2-3.4 28.5-6.5 43.5-10v49.9h26v45z M1569.2 119.2c0-5.4.3-10-.1-14.6-.6-8.5-6.1-14.1-13.8-14.3-7.7-.2-14.1 5.5-15.3 13.7-.3 1.8-.3 3.6-.5 5.8h-53.3c-1.9-20.2 8.6-38.7 28.2-47.2 28.5-12.3 57.2-11.2 85.1 2.2 17.1 8.2 25.9 22.7 26.2 41.7.4 20.3.2 40.7.3 61v6.6h12.8v43.8h-66.2c-.5-5.4-1-11-1.6-17.4-1.5 1.7-2.5 2.7-3.4 3.8-17.3 21.3-50.3 21.2-67.2 11.3-13.4-7.9-19.2-20.5-20.1-35.4-2-32.6 15.1-53.7 48.1-58.7 11.6-1.8 23.5-1.6 35.3-2.3 1.6-.1 3.2 0 5.5 0zm.7 28.2c-5.4 0-9.7-.6-13.9.1-12.9 2.1-19.5 11.1-18.1 24.1 1.2 10.7 10.4 16.1 20.3 11.9 5.3-2.2 8.9-6.3 9.7-11.8 1.2-7.9 1.4-16 2-24.3z M1475.6 149.2h-98.5c0 9.7.1 18.9 5.6 27 4.2 6.2 10.6 7.7 17.6 7 6.8-.7 11.9-4.1 14.6-10.5 1.2-2.7 1.8-5.7 2.8-9h54.4c-2.2 17.5-8.9 32.5-23.3 43.3-17 12.8-36.8 15.8-57.3 14.4-8.4-.5-16.9-2-25-4.5-21.4-6.5-36-20.6-42.8-41.9-8-25-8.2-50.2 1.1-74.9 10.3-27.1 31.1-41 59.2-44.8 13.7-1.8 27.3-.7 40.5 3.4 28.2 8.7 43.2 28.8 47.9 57 2.1 10.8 2.3 21.8 3.2 33.5zm-58.1-30.5c.1-9-.9-17.2-9.5-21.8-7.3-3.9-14.9-4-21.6 1.2-6.6 5.1-7.8 12.5-7.3 20.6h38.4z")
+
+        symbol#svg_microsoft(viewBox="0 0 609 130")
+            path(fill="#737373" d="M213.2 74.3l-3.6 10.2h-.3c-.6-2.3-1.7-5.8-3.5-10L186.5 26h-18.9v77.3h12.5V55.6c0-3 0-6.4-.1-10.6-.1-2.1-.3-3.7-.4-4.9h.3c.6 3 1.3 5.2 1.8 6.6l23.2 56.4h8.8l23-56.9c.5-1.3 1-3.9 1.5-6.1h.3c-.3 5.7-.5 10.8-.6 13.9v49h13.3V25.8H233l-19.8 48.5zm50.6-26.7h13V103h-13zm6.6-23.4c-2.2 0-4 .8-5.5 2.2-1.5 1.4-2.3 3.2-2.3 5.4 0 2.1.8 3.9 2.3 5.3 1.5 1.4 3.3 2.1 5.5 2.1s4.1-.8 5.5-2.1c1.5-1.4 2.3-3.2 2.3-5.3s-.8-3.9-2.3-5.4c-1.3-1.4-3.2-2.2-5.5-2.2m52.5 22.9c-2.4-.5-4.9-.8-7.3-.8-5.9 0-11.3 1.3-15.8 3.9-4.5 2.6-8.1 6.2-10.4 10.7-2.4 4.6-3.6 9.9-3.6 16 0 5.3 1.2 10 3.5 14.3 2.3 4.2 5.5 7.6 9.8 9.9 4.1 2.3 8.9 3.5 14.3 3.5 6.2 0 11.5-1.3 15.7-3.7l.1-.1v-12l-.5.4c-1.9 1.4-4.1 2.6-6.3 3.3-2.3.8-4.4 1.2-6.2 1.2-5.2 0-9.3-1.5-12.2-4.8-3-3.2-4.5-7.6-4.5-13.1 0-5.7 1.5-10.2 4.6-13.5 3.1-3.3 7.2-5 12.2-5 4.2 0 8.5 1.4 12.4 4.2l.5.4V49.2l-.1-.1c-1.7-.7-3.6-1.5-6.2-2m42.9-.4c-3.2 0-6.2 1-8.8 3.1-2.2 1.8-3.7 4.4-5 7.5h-.1v-9.7h-13V103h13V74.7c0-4.8 1-8.8 3.2-11.7 2.2-3 5-4.5 8.4-4.5 1.2 0 2.4.3 3.9.5 1.4.4 2.4.8 3.1 1.3l.5.4v-13l-.3-.1c-.9-.6-2.7-.9-4.9-.9m35.4-.3c-9.1 0-16.4 2.7-21.5 8-5.2 5.3-7.7 12.6-7.7 21.8 0 8.6 2.6 15.6 7.6 20.7 5 5 11.8 7.6 20.3 7.6 8.9 0 16-2.7 21.1-8.1 5.2-5.4 7.7-12.6 7.7-21.5 0-8.8-2.4-15.8-7.3-20.9-4.7-5.1-11.6-7.6-20.2-7.6M411.6 89c-2.4 3.1-6.2 4.6-10.9 4.6s-8.5-1.5-11.2-4.8c-2.7-3.1-4-7.6-4-13.3 0-5.9 1.4-10.4 4-13.6 2.7-3.2 6.4-4.8 11.1-4.8 4.6 0 8.2 1.5 10.8 4.6 2.6 3.1 4 7.6 4 13.5-.2 6-1.3 10.7-3.8 13.8m46.1-18.4c-4.1-1.7-6.7-3-7.9-4.1-1-1-1.5-2.4-1.5-4.2 0-1.5.6-3 2.1-4s3.2-1.5 5.7-1.5c2.2 0 4.5.4 6.7 1s4.2 1.5 5.8 2.7l.5.4V48.7l-.3-.1c-1.5-.6-3.5-1.2-5.9-1.7-2.4-.4-4.6-.6-6.4-.6-6.2 0-11.3 1.5-15.3 4.8-4 3.1-5.9 7.3-5.9 12.2 0 2.6.4 4.9 1.3 6.8.9 1.9 2.2 3.7 4 5.2 1.8 1.4 4.4 3 8 4.5 3 1.3 5.3 2.3 6.7 3.1 1.4.8 2.3 1.7 3 2.4.5.8.8 1.8.8 3.1 0 3.7-2.8 5.5-8.5 5.5-2.2 0-4.5-.4-7.2-1.3s-5.2-2.2-7.3-3.7l-.5-.4v12.7l.3.1c1.9.9 4.2 1.5 7 2.2 2.8.5 5.3.9 7.5.9 6.7 0 12.2-1.5 16.1-4.8 4-3.2 6.1-7.3 6.1-12.6 0-3.7-1-7-3.2-9.5-2.9-2.4-6.5-4.9-11.7-6.9m49.2-24.2c-9.1 0-16.4 2.7-21.5 8s-7.7 12.6-7.7 21.8c0 8.6 2.6 15.6 7.6 20.7 5 5 11.8 7.6 20.3 7.6 8.9 0 16-2.7 21.1-8.1 5.2-5.4 7.7-12.6 7.7-21.5 0-8.8-2.4-15.8-7.3-20.9-4.7-5.1-11.6-7.6-20.2-7.6M517.2 89c-2.4 3.1-6.2 4.6-10.9 4.6-4.8 0-8.5-1.5-11.2-4.8-2.7-3.1-4-7.6-4-13.3 0-5.9 1.4-10.4 4-13.6 2.7-3.2 6.4-4.8 11.1-4.8 4.5 0 8.2 1.5 10.8 4.6 2.6 3.1 4 7.6 4 13.5 0 6-1.3 10.7-3.8 13.8M603.9 58.3V47.6h-13.1V31.2l-.4.1L578 35l-.3.1v12.5h-19.6v-7c0-3.2.8-5.7 2.2-7.3s3.5-2.4 6.1-2.4c1.8 0 3.7.4 5.8 1.3l.5.3V21.2l-.3-.1c-1.8-.6-4.2-1-7.3-1-3.9 0-7.3.9-10.4 2.4-3.1 1.7-5.4 4-7.1 7.1-1.7 3-2.6 6.4-2.6 10.3v7.7h-9.1v10.6h9.1V103h13.1V58.3h19.6v28.5c0 11.7 5.5 17.6 16.5 17.6 1.8 0 3.7-.3 5.5-.6 1.9-.4 3.3-.9 4.1-1.3l.1-.1V91.7l-.5.4c-.8.5-1.5.9-2.7 1.2-1 .3-1.9.4-2.6.4-2.6 0-4.4-.6-5.7-2.1-1.2-1.4-1.8-3.7-1.8-7.1V58.3h13.3z")
+            path(fill="#F25022" d="M0 0h61.3v61.3H0z")
+            path(fill="#7FBA00" d="M67.7 0H129v61.3H67.7z")
+            path(fill="#00A4EF" d="M0 67.7h61.3V129H0z")
+            path(fill="#FFB900" d="M67.7 67.7H129V129H67.7z")
+
+
+    //- Filters etc.
+    defs
+        radialGradient#gradient_allenai1(cx="75.721" cy="20.894" r="11.05" gradientUnits="userSpaceOnUse")
+            stop(offset=".3" stop-color="#FDEA65")
+            stop(offset="1" stop-color="#FCB431")
+        radialGradient#gradient_allenai2(cx="75.4" cy="42.297" r="82.993" gradientUnits="userSpaceOnUse")
+            stop(offset="0" stop-color="#3FA9D0")
+            stop(offset="1" stop-color="#183A74")
diff --git a/website/_layout.jade b/website/_layout.jade
index 482af35fa..b198c8333 100644
--- a/website/_layout.jade
+++ b/website/_layout.jade
@@ -45,6 +45,7 @@ html(lang="en")
         link(href="/assets/css/style.css?v#{V_CSS}" rel="stylesheet")
 
     body
+        include _includes/_svg
         include _includes/_navigation
 
         if SECTION == "docs"
diff --git a/website/assets/img/docs/architecture.svg b/website/assets/img/architecture.svg
similarity index 91%
rename from website/assets/img/docs/architecture.svg
rename to website/assets/img/architecture.svg
index c1d12d79b..911aaec60 100644
--- a/website/assets/img/docs/architecture.svg
+++ b/website/assets/img/architecture.svg
@@ -1,9 +1,13 @@
 <svg class="o-svg" xmlns="http://www.w3.org/2000/svg" viewBox="-1 -1 907 737" width="906" height="746">
     <style>
-        .svg__architecture__text-large { fill: #1a1e23; font: 20px "Source Sans Pro", Tahoma, Helvetica, Arial, sans-serif }
-        .svg__architecture__text-medium { fill: #1a1e23; font: 17px "Source Sans Pro", Tahoma, Helvetica, Arial, sans-serif }
-        .svg__architecture__text-small { fill: #1a1e23; font: bold 14px "Source Sans Pro", Tahoma, Helvetica, Arial, sans-serif }
-        .svg__architecture__text-code {  fill: #1a1e23; font: 600 12px "Source Code Pro", Monaco, "Courier New", monospace }
+        .svg__architecture__text-large, .svg__architecture__text-medium, .svg__architecture__text-small {
+            font-family: Arial, sans-serif;
+            fill: #1a1e23
+        }
+        .svg__architecture__text-large { font-size: 18px; font-weight: bold }
+        .svg__architecture__text-medium { font-size: 15px }
+        .svg__architecture__text-small { font-size: 14px; font-weight: bold }
+        .svg__architecture__text-code {  font: 600 12px Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace }
     </style>
     <ellipse cx="404" cy="203" fill="#dae8fc" stroke="#09a3d5" stroke-width="2" rx="74.8" ry="49.8"/>
     <text class="svg__architecture__text-large" transform="translate(362.5 206.5)" width="81" height="40">Language</text>
@@ -14,37 +18,37 @@
     <path fill="none" stroke="#999" stroke-width="2" stroke-miterlimit="10" d="M457 434l100.5 80"/>
     <path fill="#999" stroke="#999" stroke-width="2" stroke-miterlimit="10" d="M562.3 517.6l-8.8-1.8 4-2 1-4.3z"/>
     <rect fill="#f6f6f6" transform="translate(424.5 462.5)" width="158" height="18"/>
-    <text class="svg__architecture__text-code" dy="1em" dx="0.5em" transform="translate(424.5 462.5)" width="158" height="18">nlp.vocab.morphology</text>
+    <text class="svg__architecture__text-code" dy="1em" dx="0.4em" transform="translate(424.5 462.5)" width="158" height="18">nlp.vocab.morphology</text>
     <ellipse cx="404" cy="399" fill="#d5e8d4" stroke="#82b366" stroke-width="2" rx="74.8" ry="49.8"/>
     <text class="svg__architecture__text-large" dy="1em" transform="translate(377.5 386.5)" width="51" height="22">Vocab</text>
     <path fill="none" stroke="#999" stroke-width="2" stroke-miterlimit="10" d="M404 253v87.8"/>
     <path fill="#999" stroke="#999" stroke-width="2" stroke-miterlimit="10" d="M404 346.8l-4-8 4 2 4-2z"/>
     <rect fill="#f6f6f6" transform="translate(364.5 285.5)" width="79" height="18" />
-    <text class="svg__architecture__text-code" dy="1em" dx="0.5em" transform="translate(364.5 285.5)" width="79" height="18">nlp.vocab</text>
+    <text class="svg__architecture__text-code" dy="1em" dx="0.4em" transform="translate(364.5 285.5)" width="79" height="18">nlp.vocab</text>
     <ellipse cx="743" cy="399" fill="#f5f5f5" stroke="#666" stroke-width="2" rx="74.8" ry="49.8"/>
     <text class="svg__architecture__text-large" transform="translate(694.5 386.5)" dy="1em" width="95" height="22">StringStore</text>
     <path fill="none" stroke="#999" stroke-width="2" stroke-miterlimit="10" d="M478 399h181.8"/>
     <path fill="#999" stroke="#999" stroke-width="2" stroke-miterlimit="10" d="M665.8 399l-8 4 2-4-2-4z"/>
     <rect fill="#f6f6f6" transform="translate(498.5 388.5)" width="137" height="18"/>
-    <text class="svg__architecture__text-code" dy="1em" dx="0.5em" transform="translate(498.5 388.5)" width="137" height="18">nlp.vocab.strings</text>
+    <text class="svg__architecture__text-code" dy="1em" dx="0.4em" transform="translate(498.5 388.5)" width="137" height="18">nlp.vocab.strings</text>
     <path fill="none" stroke="#999" stroke-width="2" stroke-miterlimit="10" d="M108 244l235.6 115.4"/>
     <path fill="#999" stroke="#999" stroke-width="2" stroke-miterlimit="10" d="M349 362h-9l3.6-2.6V355z"/>
     <rect fill="#f6f6f6" transform="translate(141.5 284.5)" width="151" height="18" />
-    <text class="svg__architecture__text-code" dy="1em" dx="0.5em" transform="translate(141.5 284.5)" width="151" height="18">nlp.tokenizer.vocab</text>
+    <text class="svg__architecture__text-code" dy="1em" dx="0.4em" transform="translate(141.5 284.5)" width="151" height="18">nlp.tokenizer.vocab</text>
     <path fill="#f8cecc" stroke="#b85450" stroke-width="2" stroke-miterlimit="10" d="M38.7 159.3H104l33 43.6-32.8 43.5H38.7L6 203z"/>
     <text class="svg__architecture__text-large" transform="translate(30.5 190.5)" dy="1em" width="80" height="22">Tokenizer</text>
     <path fill="none" stroke="#999" stroke-width="2" stroke-miterlimit="10" d="M329 203v-1H145.2"/>
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deleted file mode 100644
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diff --git a/website/assets/img/docs/language_data.svg b/website/assets/img/language_data.svg
similarity index 88%
rename from website/assets/img/docs/language_data.svg
rename to website/assets/img/language_data.svg
index 31e1a1b29..e24bb7809 100644
--- a/website/assets/img/docs/language_data.svg
+++ b/website/assets/img/language_data.svg
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-        .svg__langdata__text-tiny { fill: #1a1e23; font: bold 16px "Source Sans Pro", Tahoma, Helvetica, Arial, sans-serif }
+        .svg__langdata__text-large, .svg__langdata__text-small, .svg__langdata__text-tiny {
+            font-family: Arial, sans-serif;
+            fill: #1a1e23
+        }
+        .svg__langdata__text-large { font-size: 20px }
+        .svg__langdata__text-small, .svg__langdata__text-tiny { font-weight: bold; font-size: 15px;  }
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-    <text class="svg__langdata__text-large" transform="translate(634.5 410)" width="80" height="22">Tokenizer</text>
+    <text class="svg__langdata__text-large" transform="translate(630 410)" width="80" height="22">Tokenizer</text>
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diff --git a/website/assets/img/logo.svg b/website/assets/img/logo.svg
index fc776fb82..89b61e132 100644
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diff --git a/website/assets/img/showcase/sense2vec.jpg b/website/assets/img/resources/sense2vec.jpg
similarity index 100%
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diff --git a/website/assets/img/showcase/foxtype.jpg b/website/assets/img/showcase/foxtype.jpg
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diff --git a/website/assets/img/docs/tokenization.svg b/website/assets/img/tokenization.svg
similarity index 98%
rename from website/assets/img/docs/tokenization.svg
rename to website/assets/img/tokenization.svg
index f5b164725..9877e1a30 100644
--- a/website/assets/img/docs/tokenization.svg
+++ b/website/assets/img/tokenization.svg
@@ -1,7 +1,7 @@
 <svg class="o-svg" xmlns="http://www.w3.org/2000/svg" width="600" height="380" viewBox="-20 -10 550 400">
     <style>
-        .svg__tokenization__text { fill: #1a1e23; font: 18px "Source Sans Pro", Tahoma, Helvetica, Arial, sans-serif }
-        .svg__tokenization__text-small { fill: #fff; font: 600 13px "Source Code Pro", Monaco, "Courier New", monospace }
+        .svg__tokenization__text { fill: #1a1e23; font: 18px Arial, sans-serif }
+        .svg__tokenization__text-small { fill: #fff; font: 600 13px Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace }
     </style>
     <path fill="none" stroke="#c00" stroke-width="2" stroke-miterlimit="10" d="M71 39v12H16v11M71 39v12h20v11"/>
     <path fill="#f8cecc" stroke="#c00" stroke-width="2" d="M1 1h140v38.2H1z"/>
diff --git a/website/assets/img/docs/training-loop.svg b/website/assets/img/training-loop.svg
similarity index 95%
rename from website/assets/img/docs/training-loop.svg
rename to website/assets/img/training-loop.svg
index e670f816a..e883b36be 100644
--- a/website/assets/img/docs/training-loop.svg
+++ b/website/assets/img/training-loop.svg
@@ -1,7 +1,7 @@
 <svg xmlns="http://www.w3.org/2000/svg" width="612" height="280" viewBox="-10 -10 622 360">
     <style>
-        .svg__trainloop__text { fill: #1a1e23; font: 18px "Source Sans Pro", Tahoma, Helvetica, Arial, sans-serif }
-        .svg__trainloop__text-small { fill: #1a1e23; font: 16px "Source Sans Pro", Tahoma, Helvetica, Arial, sans-serif }
+        .svg__trainloop__text { fill: #1a1e23; font: 18px Arial, sans-serif }
+        .svg__trainloop__text-small { fill: #1a1e23; font: 16px Arial, sans-serif }
     </style>
     <path fill="none" stroke="#999" stroke-width="2" stroke-miterlimit="10" d="M121 264h31.8"/>
     <path fill="#999" stroke="#999" stroke-width="2" stroke-miterlimit="10" d="M158.8 264l-8 4 2-4-2-4z"/>
diff --git a/website/assets/img/docs/training.svg b/website/assets/img/training.svg
similarity index 95%
rename from website/assets/img/docs/training.svg
rename to website/assets/img/training.svg
index cd6b74f04..65f6de0b6 100644
--- a/website/assets/img/docs/training.svg
+++ b/website/assets/img/training.svg
@@ -1,7 +1,7 @@
 <svg class="o-svg" xmlns="http://www.w3.org/2000/svg" width="827" height="168" viewBox="-10 -10 837 178">
     <style>
-        .svg__training__text { fill: #1a1e23; font: 18px "Source Sans Pro", Tahoma, Helvetica, Arial, sans-serif }
-        .svg__training__text-code {  fill: #1a1e23; font: bold 16px "Source Code Pro", Monaco, "Courier New", monospace }
+        .svg__training__text { fill: #1a1e23; font: 18px Arial, sans-serif }
+        .svg__training__text-code {  fill: #1a1e23; font: bold 16px Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace }
     </style>
     <defs>
     <linearGradient id="a" x1="0%" x2="0%" y1="100%" y2="0%">
diff --git a/website/assets/img/docs/vocab_stringstore.svg b/website/assets/img/vocab_stringstore.svg
similarity index 94%
rename from website/assets/img/docs/vocab_stringstore.svg
rename to website/assets/img/vocab_stringstore.svg
index 119175247..b604041f2 100644
--- a/website/assets/img/docs/vocab_stringstore.svg
+++ b/website/assets/img/vocab_stringstore.svg
@@ -1,9 +1,9 @@
 <svg class="o-svg" xmlns="http://www.w3.org/2000/svg" viewBox="-10 -10 582 365" width="572" height="355">
     <style>
-        .svg__vocab__text { fill: #1a1e23; font: 18px "Source Sans Pro", Tahoma, Helvetica, Arial, sans-serif }
-        .svg__vocab__text-large { fill: #fff; font: bold 18px "Source Sans Pro", Tahoma, Helvetica, Arial, sans-serif; text-transform: uppercase }
-        .svg__vocab__text-box { fill: #fff; font: bold 12px "Source Code Pro", Monaco, "Courier New", monospace }
-        .svg__vocab__text-code {  fill: #1a1e23; font: bold 12px "Source Code Pro", Monaco, "Courier New", monospace }
+        .svg__vocab__text { fill: #1a1e23; font: 18px Arial, sans-serif }
+        .svg__vocab__text-large { fill: #fff; font: bold 18px Arial, sans-serif; text-transform: uppercase }
+        .svg__vocab__text-box { fill: #fff; font: bold 12px Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace }
+        .svg__vocab__text-code {  fill: #1a1e23; font: bold 12px Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace }
     </style>
     <rect width="570" height="88" x="1" y="135" fill="#d5e8d4" stroke="#82b366" stroke-width="2" rx="13.2" ry="13.2"/>
     <path fill="#f5f5f5" stroke="#666" stroke-width="2" d="M444 164h100v40H444z"/>

From 49b58d35fd60d15406a4f2fecea982fb3de82f9f Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 14:18:49 +0200
Subject: [PATCH 163/213] Update JavaScript

---
 website/_includes/_scripts.jade               |  53 ++-
 website/assets/js/chart.min.js                |  14 +
 website/assets/js/in-view.min.js              |   6 +
 website/assets/js/main.js                     | 329 +++++++++++++++++-
 website/assets/js/{prism.js => prism.min.js}  |   0
 .../js/{quickstart.js => quickstart.min.js}   |   0
 6 files changed, 371 insertions(+), 31 deletions(-)
 create mode 100644 website/assets/js/chart.min.js
 create mode 100644 website/assets/js/in-view.min.js
 rename website/assets/js/{prism.js => prism.min.js} (100%)
 rename website/assets/js/{quickstart.js => quickstart.min.js} (100%)

diff --git a/website/_includes/_scripts.jade b/website/_includes/_scripts.jade
index e5a863787..4bb4d87ef 100644
--- a/website/_includes/_scripts.jade
+++ b/website/_includes/_scripts.jade
@@ -1,27 +1,46 @@
 //- 💫 INCLUDES > SCRIPTS
 
-script(src="/assets/js/main.js?v#{V_JS}")
-script(src="/assets/js/prism.js")
+if quickstart
+        script(src="/assets/js/quickstart.min.js")
 
-if SECTION == "docs"
-    if quickstart
-        script(src="/assets/js/quickstart.js")
-        script var qs = new Quickstart("#qs")
+if IS_PAGE
+    script(src="/assets/js/in-view.min.js")
 
-    script.
-        ((window.gitter = {}).chat = {}).options = {
-            useStyles: false,
-            activationElement: '.js-gitter-button',
-            targetElement: '.js-gitter',
-            room: '!{SOCIAL.gitter}'
-        };
-
-    script(src="https://sidecar.gitter.im/dist/sidecar.v1.js" async defer)
+if HAS_MODELS
+    script(src="/assets/js/chart.min.js")
 
 if environment == "deploy"
-    script
+    script(async src="https://www.google-analytics.com/analytics.js")
+
+script(src="/assets/js/prism.min.js")
+script(src="/assets/js/main.js?v#{V_JS}")
+
+script
+    | new ProgressBar('.js-progress');
+
+    if changelog
+        | new Changelog('!{SOCIAL.github}', 'spacy');
+
+    if quickstart
+        | new Quickstart("#qs");
+
+    if IS_PAGE
+        | new SectionHighlighter('data-section', 'data-nav');
+        | new GitHubEmbed('!{SOCIAL.github}', 'data-gh-embed');
+        | ((window.gitter = {}).chat = {}).options = {
+        |     useStyles: false,
+        |     activationElement: '.js-gitter-button',
+        |     targetElement: '.js-gitter',
+        |     room: '!{SOCIAL.gitter}'
+        | };
+
+    if HAS_MODELS
+        | new ModelLoader('!{MODELS_REPO}', !{JSON.stringify(CURRENT_MODELS)}, !{JSON.stringify(MODEL_LICENSES)}, !{JSON.stringify(MODEL_ACCURACY)});
+
+    if environment == "deploy"
         | window.ga=window.ga||function(){
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+if IS_PAGE
+    script(src="https://sidecar.gitter.im/dist/sidecar.v1.js" async defer)
diff --git a/website/assets/js/chart.min.js b/website/assets/js/chart.min.js
new file mode 100644
index 000000000..184c23440
--- /dev/null
+++ b/website/assets/js/chart.min.js
@@ -0,0 +1,14 @@
+/*!
+ * Chart.js
+ * http://chartjs.org/
+ * Version: 2.6.0
+ *
+ * Copyright 2017 Nick Downie
+ * Released under the MIT license
+ * https://github.com/chartjs/Chart.js/blob/master/LICENSE.md
+ */
+!function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{var e;e="undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this,e.Chart=t()}}(function(){return function t(e,n,i){function a(r,l){if(!n[r]){if(!e[r]){var s="function"==typeof require&&require;if(!l&&s)return s(r,!0);if(o)return o(r,!0);var u=new Error("Cannot find module '"+r+"'");throw u.code="MODULE_NOT_FOUND",u}var d=n[r]={exports:{}};e[r][0].call(d.exports,function(t){var n=e[r][1][t];return a(n?n:t)},d,d.exports,t,e,n,i)}return n[r].exports}for(var o="function"==typeof require&&require,r=0;r<i.length;r++)a(i[r]);return a}({1:[function(t,e,n){},{}],2:[function(t,e,n){function i(t){if(t){var 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i=this,a=t._model,o=i.getValueScale(),r=o.getBasePixel(),l=o.isHorizontal(),s=i._ruler||i.getRuler(),u=i.calculateBarValuePixels(i.index,e),d=i.calculateBarIndexPixels(i.index,e,s);a.horizontal=l,a.base=n?r:u.base,a.x=l?n?r:u.head:d.center,a.y=l?d.center:n?r:u.head,a.height=l?d.size:void 0,a.width=l?void 0:d.size},getValueScaleId:function(){return this.getMeta().yAxisID},getIndexScaleId:function(){return this.getMeta().xAxisID},getValueScale:function(){return this.getScaleForId(this.getValueScaleId())},getIndexScale:function(){return this.getScaleForId(this.getIndexScaleId())},getStackCount:function(t){var e,n,i=this,a=i.chart,o=i.getIndexScale(),r=o.options.stacked,l=void 0===t?a.data.datasets.length:t+1,s=[];for(e=0;e<l;++e)n=a.getDatasetMeta(e),n.bar&&a.isDatasetVisible(e)&&(r===!1||r===!0&&s.indexOf(n.stack)===-1||void 0===r&&(void 0===n.stack||s.indexOf(n.stack)===-1))&&s.push(n.stack);return s.length},getStackIndex:function(t){return this.getStackCount(t)-1},getRuler:function(){var t=this,n=t.getIndexScale(),i=n.options,a=t.getStackCount(),o=n.isHorizontal()?n.width:n.height,r=o/n.ticks.length,l=r*i.categoryPercentage,s=l/a,u=s*i.barPercentage;return u=Math.min(e.getValueOrDefault(i.barThickness,u),e.getValueOrDefault(i.maxBarThickness,1/0)),{stackCount:a,tickSize:r,categorySize:l,categorySpacing:r-l,fullBarSize:s,barSize:u,barSpacing:s-u,scale:n}},calculateBarValuePixels:function(t,e){var n,i,a,o,r,l,s=this,u=s.chart,d=s.getMeta(),c=s.getValueScale(),h=u.data.datasets,f=Number(h[t].data[e]),g=c.options.stacked,p=d.stack,m=0;if(g||void 0===g&&void 0!==p)for(n=0;n<t;++n)i=u.getDatasetMeta(n),i.bar&&i.stack===p&&i.controller.getValueScaleId()===c.id&&u.isDatasetVisible(n)&&(a=Number(h[n].data[e]),(f<0&&a<0||f>=0&&a>0)&&(m+=a));return o=c.getPixelForValue(m),r=c.getPixelForValue(m+f),l=(r-o)/2,{size:l,base:o,head:r,center:r+l/2}},calculateBarIndexPixels:function(t,e,n){var i=this,a=n.scale,o=i.chart.isCombo,r=i.getStackIndex(t),l=a.getPixelForValue(null,e,t,o),s=n.barSize;return l-=o?n.tickSize/2:0,l+=n.fullBarSize*r,l+=n.categorySpacing/2,l+=n.barSpacing/2,{size:s,base:l,head:l+s,center:l+s/2}},draw:function(){var t,n=this,i=n.chart,a=n.getMeta().data,o=n.getDataset(),r=a.length,l=0;for(e.canvas.clipArea(i.ctx,i.chartArea);l<r;++l)t=o.data[l],null===t||void 0===t||isNaN(t)||a[l].draw();e.canvas.unclipArea(i.ctx)},setHoverStyle:function(t){var n=this.chart.data.datasets[t._datasetIndex],i=t._index,a=t.custom||{},o=t._model;o.backgroundColor=a.hoverBackgroundColor?a.hoverBackgroundColor:e.getValueAtIndexOrDefault(n.hoverBackgroundColor,i,e.getHoverColor(o.backgroundColor)),o.borderColor=a.hoverBorderColor?a.hoverBorderColor:e.getValueAtIndexOrDefault(n.hoverBorderColor,i,e.getHoverColor(o.borderColor)),o.borderWidth=a.hoverBorderWidth?a.hoverBorderWidth:e.getValueAtIndexOrDefault(n.hoverBorderWidth,i,o.borderWidth)},removeHoverStyle:function(t){var n=this.chart.data.datasets[t._datasetIndex],i=t._index,a=t.custom||{},o=t._model,r=this.chart.options.elements.rectangle;o.backgroundColor=a.backgroundColor?a.backgroundColor:e.getValueAtIndexOrDefault(n.backgroundColor,i,r.backgroundColor),o.borderColor=a.borderColor?a.borderColor:e.getValueAtIndexOrDefault(n.borderColor,i,r.borderColor),o.borderWidth=a.borderWidth?a.borderWidth:e.getValueAtIndexOrDefault(n.borderWidth,i,r.borderWidth)}}),t.defaults.horizontalBar={hover:{mode:"label"},scales:{xAxes:[{type:"linear",position:"bottom"}],yAxes:[{position:"left",type:"category",categoryPercentage:.8,barPercentage:.9,gridLines:{offsetGridLines:!0}}]},elements:{rectangle:{borderSkipped:"left"}},tooltips:{callbacks:{title:function(t,e){var n="";return t.length>0&&(t[0].yLabel?n=t[0].yLabel:e.labels.length>0&&t[0].index<e.labels.length&&(n=e.labels[t[0].index])),n},label:function(t,e){var n=e.datasets[t.datasetIndex].label||"";return n+": "+t.xLabel}}}},t.controllers.horizontalBar=t.controllers.bar.extend({getValueScaleId:function(){return this.getMeta().xAxisID},getIndexScaleId:function(){return this.getMeta().yAxisID}})}},{}],16:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers;t.defaults.bubble={hover:{mode:"single"},scales:{xAxes:[{type:"linear",position:"bottom",id:"x-axis-0"}],yAxes:[{type:"linear",position:"left",id:"y-axis-0"}]},tooltips:{callbacks:{title:function(){return""},label:function(t,e){var n=e.datasets[t.datasetIndex].label||"",i=e.datasets[t.datasetIndex].data[t.index];return n+": ("+t.xLabel+", "+t.yLabel+", "+i.r+")"}}}},t.controllers.bubble=t.DatasetController.extend({dataElementType:t.elements.Point,update:function(t){var n=this,i=n.getMeta(),a=i.data;e.each(a,function(e,i){n.updateElement(e,i,t)})},updateElement:function(n,i,a){var o=this,r=o.getMeta(),l=o.getScaleForId(r.xAxisID),s=o.getScaleForId(r.yAxisID),u=n.custom||{},d=o.getDataset(),c=d.data[i],h=o.chart.options.elements.point,f=o.index;e.extend(n,{_xScale:l,_yScale:s,_datasetIndex:f,_index:i,_model:{x:a?l.getPixelForDecimal(.5):l.getPixelForValue("object"==typeof c?c:NaN,i,f,o.chart.isCombo),y:a?s.getBasePixel():s.getPixelForValue(c,i,f),radius:a?0:u.radius?u.radius:o.getRadius(c),hitRadius:u.hitRadius?u.hitRadius:e.getValueAtIndexOrDefault(d.hitRadius,i,h.hitRadius)}}),t.DatasetController.prototype.removeHoverStyle.call(o,n,h);var g=n._model;g.skip=u.skip?u.skip:isNaN(g.x)||isNaN(g.y),n.pivot()},getRadius:function(t){return t.r||this.chart.options.elements.point.radius},setHoverStyle:function(n){var i=this;t.DatasetController.prototype.setHoverStyle.call(i,n);var a=i.chart.data.datasets[n._datasetIndex],o=n._index,r=n.custom||{},l=n._model;
+l.radius=r.hoverRadius?r.hoverRadius:e.getValueAtIndexOrDefault(a.hoverRadius,o,i.chart.options.elements.point.hoverRadius)+i.getRadius(a.data[o])},removeHoverStyle:function(e){var n=this;t.DatasetController.prototype.removeHoverStyle.call(n,e,n.chart.options.elements.point);var i=n.chart.data.datasets[e._datasetIndex].data[e._index],a=e.custom||{},o=e._model;o.radius=a.radius?a.radius:n.getRadius(i)}})}},{}],17:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers,n=t.defaults;n.doughnut={animation:{animateRotate:!0,animateScale:!1},aspectRatio:1,hover:{mode:"single"},legendCallback:function(t){var e=[];e.push('<ul class="'+t.id+'-legend">');var n=t.data,i=n.datasets,a=n.labels;if(i.length)for(var o=0;o<i[0].data.length;++o)e.push('<li><span style="background-color:'+i[0].backgroundColor[o]+'"></span>'),a[o]&&e.push(a[o]),e.push("</li>");return e.push("</ul>"),e.join("")},legend:{labels:{generateLabels:function(t){var n=t.data;return n.labels.length&&n.datasets.length?n.labels.map(function(i,a){var o=t.getDatasetMeta(0),r=n.datasets[0],l=o.data[a],s=l&&l.custom||{},u=e.getValueAtIndexOrDefault,d=t.options.elements.arc,c=s.backgroundColor?s.backgroundColor:u(r.backgroundColor,a,d.backgroundColor),h=s.borderColor?s.borderColor:u(r.borderColor,a,d.borderColor),f=s.borderWidth?s.borderWidth:u(r.borderWidth,a,d.borderWidth);return{text:i,fillStyle:c,strokeStyle:h,lineWidth:f,hidden:isNaN(r.data[a])||o.data[a].hidden,index:a}}):[]}},onClick:function(t,e){var n,i,a,o=e.index,r=this.chart;for(n=0,i=(r.data.datasets||[]).length;n<i;++n)a=r.getDatasetMeta(n),a.data[o]&&(a.data[o].hidden=!a.data[o].hidden);r.update()}},cutoutPercentage:50,rotation:Math.PI*-.5,circumference:2*Math.PI,tooltips:{callbacks:{title:function(){return""},label:function(t,n){var i=n.labels[t.index],a=": "+n.datasets[t.datasetIndex].data[t.index];return e.isArray(i)?(i=i.slice(),i[0]+=a):i+=a,i}}}},n.pie=e.clone(n.doughnut),e.extend(n.pie,{cutoutPercentage:0}),t.controllers.doughnut=t.controllers.pie=t.DatasetController.extend({dataElementType:t.elements.Arc,linkScales:e.noop,getRingIndex:function(t){for(var e=0,n=0;n<t;++n)this.chart.isDatasetVisible(n)&&++e;return e},update:function(t){var n=this,i=n.chart,a=i.chartArea,o=i.options,r=o.elements.arc,l=a.right-a.left-r.borderWidth,s=a.bottom-a.top-r.borderWidth,u=Math.min(l,s),d={x:0,y:0},c=n.getMeta(),h=o.cutoutPercentage,f=o.circumference;if(f<2*Math.PI){var g=o.rotation%(2*Math.PI);g+=2*Math.PI*(g>=Math.PI?-1:g<-Math.PI?1:0);var p=g+f,m={x:Math.cos(g),y:Math.sin(g)},v={x:Math.cos(p),y:Math.sin(p)},b=g<=0&&0<=p||g<=2*Math.PI&&2*Math.PI<=p,x=g<=.5*Math.PI&&.5*Math.PI<=p||g<=2.5*Math.PI&&2.5*Math.PI<=p,y=g<=-Math.PI&&-Math.PI<=p||g<=Math.PI&&Math.PI<=p,k=g<=.5*-Math.PI&&.5*-Math.PI<=p||g<=1.5*Math.PI&&1.5*Math.PI<=p,w=h/100,M={x:y?-1:Math.min(m.x*(m.x<0?1:w),v.x*(v.x<0?1:w)),y:k?-1:Math.min(m.y*(m.y<0?1:w),v.y*(v.y<0?1:w))},S={x:b?1:Math.max(m.x*(m.x>0?1:w),v.x*(v.x>0?1:w)),y:x?1:Math.max(m.y*(m.y>0?1:w),v.y*(v.y>0?1:w))},C={width:.5*(S.x-M.x),height:.5*(S.y-M.y)};u=Math.min(l/C.width,s/C.height),d={x:(S.x+M.x)*-.5,y:(S.y+M.y)*-.5}}i.borderWidth=n.getMaxBorderWidth(c.data),i.outerRadius=Math.max((u-i.borderWidth)/2,0),i.innerRadius=Math.max(h?i.outerRadius/100*h:0,0),i.radiusLength=(i.outerRadius-i.innerRadius)/i.getVisibleDatasetCount(),i.offsetX=d.x*i.outerRadius,i.offsetY=d.y*i.outerRadius,c.total=n.calculateTotal(),n.outerRadius=i.outerRadius-i.radiusLength*n.getRingIndex(n.index),n.innerRadius=Math.max(n.outerRadius-i.radiusLength,0),e.each(c.data,function(e,i){n.updateElement(e,i,t)})},updateElement:function(t,n,i){var a=this,o=a.chart,r=o.chartArea,l=o.options,s=l.animation,u=(r.left+r.right)/2,d=(r.top+r.bottom)/2,c=l.rotation,h=l.rotation,f=a.getDataset(),g=i&&s.animateRotate?0:t.hidden?0:a.calculateCircumference(f.data[n])*(l.circumference/(2*Math.PI)),p=i&&s.animateScale?0:a.innerRadius,m=i&&s.animateScale?0:a.outerRadius,v=e.getValueAtIndexOrDefault;e.extend(t,{_datasetIndex:a.index,_index:n,_model:{x:u+o.offsetX,y:d+o.offsetY,startAngle:c,endAngle:h,circumference:g,outerRadius:m,innerRadius:p,label:v(f.label,n,o.data.labels[n])}});var b=t._model;this.removeHoverStyle(t),i&&s.animateRotate||(0===n?b.startAngle=l.rotation:b.startAngle=a.getMeta().data[n-1]._model.endAngle,b.endAngle=b.startAngle+b.circumference),t.pivot()},removeHoverStyle:function(e){t.DatasetController.prototype.removeHoverStyle.call(this,e,this.chart.options.elements.arc)},calculateTotal:function(){var t,n=this.getDataset(),i=this.getMeta(),a=0;return e.each(i.data,function(e,i){t=n.data[i],isNaN(t)||e.hidden||(a+=Math.abs(t))}),a},calculateCircumference:function(t){var e=this.getMeta().total;return e>0&&!isNaN(t)?2*Math.PI*(t/e):0},getMaxBorderWidth:function(t){for(var e,n,i=0,a=this.index,o=t.length,r=0;r<o;r++)e=t[r]._model?t[r]._model.borderWidth:0,n=t[r]._chart?t[r]._chart.config.data.datasets[a].hoverBorderWidth:0,i=e>i?e:i,i=n>i?n:i;return i}})}},{}],18:[function(t,e,n){"use strict";e.exports=function(t){function e(t,e){return n.getValueOrDefault(t.showLine,e.showLines)}var n=t.helpers;t.defaults.line={showLines:!0,spanGaps:!1,hover:{mode:"label"},scales:{xAxes:[{type:"category",id:"x-axis-0"}],yAxes:[{type:"linear",id:"y-axis-0"}]}},t.controllers.line=t.DatasetController.extend({datasetElementType:t.elements.Line,dataElementType:t.elements.Point,update:function(t){var i,a,o,r=this,l=r.getMeta(),s=l.dataset,u=l.data||[],d=r.chart.options,c=d.elements.line,h=r.getScaleForId(l.yAxisID),f=r.getDataset(),g=e(f,d);for(g&&(o=s.custom||{},void 0!==f.tension&&void 0===f.lineTension&&(f.lineTension=f.tension),s._scale=h,s._datasetIndex=r.index,s._children=u,s._model={spanGaps:f.spanGaps?f.spanGaps:d.spanGaps,tension:o.tension?o.tension:n.getValueOrDefault(f.lineTension,c.tension),backgroundColor:o.backgroundColor?o.backgroundColor:f.backgroundColor||c.backgroundColor,borderWidth:o.borderWidth?o.borderWidth:f.borderWidth||c.borderWidth,borderColor:o.borderColor?o.borderColor:f.borderColor||c.borderColor,borderCapStyle:o.borderCapStyle?o.borderCapStyle:f.borderCapStyle||c.borderCapStyle,borderDash:o.borderDash?o.borderDash:f.borderDash||c.borderDash,borderDashOffset:o.borderDashOffset?o.borderDashOffset:f.borderDashOffset||c.borderDashOffset,borderJoinStyle:o.borderJoinStyle?o.borderJoinStyle:f.borderJoinStyle||c.borderJoinStyle,fill:o.fill?o.fill:void 0!==f.fill?f.fill:c.fill,steppedLine:o.steppedLine?o.steppedLine:n.getValueOrDefault(f.steppedLine,c.stepped),cubicInterpolationMode:o.cubicInterpolationMode?o.cubicInterpolationMode:n.getValueOrDefault(f.cubicInterpolationMode,c.cubicInterpolationMode)},s.pivot()),i=0,a=u.length;i<a;++i)r.updateElement(u[i],i,t);for(g&&0!==s._model.tension&&r.updateBezierControlPoints(),i=0,a=u.length;i<a;++i)u[i].pivot()},getPointBackgroundColor:function(t,e){var i=this.chart.options.elements.point.backgroundColor,a=this.getDataset(),o=t.custom||{};return o.backgroundColor?i=o.backgroundColor:a.pointBackgroundColor?i=n.getValueAtIndexOrDefault(a.pointBackgroundColor,e,i):a.backgroundColor&&(i=a.backgroundColor),i},getPointBorderColor:function(t,e){var i=this.chart.options.elements.point.borderColor,a=this.getDataset(),o=t.custom||{};return o.borderColor?i=o.borderColor:a.pointBorderColor?i=n.getValueAtIndexOrDefault(a.pointBorderColor,e,i):a.borderColor&&(i=a.borderColor),i},getPointBorderWidth:function(t,e){var i=this.chart.options.elements.point.borderWidth,a=this.getDataset(),o=t.custom||{};return isNaN(o.borderWidth)?isNaN(a.pointBorderWidth)?isNaN(a.borderWidth)||(i=a.borderWidth):i=n.getValueAtIndexOrDefault(a.pointBorderWidth,e,i):i=o.borderWidth,i},updateElement:function(t,e,i){var a,o,r=this,l=r.getMeta(),s=t.custom||{},u=r.getDataset(),d=r.index,c=u.data[e],h=r.getScaleForId(l.yAxisID),f=r.getScaleForId(l.xAxisID),g=r.chart.options.elements.point,p=r.chart.data.labels||[],m=1===p.length||1===u.data.length||r.chart.isCombo;void 0!==u.radius&&void 0===u.pointRadius&&(u.pointRadius=u.radius),void 0!==u.hitRadius&&void 0===u.pointHitRadius&&(u.pointHitRadius=u.hitRadius),a=f.getPixelForValue("object"==typeof c?c:NaN,e,d,m),o=i?h.getBasePixel():r.calculatePointY(c,e,d),t._xScale=f,t._yScale=h,t._datasetIndex=d,t._index=e,t._model={x:a,y:o,skip:s.skip||isNaN(a)||isNaN(o),radius:s.radius||n.getValueAtIndexOrDefault(u.pointRadius,e,g.radius),pointStyle:s.pointStyle||n.getValueAtIndexOrDefault(u.pointStyle,e,g.pointStyle),backgroundColor:r.getPointBackgroundColor(t,e),borderColor:r.getPointBorderColor(t,e),borderWidth:r.getPointBorderWidth(t,e),tension:l.dataset._model?l.dataset._model.tension:0,steppedLine:!!l.dataset._model&&l.dataset._model.steppedLine,hitRadius:s.hitRadius||n.getValueAtIndexOrDefault(u.pointHitRadius,e,g.hitRadius)}},calculatePointY:function(t,e,n){var i,a,o,r=this,l=r.chart,s=r.getMeta(),u=r.getScaleForId(s.yAxisID),d=0,c=0;if(u.options.stacked){for(i=0;i<n;i++)if(a=l.data.datasets[i],o=l.getDatasetMeta(i),"line"===o.type&&o.yAxisID===u.id&&l.isDatasetVisible(i)){var h=Number(u.getRightValue(a.data[e]));h<0?c+=h||0:d+=h||0}var f=Number(u.getRightValue(t));return f<0?u.getPixelForValue(c+f):u.getPixelForValue(d+f)}return u.getPixelForValue(t)},updateBezierControlPoints:function(){function t(t,e,n){return Math.max(Math.min(t,n),e)}var e,i,a,o,r,l=this,s=l.getMeta(),u=l.chart.chartArea,d=s.data||[];if(s.dataset._model.spanGaps&&(d=d.filter(function(t){return!t._model.skip})),"monotone"===s.dataset._model.cubicInterpolationMode)n.splineCurveMonotone(d);else for(e=0,i=d.length;e<i;++e)a=d[e],o=a._model,r=n.splineCurve(n.previousItem(d,e)._model,o,n.nextItem(d,e)._model,s.dataset._model.tension),o.controlPointPreviousX=r.previous.x,o.controlPointPreviousY=r.previous.y,o.controlPointNextX=r.next.x,o.controlPointNextY=r.next.y;if(l.chart.options.elements.line.capBezierPoints)for(e=0,i=d.length;e<i;++e)o=d[e]._model,o.controlPointPreviousX=t(o.controlPointPreviousX,u.left,u.right),o.controlPointPreviousY=t(o.controlPointPreviousY,u.top,u.bottom),o.controlPointNextX=t(o.controlPointNextX,u.left,u.right),o.controlPointNextY=t(o.controlPointNextY,u.top,u.bottom)},draw:function(){var n=this,i=n.chart,a=n.getMeta(),o=a.data||[],r=i.chartArea,l=o.length,s=0;for(t.canvasHelpers.clipArea(i.ctx,r),e(n.getDataset(),i.options)&&a.dataset.draw(),t.canvasHelpers.unclipArea(i.ctx);s<l;++s)o[s].draw(r)},setHoverStyle:function(t){var e=this.chart.data.datasets[t._datasetIndex],i=t._index,a=t.custom||{},o=t._model;o.radius=a.hoverRadius||n.getValueAtIndexOrDefault(e.pointHoverRadius,i,this.chart.options.elements.point.hoverRadius),o.backgroundColor=a.hoverBackgroundColor||n.getValueAtIndexOrDefault(e.pointHoverBackgroundColor,i,n.getHoverColor(o.backgroundColor)),o.borderColor=a.hoverBorderColor||n.getValueAtIndexOrDefault(e.pointHoverBorderColor,i,n.getHoverColor(o.borderColor)),o.borderWidth=a.hoverBorderWidth||n.getValueAtIndexOrDefault(e.pointHoverBorderWidth,i,o.borderWidth)},removeHoverStyle:function(t){var e=this,i=e.chart.data.datasets[t._datasetIndex],a=t._index,o=t.custom||{},r=t._model;void 0!==i.radius&&void 0===i.pointRadius&&(i.pointRadius=i.radius),r.radius=o.radius||n.getValueAtIndexOrDefault(i.pointRadius,a,e.chart.options.elements.point.radius),r.backgroundColor=e.getPointBackgroundColor(t,a),r.borderColor=e.getPointBorderColor(t,a),r.borderWidth=e.getPointBorderWidth(t,a)}})}},{}],19:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers;t.defaults.polarArea={scale:{type:"radialLinear",angleLines:{display:!1},gridLines:{circular:!0},pointLabels:{display:!1},ticks:{beginAtZero:!0}},animation:{animateRotate:!0,animateScale:!0},startAngle:-.5*Math.PI,aspectRatio:1,legendCallback:function(t){var e=[];e.push('<ul class="'+t.id+'-legend">');var n=t.data,i=n.datasets,a=n.labels;if(i.length)for(var o=0;o<i[0].data.length;++o)e.push('<li><span style="background-color:'+i[0].backgroundColor[o]+'"></span>'),a[o]&&e.push(a[o]),e.push("</li>");return e.push("</ul>"),e.join("")},legend:{labels:{generateLabels:function(t){var n=t.data;return n.labels.length&&n.datasets.length?n.labels.map(function(i,a){var o=t.getDatasetMeta(0),r=n.datasets[0],l=o.data[a],s=l.custom||{},u=e.getValueAtIndexOrDefault,d=t.options.elements.arc,c=s.backgroundColor?s.backgroundColor:u(r.backgroundColor,a,d.backgroundColor),h=s.borderColor?s.borderColor:u(r.borderColor,a,d.borderColor),f=s.borderWidth?s.borderWidth:u(r.borderWidth,a,d.borderWidth);return{text:i,fillStyle:c,strokeStyle:h,lineWidth:f,hidden:isNaN(r.data[a])||o.data[a].hidden,index:a}}):[]}},onClick:function(t,e){var n,i,a,o=e.index,r=this.chart;for(n=0,i=(r.data.datasets||[]).length;n<i;++n)a=r.getDatasetMeta(n),a.data[o].hidden=!a.data[o].hidden;r.update()}},tooltips:{callbacks:{title:function(){return""},label:function(t,e){return e.labels[t.index]+": "+t.yLabel}}}},t.controllers.polarArea=t.DatasetController.extend({dataElementType:t.elements.Arc,linkScales:e.noop,update:function(t){var n=this,i=n.chart,a=i.chartArea,o=n.getMeta(),r=i.options,l=r.elements.arc,s=Math.min(a.right-a.left,a.bottom-a.top);i.outerRadius=Math.max((s-l.borderWidth/2)/2,0),i.innerRadius=Math.max(r.cutoutPercentage?i.outerRadius/100*r.cutoutPercentage:1,0),i.radiusLength=(i.outerRadius-i.innerRadius)/i.getVisibleDatasetCount(),n.outerRadius=i.outerRadius-i.radiusLength*n.index,n.innerRadius=n.outerRadius-i.radiusLength,o.count=n.countVisibleElements(),e.each(o.data,function(e,i){n.updateElement(e,i,t)})},updateElement:function(t,n,i){for(var a=this,o=a.chart,r=a.getDataset(),l=o.options,s=l.animation,u=o.scale,d=e.getValueAtIndexOrDefault,c=o.data.labels,h=a.calculateCircumference(r.data[n]),f=u.xCenter,g=u.yCenter,p=0,m=a.getMeta(),v=0;v<n;++v)isNaN(r.data[v])||m.data[v].hidden||++p;var b=l.startAngle,x=t.hidden?0:u.getDistanceFromCenterForValue(r.data[n]),y=b+h*p,k=y+(t.hidden?0:h),w=s.animateScale?0:u.getDistanceFromCenterForValue(r.data[n]);e.extend(t,{_datasetIndex:a.index,_index:n,_scale:u,_model:{x:f,y:g,innerRadius:0,outerRadius:i?w:x,startAngle:i&&s.animateRotate?b:y,endAngle:i&&s.animateRotate?b:k,label:d(c,n,c[n])}}),a.removeHoverStyle(t),t.pivot()},removeHoverStyle:function(e){t.DatasetController.prototype.removeHoverStyle.call(this,e,this.chart.options.elements.arc)},countVisibleElements:function(){var t=this.getDataset(),n=this.getMeta(),i=0;return e.each(n.data,function(e,n){isNaN(t.data[n])||e.hidden||i++}),i},calculateCircumference:function(t){var e=this.getMeta().count;return e>0&&!isNaN(t)?2*Math.PI/e:0}})}},{}],20:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers;t.defaults.radar={aspectRatio:1,scale:{type:"radialLinear"},elements:{line:{tension:0}}},t.controllers.radar=t.DatasetController.extend({datasetElementType:t.elements.Line,dataElementType:t.elements.Point,linkScales:e.noop,update:function(t){var n=this,i=n.getMeta(),a=i.dataset,o=i.data,r=a.custom||{},l=n.getDataset(),s=n.chart.options.elements.line,u=n.chart.scale;void 0!==l.tension&&void 0===l.lineTension&&(l.lineTension=l.tension),e.extend(i.dataset,{_datasetIndex:n.index,_scale:u,_children:o,_loop:!0,_model:{tension:r.tension?r.tension:e.getValueOrDefault(l.lineTension,s.tension),backgroundColor:r.backgroundColor?r.backgroundColor:l.backgroundColor||s.backgroundColor,borderWidth:r.borderWidth?r.borderWidth:l.borderWidth||s.borderWidth,borderColor:r.borderColor?r.borderColor:l.borderColor||s.borderColor,fill:r.fill?r.fill:void 0!==l.fill?l.fill:s.fill,borderCapStyle:r.borderCapStyle?r.borderCapStyle:l.borderCapStyle||s.borderCapStyle,borderDash:r.borderDash?r.borderDash:l.borderDash||s.borderDash,borderDashOffset:r.borderDashOffset?r.borderDashOffset:l.borderDashOffset||s.borderDashOffset,borderJoinStyle:r.borderJoinStyle?r.borderJoinStyle:l.borderJoinStyle||s.borderJoinStyle}}),i.dataset.pivot(),e.each(o,function(e,i){n.updateElement(e,i,t)},n),n.updateBezierControlPoints()},updateElement:function(t,n,i){var a=this,o=t.custom||{},r=a.getDataset(),l=a.chart.scale,s=a.chart.options.elements.point,u=l.getPointPositionForValue(n,r.data[n]);void 0!==r.radius&&void 0===r.pointRadius&&(r.pointRadius=r.radius),void 0!==r.hitRadius&&void 0===r.pointHitRadius&&(r.pointHitRadius=r.hitRadius),e.extend(t,{_datasetIndex:a.index,_index:n,_scale:l,_model:{x:i?l.xCenter:u.x,y:i?l.yCenter:u.y,tension:o.tension?o.tension:e.getValueOrDefault(r.lineTension,a.chart.options.elements.line.tension),radius:o.radius?o.radius:e.getValueAtIndexOrDefault(r.pointRadius,n,s.radius),backgroundColor:o.backgroundColor?o.backgroundColor:e.getValueAtIndexOrDefault(r.pointBackgroundColor,n,s.backgroundColor),borderColor:o.borderColor?o.borderColor:e.getValueAtIndexOrDefault(r.pointBorderColor,n,s.borderColor),borderWidth:o.borderWidth?o.borderWidth:e.getValueAtIndexOrDefault(r.pointBorderWidth,n,s.borderWidth),pointStyle:o.pointStyle?o.pointStyle:e.getValueAtIndexOrDefault(r.pointStyle,n,s.pointStyle),hitRadius:o.hitRadius?o.hitRadius:e.getValueAtIndexOrDefault(r.pointHitRadius,n,s.hitRadius)}}),t._model.skip=o.skip?o.skip:isNaN(t._model.x)||isNaN(t._model.y)},updateBezierControlPoints:function(){var t=this.chart.chartArea,n=this.getMeta();e.each(n.data,function(i,a){var o=i._model,r=e.splineCurve(e.previousItem(n.data,a,!0)._model,o,e.nextItem(n.data,a,!0)._model,o.tension);o.controlPointPreviousX=Math.max(Math.min(r.previous.x,t.right),t.left),o.controlPointPreviousY=Math.max(Math.min(r.previous.y,t.bottom),t.top),o.controlPointNextX=Math.max(Math.min(r.next.x,t.right),t.left),o.controlPointNextY=Math.max(Math.min(r.next.y,t.bottom),t.top),i.pivot()})},setHoverStyle:function(t){var n=this.chart.data.datasets[t._datasetIndex],i=t.custom||{},a=t._index,o=t._model;o.radius=i.hoverRadius?i.hoverRadius:e.getValueAtIndexOrDefault(n.pointHoverRadius,a,this.chart.options.elements.point.hoverRadius),o.backgroundColor=i.hoverBackgroundColor?i.hoverBackgroundColor:e.getValueAtIndexOrDefault(n.pointHoverBackgroundColor,a,e.getHoverColor(o.backgroundColor)),o.borderColor=i.hoverBorderColor?i.hoverBorderColor:e.getValueAtIndexOrDefault(n.pointHoverBorderColor,a,e.getHoverColor(o.borderColor)),o.borderWidth=i.hoverBorderWidth?i.hoverBorderWidth:e.getValueAtIndexOrDefault(n.pointHoverBorderWidth,a,o.borderWidth)},removeHoverStyle:function(t){var n=this.chart.data.datasets[t._datasetIndex],i=t.custom||{},a=t._index,o=t._model,r=this.chart.options.elements.point;o.radius=i.radius?i.radius:e.getValueAtIndexOrDefault(n.pointRadius,a,r.radius),o.backgroundColor=i.backgroundColor?i.backgroundColor:e.getValueAtIndexOrDefault(n.pointBackgroundColor,a,r.backgroundColor),o.borderColor=i.borderColor?i.borderColor:e.getValueAtIndexOrDefault(n.pointBorderColor,a,r.borderColor),o.borderWidth=i.borderWidth?i.borderWidth:e.getValueAtIndexOrDefault(n.pointBorderWidth,a,r.borderWidth)}})}},{}],21:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers;t.defaults.global.animation={duration:1e3,easing:"easeOutQuart",onProgress:e.noop,onComplete:e.noop},t.Animation=t.Element.extend({chart:null,currentStep:0,numSteps:60,easing:"",render:null,onAnimationProgress:null,onAnimationComplete:null}),t.animationService={frameDuration:17,animations:[],dropFrames:0,request:null,addAnimation:function(t,e,n,i){var a,o,r=this.animations;for(e.chart=t,i||(t.animating=!0),a=0,o=r.length;a<o;++a)if(r[a].chart===t)return void(r[a]=e);r.push(e),1===r.length&&this.requestAnimationFrame()},cancelAnimation:function(t){var n=e.findIndex(this.animations,function(e){return e.chart===t});n!==-1&&(this.animations.splice(n,1),t.animating=!1)},requestAnimationFrame:function(){var t=this;null===t.request&&(t.request=e.requestAnimFrame.call(window,function(){t.request=null,t.startDigest()}))},startDigest:function(){var t=this,e=Date.now(),n=0;t.dropFrames>1&&(n=Math.floor(t.dropFrames),t.dropFrames=t.dropFrames%1),t.advance(1+n);var i=Date.now();t.dropFrames+=(i-e)/t.frameDuration,t.animations.length>0&&t.requestAnimationFrame()},advance:function(t){for(var n,i,a=this.animations,o=0;o<a.length;)n=a[o],i=n.chart,n.currentStep=(n.currentStep||0)+t,n.currentStep=Math.min(n.currentStep,n.numSteps),e.callback(n.render,[i,n],i),e.callback(n.onAnimationProgress,[n],i),n.currentStep>=n.numSteps?(e.callback(n.onAnimationComplete,[n],i),i.animating=!1,a.splice(o,1)):++o}},Object.defineProperty(t.Animation.prototype,"animationObject",{get:function(){return this}}),Object.defineProperty(t.Animation.prototype,"chartInstance",{get:function(){return this.chart},set:function(t){this.chart=t}})}},{}],22:[function(t,e,n){"use strict";e.exports=function(t){var e=t.canvasHelpers={};e.drawPoint=function(e,n,i,a,o){var r,l,s,u,d,c;if("object"==typeof n&&(r=n.toString(),"[object HTMLImageElement]"===r||"[object HTMLCanvasElement]"===r))return void e.drawImage(n,a-n.width/2,o-n.height/2,n.width,n.height);if(!(isNaN(i)||i<=0)){switch(n){default:e.beginPath(),e.arc(a,o,i,0,2*Math.PI),e.closePath(),e.fill();break;case"triangle":e.beginPath(),l=3*i/Math.sqrt(3),d=l*Math.sqrt(3)/2,e.moveTo(a-l/2,o+d/3),e.lineTo(a+l/2,o+d/3),e.lineTo(a,o-2*d/3),e.closePath(),e.fill();break;case"rect":c=1/Math.SQRT2*i,e.beginPath(),e.fillRect(a-c,o-c,2*c,2*c),e.strokeRect(a-c,o-c,2*c,2*c);break;case"rectRounded":var h=i/Math.SQRT2,f=a-h,g=o-h,p=Math.SQRT2*i;t.helpers.drawRoundedRectangle(e,f,g,p,p,i/2),e.fill();break;case"rectRot":c=1/Math.SQRT2*i,e.beginPath(),e.moveTo(a-c,o),e.lineTo(a,o+c),e.lineTo(a+c,o),e.lineTo(a,o-c),e.closePath(),e.fill();break;case"cross":e.beginPath(),e.moveTo(a,o+i),e.lineTo(a,o-i),e.moveTo(a-i,o),e.lineTo(a+i,o),e.closePath();break;case"crossRot":e.beginPath(),s=Math.cos(Math.PI/4)*i,u=Math.sin(Math.PI/4)*i,e.moveTo(a-s,o-u),e.lineTo(a+s,o+u),e.moveTo(a-s,o+u),e.lineTo(a+s,o-u),e.closePath();break;case"star":e.beginPath(),e.moveTo(a,o+i),e.lineTo(a,o-i),e.moveTo(a-i,o),e.lineTo(a+i,o),s=Math.cos(Math.PI/4)*i,u=Math.sin(Math.PI/4)*i,e.moveTo(a-s,o-u),e.lineTo(a+s,o+u),e.moveTo(a-s,o+u),e.lineTo(a+s,o-u),e.closePath();break;case"line":e.beginPath(),e.moveTo(a-i,o),e.lineTo(a+i,o),e.closePath();break;case"dash":e.beginPath(),e.moveTo(a,o),e.lineTo(a+i,o),e.closePath()}e.stroke()}},e.clipArea=function(t,e){t.save(),t.beginPath(),t.rect(e.left,e.top,e.right-e.left,e.bottom-e.top),t.clip()},e.unclipArea=function(t){t.restore()},e.lineTo=function(t,e,n,i){return n.steppedLine?("after"===n.steppedLine?t.lineTo(e.x,n.y):t.lineTo(n.x,e.y),void t.lineTo(n.x,n.y)):n.tension?void t.bezierCurveTo(i?e.controlPointPreviousX:e.controlPointNextX,i?e.controlPointPreviousY:e.controlPointNextY,i?n.controlPointNextX:n.controlPointPreviousX,i?n.controlPointNextY:n.controlPointPreviousY,n.x,n.y):void t.lineTo(n.x,n.y)},t.helpers.canvas=e}},{}],23:[function(t,e,n){"use strict";e.exports=function(t){function e(e){e=e||{};var n=e.data=e.data||{};return n.datasets=n.datasets||[],n.labels=n.labels||[],e.options=a.configMerge(t.defaults.global,t.defaults[e.type],e.options||{}),e}function n(t){var e=t.options;e.scale?t.scale.options=e.scale:e.scales&&e.scales.xAxes.concat(e.scales.yAxes).forEach(function(e){t.scales[e.id].options=e}),t.tooltip._options=e.tooltips}function i(t){return"top"===t||"bottom"===t}var a=t.helpers,o=t.plugins,r=t.platform;t.types={},t.instances={},t.controllers={},a.extend(t.prototype,{construct:function(n,i){var o=this;i=e(i);var l=r.acquireContext(n,i),s=l&&l.canvas,u=s&&s.height,d=s&&s.width;return o.id=a.uid(),o.ctx=l,o.canvas=s,o.config=i,o.width=d,o.height=u,o.aspectRatio=u?d/u:null,o.options=i.options,o._bufferedRender=!1,o.chart=o,o.controller=o,t.instances[o.id]=o,Object.defineProperty(o,"data",{get:function(){return o.config.data},set:function(t){o.config.data=t}}),l&&s?(o.initialize(),void o.update()):void console.error("Failed to create chart: can't acquire context from the given item")},initialize:function(){var t=this;return o.notify(t,"beforeInit"),a.retinaScale(t),t.bindEvents(),t.options.responsive&&t.resize(!0),t.ensureScalesHaveIDs(),t.buildScales(),t.initToolTip(),o.notify(t,"afterInit"),t},clear:function(){return a.clear(this),this},stop:function(){return t.animationService.cancelAnimation(this),this},resize:function(t){var e=this,n=e.options,i=e.canvas,r=n.maintainAspectRatio&&e.aspectRatio||null,l=Math.floor(a.getMaximumWidth(i)),s=Math.floor(r?l/r:a.getMaximumHeight(i));if((e.width!==l||e.height!==s)&&(i.width=e.width=l,i.height=e.height=s,i.style.width=l+"px",i.style.height=s+"px",a.retinaScale(e),!t)){var u={width:l,height:s};o.notify(e,"resize",[u]),e.options.onResize&&e.options.onResize(e,u),e.stop(),e.update(e.options.responsiveAnimationDuration)}},ensureScalesHaveIDs:function(){var t=this.options,e=t.scales||{},n=t.scale;a.each(e.xAxes,function(t,e){t.id=t.id||"x-axis-"+e}),a.each(e.yAxes,function(t,e){t.id=t.id||"y-axis-"+e}),n&&(n.id=n.id||"scale")},buildScales:function(){var e=this,n=e.options,o=e.scales={},r=[];n.scales&&(r=r.concat((n.scales.xAxes||[]).map(function(t){return{options:t,dtype:"category",dposition:"bottom"}}),(n.scales.yAxes||[]).map(function(t){return{options:t,dtype:"linear",dposition:"left"}}))),n.scale&&r.push({options:n.scale,dtype:"radialLinear",isDefault:!0,dposition:"chartArea"}),a.each(r,function(n){var r=n.options,l=a.getValueOrDefault(r.type,n.dtype),s=t.scaleService.getScaleConstructor(l);if(s){i(r.position)!==i(n.dposition)&&(r.position=n.dposition);var u=new s({id:r.id,options:r,ctx:e.ctx,chart:e});o[u.id]=u,n.isDefault&&(e.scale=u)}}),t.scaleService.addScalesToLayout(this)},buildOrUpdateControllers:function(){var e=this,n=[],i=[];if(a.each(e.data.datasets,function(a,o){var r=e.getDatasetMeta(o);if(r.type||(r.type=a.type||e.config.type),n.push(r.type),r.controller)r.controller.updateIndex(o);else{var l=t.controllers[r.type];if(void 0===l)throw new Error('"'+r.type+'" is not a chart type.');r.controller=new l(e,o),i.push(r.controller)}},e),n.length>1)for(var o=1;o<n.length;o++)if(n[o]!==n[o-1]){e.isCombo=!0;break}return i},resetElements:function(){var t=this;a.each(t.data.datasets,function(e,n){t.getDatasetMeta(n).controller.reset()},t)},reset:function(){this.resetElements(),this.tooltip.initialize()},update:function(t,e){var i=this;if(n(i),o.notify(i,"beforeUpdate")!==!1){i.tooltip._data=i.data;var r=i.buildOrUpdateControllers();a.each(i.data.datasets,function(t,e){i.getDatasetMeta(e).controller.buildOrUpdateElements()},i),i.updateLayout(),a.each(r,function(t){t.reset()}),i.updateDatasets(),o.notify(i,"afterUpdate"),i._bufferedRender?i._bufferedRequest={lazy:e,duration:t}:i.render(t,e)}},updateLayout:function(){var e=this;o.notify(e,"beforeLayout")!==!1&&(t.layoutService.update(this,this.width,this.height),o.notify(e,"afterScaleUpdate"),o.notify(e,"afterLayout"))},updateDatasets:function(){var t=this;if(o.notify(t,"beforeDatasetsUpdate")!==!1){for(var e=0,n=t.data.datasets.length;e<n;++e)t.updateDataset(e);o.notify(t,"afterDatasetsUpdate")}},updateDataset:function(t){var e=this,n=e.getDatasetMeta(t),i={meta:n,index:t};o.notify(e,"beforeDatasetUpdate",[i])!==!1&&(n.controller.update(),o.notify(e,"afterDatasetUpdate",[i]))},render:function(e,n){var i=this;if(o.notify(i,"beforeRender")!==!1){var r=i.options.animation,l=function(t){o.notify(i,"afterRender"),a.callback(r&&r.onComplete,[t],i)};if(r&&("undefined"!=typeof e&&0!==e||"undefined"==typeof e&&0!==r.duration)){var s=new t.Animation({numSteps:(e||r.duration)/16.66,easing:r.easing,render:function(t,e){var n=a.easingEffects[e.easing],i=e.currentStep,o=i/e.numSteps;t.draw(n(o),o,i)},onAnimationProgress:r.onProgress,onAnimationComplete:l});t.animationService.addAnimation(i,s,e,n)}else i.draw(),l(new t.Animation({numSteps:0,chart:i}));return i}},draw:function(t){var e=this;e.clear(),void 0!==t&&null!==t||(t=1),e.transition(t),o.notify(e,"beforeDraw",[t])!==!1&&(a.each(e.boxes,function(t){t.draw(e.chartArea)},e),e.scale&&e.scale.draw(),e.drawDatasets(t),e.tooltip.draw(),o.notify(e,"afterDraw",[t]))},transition:function(t){for(var e=this,n=0,i=(e.data.datasets||[]).length;n<i;++n)e.isDatasetVisible(n)&&e.getDatasetMeta(n).controller.transition(t);e.tooltip.transition(t)},drawDatasets:function(t){var e=this;if(o.notify(e,"beforeDatasetsDraw",[t])!==!1){for(var n=(e.data.datasets||[]).length-1;n>=0;--n)e.isDatasetVisible(n)&&e.drawDataset(n,t);o.notify(e,"afterDatasetsDraw",[t])}},drawDataset:function(t,e){var n=this,i=n.getDatasetMeta(t),a={meta:i,index:t,easingValue:e};o.notify(n,"beforeDatasetDraw",[a])!==!1&&(i.controller.draw(e),o.notify(n,"afterDatasetDraw",[a]))},getElementAtEvent:function(e){return t.Interaction.modes.single(this,e)},getElementsAtEvent:function(e){return t.Interaction.modes.label(this,e,{intersect:!0})},getElementsAtXAxis:function(e){return t.Interaction.modes["x-axis"](this,e,{intersect:!0})},getElementsAtEventForMode:function(e,n,i){var a=t.Interaction.modes[n];return"function"==typeof a?a(this,e,i):[]},getDatasetAtEvent:function(e){return t.Interaction.modes.dataset(this,e,{intersect:!0})},getDatasetMeta:function(t){var e=this,n=e.data.datasets[t];n._meta||(n._meta={});var i=n._meta[e.id];return i||(i=n._meta[e.id]={type:null,data:[],dataset:null,controller:null,hidden:null,xAxisID:null,yAxisID:null}),i},getVisibleDatasetCount:function(){for(var t=0,e=0,n=this.data.datasets.length;e<n;++e)this.isDatasetVisible(e)&&t++;return t},isDatasetVisible:function(t){var e=this.getDatasetMeta(t);return"boolean"==typeof e.hidden?!e.hidden:!this.data.datasets[t].hidden},generateLegend:function(){return this.options.legendCallback(this)},destroy:function(){var e,n,i,l=this,s=l.canvas;for(l.stop(),n=0,i=l.data.datasets.length;n<i;++n)e=l.getDatasetMeta(n),e.controller&&(e.controller.destroy(),e.controller=null);s&&(l.unbindEvents(),a.clear(l),r.releaseContext(l.ctx),l.canvas=null,l.ctx=null),o.notify(l,"destroy"),delete t.instances[l.id]},toBase64Image:function(){return this.canvas.toDataURL.apply(this.canvas,arguments)},initToolTip:function(){var e=this;e.tooltip=new t.Tooltip({_chart:e,_chartInstance:e,_data:e.data,_options:e.options.tooltips},e),e.tooltip.initialize()},bindEvents:function(){var t=this,e=t._listeners={},n=function(){t.eventHandler.apply(t,arguments)};a.each(t.options.events,function(i){r.addEventListener(t,i,n),e[i]=n}),t.options.responsive&&(n=function(){t.resize()},r.addEventListener(t,"resize",n),e.resize=n)},unbindEvents:function(){var t=this,e=t._listeners;e&&(delete t._listeners,a.each(e,function(e,n){r.removeEventListener(t,n,e)}))},updateHoverStyle:function(t,e,n){var 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r.mirror?g=0:g+=t.options.ticks.padding,a.width=Math.min(t.maxWidth,a.width+g),t.paddingTop=c.size/2,t.paddingBottom=c.size/2}t.handleMargins(),t.width=a.width,t.height=a.height},handleMargins:function(){var t=this;t.margins&&(t.paddingLeft=Math.max(t.paddingLeft-t.margins.left,0),t.paddingTop=Math.max(t.paddingTop-t.margins.top,0),t.paddingRight=Math.max(t.paddingRight-t.margins.right,0),t.paddingBottom=Math.max(t.paddingBottom-t.margins.bottom,0))},afterFit:function(){i.callback(this.options.afterFit,[this])},isHorizontal:function(){return"top"===this.options.position||"bottom"===this.options.position},isFullWidth:function(){return this.options.fullWidth},getRightValue:function(t){return null===t||"undefined"==typeof t?NaN:"number"!=typeof t||isFinite(t)?"object"==typeof t?t instanceof Date||t.isValid?t:this.getRightValue(this.isHorizontal()?t.x:t.y):t:NaN},getLabelForIndex:i.noop,getPixelForValue:i.noop,getValueForPixel:i.noop,getPixelForTick:function(t,e){var n=this;if(n.isHorizontal()){var i=n.width-(n.paddingLeft+n.paddingRight),a=i/Math.max(n.ticks.length-(n.options.gridLines.offsetGridLines?0:1),1),o=a*t+n.paddingLeft;e&&(o+=a/2);var r=n.left+Math.round(o);return r+=n.isFullWidth()?n.margins.left:0}var l=n.height-(n.paddingTop+n.paddingBottom);return n.top+t*(l/(n.ticks.length-1))},getPixelForDecimal:function(t){var e=this;if(e.isHorizontal()){var n=e.width-(e.paddingLeft+e.paddingRight),i=n*t+e.paddingLeft,a=e.left+Math.round(i);return a+=e.isFullWidth()?e.margins.left:0}return e.top+t*e.height},getBasePixel:function(){return this.getPixelForValue(this.getBaseValue())},getBaseValue:function(){var t=this,e=t.min,n=t.max;return t.beginAtZero?0:e<0&&n<0?n:e>0&&n>0?e:0},draw:function(e){var a=this,o=a.options;if(o.display){var r,l,s=a.ctx,u=t.defaults.global,d=o.ticks,c=o.gridLines,h=o.scaleLabel,f=0!==a.labelRotation,g=d.autoSkip,p=a.isHorizontal();d.maxTicksLimit&&(l=d.maxTicksLimit);var m=i.getValueOrDefault(d.fontColor,u.defaultFontColor),v=n(d),b=c.drawTicks?c.tickMarkLength:0,x=i.getValueOrDefault(h.fontColor,u.defaultFontColor),y=n(h),k=i.toRadians(a.labelRotation),w=Math.cos(k),M=a.longestLabelWidth*w;s.fillStyle=m;var S=[];if(p){if(r=!1,(M+d.autoSkipPadding)*a.ticks.length>a.width-(a.paddingLeft+a.paddingRight)&&(r=1+Math.floor((M+d.autoSkipPadding)*a.ticks.length/(a.width-(a.paddingLeft+a.paddingRight)))),l&&a.ticks.length>l)for(;!r||a.ticks.length/(r||1)>l;)r||(r=1),r+=1;g||(r=!1)}var C="right"===o.position?a.left:a.right-b,D="right"===o.position?a.left+b:a.right,I="bottom"===o.position?a.top:a.bottom-b,A="bottom"===o.position?a.top+b:a.bottom;if(i.each(a.ticks,function(t,n){if(void 0!==t&&null!==t){var l=a.ticks.length===n+1,s=r>1&&n%r>0||n%r===0&&n+r>=a.ticks.length;if((!s||l)&&void 0!==t&&null!==t){var h,g,m,v;n===("undefined"!=typeof a.zeroLineIndex?a.zeroLineIndex:0)?(h=c.zeroLineWidth,g=c.zeroLineColor,m=c.zeroLineBorderDash,v=c.zeroLineBorderDashOffset):(h=i.getValueAtIndexOrDefault(c.lineWidth,n),g=i.getValueAtIndexOrDefault(c.color,n),m=i.getValueOrDefault(c.borderDash,u.borderDash),v=i.getValueOrDefault(c.borderDashOffset,u.borderDashOffset));var x,y,w,M,P,_,T,F,R,L,V="middle",O="middle";if(p){"bottom"===o.position?(O=f?"middle":"top",V=f?"right":"center",L=a.top+b):(O=f?"middle":"bottom",V=f?"left":"center",L=a.bottom-b);var z=a.getPixelForTick(n)+i.aliasPixel(h);R=a.getPixelForTick(n,c.offsetGridLines)+d.labelOffset,x=w=P=T=z,y=I,M=A,_=e.top,F=e.bottom}else{var B,W="left"===o.position,N=d.padding;d.mirror?(V=W?"left":"right",B=N):(V=W?"right":"left",
+B=b+N),R=W?a.right-B:a.left+B;var E=a.getPixelForTick(n);E+=i.aliasPixel(h),L=a.getPixelForTick(n,c.offsetGridLines),x=C,w=D,P=e.left,T=e.right,y=M=_=F=E}S.push({tx1:x,ty1:y,tx2:w,ty2:M,x1:P,y1:_,x2:T,y2:F,labelX:R,labelY:L,glWidth:h,glColor:g,glBorderDash:m,glBorderDashOffset:v,rotation:-1*k,label:t,textBaseline:O,textAlign:V})}}}),i.each(S,function(t){if(c.display&&(s.save(),s.lineWidth=t.glWidth,s.strokeStyle=t.glColor,s.setLineDash&&(s.setLineDash(t.glBorderDash),s.lineDashOffset=t.glBorderDashOffset),s.beginPath(),c.drawTicks&&(s.moveTo(t.tx1,t.ty1),s.lineTo(t.tx2,t.ty2)),c.drawOnChartArea&&(s.moveTo(t.x1,t.y1),s.lineTo(t.x2,t.y2)),s.stroke(),s.restore()),d.display){s.save(),s.translate(t.labelX,t.labelY),s.rotate(t.rotation),s.font=v.font,s.textBaseline=t.textBaseline,s.textAlign=t.textAlign;var e=t.label;if(i.isArray(e))for(var n=0,a=0;n<e.length;++n)s.fillText(""+e[n],0,a),a+=1.5*v.size;else s.fillText(e,0,0);s.restore()}}),h.display){var P,_,T=0;if(p)P=a.left+(a.right-a.left)/2,_="bottom"===o.position?a.bottom-y.size/2:a.top+y.size/2;else{var F="left"===o.position;P=F?a.left+y.size/2:a.right-y.size/2,_=a.top+(a.bottom-a.top)/2,T=F?-.5*Math.PI:.5*Math.PI}s.save(),s.translate(P,_),s.rotate(T),s.textAlign="center",s.textBaseline="middle",s.fillStyle=x,s.font=y.font,s.fillText(h.labelString,0,0),s.restore()}if(c.drawBorder){s.lineWidth=i.getValueAtIndexOrDefault(c.lineWidth,0),s.strokeStyle=i.getValueAtIndexOrDefault(c.color,0);var R=a.left,L=a.right,V=a.top,O=a.bottom,z=i.aliasPixel(s.lineWidth);p?(V=O="top"===o.position?a.bottom:a.top,V+=z,O+=z):(R=L="left"===o.position?a.right:a.left,R+=z,L+=z),s.beginPath(),s.moveTo(R,V),s.lineTo(L,O),s.stroke()}}}})}},{}],32:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers;t.scaleService={constructors:{},defaults:{},registerScaleType:function(t,n,i){this.constructors[t]=n,this.defaults[t]=e.clone(i)},getScaleConstructor:function(t){return this.constructors.hasOwnProperty(t)?this.constructors[t]:void 0},getScaleDefaults:function(n){return this.defaults.hasOwnProperty(n)?e.scaleMerge(t.defaults.scale,this.defaults[n]):{}},updateScaleDefaults:function(t,n){var i=this.defaults;i.hasOwnProperty(t)&&(i[t]=e.extend(i[t],n))},addScalesToLayout:function(n){e.each(n.scales,function(e){e.fullWidth=e.options.fullWidth,e.position=e.options.position,e.weight=e.options.weight,t.layoutService.addBox(n,e)})}}}},{}],33:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers;t.Ticks={generators:{linear:function(t,n){var i,a=[];if(t.stepSize&&t.stepSize>0)i=t.stepSize;else{var o=e.niceNum(n.max-n.min,!1);i=e.niceNum(o/(t.maxTicks-1),!0)}var r=Math.floor(n.min/i)*i,l=Math.ceil(n.max/i)*i;t.min&&t.max&&t.stepSize&&e.almostWhole((t.max-t.min)/t.stepSize,i/1e3)&&(r=t.min,l=t.max);var s=(l-r)/i;s=e.almostEquals(s,Math.round(s),i/1e3)?Math.round(s):Math.ceil(s),a.push(void 0!==t.min?t.min:r);for(var u=1;u<s;++u)a.push(r+u*i);return a.push(void 0!==t.max?t.max:l),a},logarithmic:function(t,n){var i,a,o=[],r=e.getValueOrDefault,l=r(t.min,Math.pow(10,Math.floor(e.log10(n.min)))),s=Math.floor(e.log10(n.max)),u=Math.ceil(n.max/Math.pow(10,s));0===l?(i=Math.floor(e.log10(n.minNotZero)),a=Math.floor(n.minNotZero/Math.pow(10,i)),o.push(l),l=a*Math.pow(10,i)):(i=Math.floor(e.log10(l)),a=Math.floor(l/Math.pow(10,i)));do o.push(l),++a,10===a&&(a=1,++i),l=a*Math.pow(10,i);while(i<s||i===s&&a<u);var d=r(t.max,l);return o.push(d),o}},formatters:{values:function(t){return e.isArray(t)?t:""+t},linear:function(t,n,i){var a=i.length>3?i[2]-i[1]:i[1]-i[0];Math.abs(a)>1&&t!==Math.floor(t)&&(a=t-Math.floor(t));var o=e.log10(Math.abs(a)),r="";if(0!==t){var l=-1*Math.floor(o);l=Math.max(Math.min(l,20),0),r=t.toFixed(l)}else r="0";return r},logarithmic:function(t,n,i){var a=t/Math.pow(10,Math.floor(e.log10(t)));return 0===t?"0":1===a||2===a||5===a||0===n||n===i.length-1?t.toExponential():""}}}}},{}],34:[function(t,e,n){"use strict";e.exports=function(t){function e(t,e){var n=s.color(t);return n.alpha(e*n.alpha()).rgbaString()}function n(t,e){return e&&(s.isArray(e)?Array.prototype.push.apply(t,e):t.push(e)),t}function i(t){var e=t._xScale,n=t._yScale||t._scale,i=t._index,a=t._datasetIndex;return{xLabel:e?e.getLabelForIndex(i,a):"",yLabel:n?n.getLabelForIndex(i,a):"",index:i,datasetIndex:a,x:t._model.x,y:t._model.y}}function a(e){var n=t.defaults.global,i=s.getValueOrDefault;return{xPadding:e.xPadding,yPadding:e.yPadding,xAlign:e.xAlign,yAlign:e.yAlign,bodyFontColor:e.bodyFontColor,_bodyFontFamily:i(e.bodyFontFamily,n.defaultFontFamily),_bodyFontStyle:i(e.bodyFontStyle,n.defaultFontStyle),_bodyAlign:e.bodyAlign,bodyFontSize:i(e.bodyFontSize,n.defaultFontSize),bodySpacing:e.bodySpacing,titleFontColor:e.titleFontColor,_titleFontFamily:i(e.titleFontFamily,n.defaultFontFamily),_titleFontStyle:i(e.titleFontStyle,n.defaultFontStyle),titleFontSize:i(e.titleFontSize,n.defaultFontSize),_titleAlign:e.titleAlign,titleSpacing:e.titleSpacing,titleMarginBottom:e.titleMarginBottom,footerFontColor:e.footerFontColor,_footerFontFamily:i(e.footerFontFamily,n.defaultFontFamily),_footerFontStyle:i(e.footerFontStyle,n.defaultFontStyle),footerFontSize:i(e.footerFontSize,n.defaultFontSize),_footerAlign:e.footerAlign,footerSpacing:e.footerSpacing,footerMarginTop:e.footerMarginTop,caretSize:e.caretSize,cornerRadius:e.cornerRadius,backgroundColor:e.backgroundColor,opacity:0,legendColorBackground:e.multiKeyBackground,displayColors:e.displayColors,borderColor:e.borderColor,borderWidth:e.borderWidth}}function o(t,e){var n=t._chart.ctx,i=2*e.yPadding,a=0,o=e.body,r=o.reduce(function(t,e){return t+e.before.length+e.lines.length+e.after.length},0);r+=e.beforeBody.length+e.afterBody.length;var l=e.title.length,u=e.footer.length,d=e.titleFontSize,c=e.bodyFontSize,h=e.footerFontSize;i+=l*d,i+=l?(l-1)*e.titleSpacing:0,i+=l?e.titleMarginBottom:0,i+=r*c,i+=r?(r-1)*e.bodySpacing:0,i+=u?e.footerMarginTop:0,i+=u*h,i+=u?(u-1)*e.footerSpacing:0;var f=0,g=function(t){a=Math.max(a,n.measureText(t).width+f)};return n.font=s.fontString(d,e._titleFontStyle,e._titleFontFamily),s.each(e.title,g),n.font=s.fontString(c,e._bodyFontStyle,e._bodyFontFamily),s.each(e.beforeBody.concat(e.afterBody),g),f=e.displayColors?c+2:0,s.each(o,function(t){s.each(t.before,g),s.each(t.lines,g),s.each(t.after,g)}),f=0,n.font=s.fontString(h,e._footerFontStyle,e._footerFontFamily),s.each(e.footer,g),a+=2*e.xPadding,{width:a,height:i}}function r(t,e){var n=t._model,i=t._chart,a=t._chart.chartArea,o="center",r="center";n.y<e.height?r="top":n.y>i.height-e.height&&(r="bottom");var l,s,u,d,c,h=(a.left+a.right)/2,f=(a.top+a.bottom)/2;"center"===r?(l=function(t){return t<=h},s=function(t){return t>h}):(l=function(t){return t<=e.width/2},s=function(t){return t>=i.width-e.width/2}),u=function(t){return t+e.width>i.width},d=function(t){return t-e.width<0},c=function(t){return t<=f?"top":"bottom"},l(n.x)?(o="left",u(n.x)&&(o="center",r=c(n.y))):s(n.x)&&(o="right",d(n.x)&&(o="center",r=c(n.y)));var g=t._options;return{xAlign:g.xAlign?g.xAlign:o,yAlign:g.yAlign?g.yAlign:r}}function l(t,e,n){var i=t.x,a=t.y,o=t.caretSize,r=t.caretPadding,l=t.cornerRadius,s=n.xAlign,u=n.yAlign,d=o+r,c=l+r;return"right"===s?i-=e.width:"center"===s&&(i-=e.width/2),"top"===u?a+=d:a-="bottom"===u?e.height+d:e.height/2,"center"===u?"left"===s?i+=d:"right"===s&&(i-=d):"left"===s?i-=c:"right"===s&&(i+=c),{x:i,y:a}}var s=t.helpers;t.defaults.global.tooltips={enabled:!0,custom:null,mode:"nearest",position:"average",intersect:!0,backgroundColor:"rgba(0,0,0,0.8)",titleFontStyle:"bold",titleSpacing:2,titleMarginBottom:6,titleFontColor:"#fff",titleAlign:"left",bodySpacing:2,bodyFontColor:"#fff",bodyAlign:"left",footerFontStyle:"bold",footerSpacing:2,footerMarginTop:6,footerFontColor:"#fff",footerAlign:"left",yPadding:6,xPadding:6,caretPadding:2,caretSize:5,cornerRadius:6,multiKeyBackground:"#fff",displayColors:!0,borderColor:"rgba(0,0,0,0)",borderWidth:0,callbacks:{beforeTitle:s.noop,title:function(t,e){var n="",i=e.labels,a=i?i.length:0;if(t.length>0){var o=t[0];o.xLabel?n=o.xLabel:a>0&&o.index<a&&(n=i[o.index])}return n},afterTitle:s.noop,beforeBody:s.noop,beforeLabel:s.noop,label:function(t,e){var n=e.datasets[t.datasetIndex].label||"";return n&&(n+=": "),n+=t.yLabel},labelColor:function(t,e){var n=e.getDatasetMeta(t.datasetIndex),i=n.data[t.index],a=i._view;return{borderColor:a.borderColor,backgroundColor:a.backgroundColor}},afterLabel:s.noop,afterBody:s.noop,beforeFooter:s.noop,footer:s.noop,afterFooter:s.noop}},t.Tooltip=t.Element.extend({initialize:function(){this._model=a(this._options)},getTitle:function(){var t=this,e=t._options,i=e.callbacks,a=i.beforeTitle.apply(t,arguments),o=i.title.apply(t,arguments),r=i.afterTitle.apply(t,arguments),l=[];return l=n(l,a),l=n(l,o),l=n(l,r)},getBeforeBody:function(){var t=this._options.callbacks.beforeBody.apply(this,arguments);return s.isArray(t)?t:void 0!==t?[t]:[]},getBody:function(t,e){var i=this,a=i._options.callbacks,o=[];return s.each(t,function(t){var r={before:[],lines:[],after:[]};n(r.before,a.beforeLabel.call(i,t,e)),n(r.lines,a.label.call(i,t,e)),n(r.after,a.afterLabel.call(i,t,e)),o.push(r)}),o},getAfterBody:function(){var t=this._options.callbacks.afterBody.apply(this,arguments);return s.isArray(t)?t:void 0!==t?[t]:[]},getFooter:function(){var t=this,e=t._options.callbacks,i=e.beforeFooter.apply(t,arguments),a=e.footer.apply(t,arguments),o=e.afterFooter.apply(t,arguments),r=[];return r=n(r,i),r=n(r,a),r=n(r,o)},update:function(e){var n,u,d=this,c=d._options,h=d._model,f=d._model=a(c),g=d._active,p=d._data,m={xAlign:h.xAlign,yAlign:h.yAlign},v={x:h.x,y:h.y},b={width:h.width,height:h.height},x={x:h.caretX,y:h.caretY};if(g.length){f.opacity=1;var y=[];x=t.Tooltip.positioners[c.position](g,d._eventPosition);var k=[];for(n=0,u=g.length;n<u;++n)k.push(i(g[n]));c.filter&&(k=k.filter(function(t){return c.filter(t,p)})),c.itemSort&&(k=k.sort(function(t,e){return c.itemSort(t,e,p)})),s.each(k,function(t){y.push(c.callbacks.labelColor.call(d,t,d._chart))}),f.title=d.getTitle(k,p),f.beforeBody=d.getBeforeBody(k,p),f.body=d.getBody(k,p),f.afterBody=d.getAfterBody(k,p),f.footer=d.getFooter(k,p),f.x=Math.round(x.x),f.y=Math.round(x.y),f.caretPadding=c.caretPadding,f.labelColors=y,f.dataPoints=k,b=o(this,f),m=r(this,b),v=l(f,b,m)}else f.opacity=0;return f.xAlign=m.xAlign,f.yAlign=m.yAlign,f.x=v.x,f.y=v.y,f.width=b.width,f.height=b.height,f.caretX=x.x,f.caretY=x.y,d._model=f,e&&c.custom&&c.custom.call(d,f),d},drawCaret:function(t,e){var n=this._chart.ctx,i=this._view,a=this.getCaretPosition(t,e,i);n.lineTo(a.x1,a.y1),n.lineTo(a.x2,a.y2),n.lineTo(a.x3,a.y3)},getCaretPosition:function(t,e,n){var i,a,o,r,l,s,u=n.caretSize,d=n.cornerRadius,c=n.xAlign,h=n.yAlign,f=t.x,g=t.y,p=e.width,m=e.height;if("center"===h)l=g+m/2,"left"===c?(i=f,a=i-u,o=i,r=l+u,s=l-u):(i=f+p,a=i+u,o=i,r=l-u,s=l+u);else if("left"===c?(a=f+d+u,i=a-u,o=a+u):"right"===c?(a=f+p-d-u,i=a-u,o=a+u):(a=f+p/2,i=a-u,o=a+u),"top"===h)r=g,l=r-u,s=r;else{r=g+m,l=r+u,s=r;var v=o;o=i,i=v}return{x1:i,x2:a,x3:o,y1:r,y2:l,y3:s}},drawTitle:function(t,n,i,a){var o=n.title;if(o.length){i.textAlign=n._titleAlign,i.textBaseline="top";var r=n.titleFontSize,l=n.titleSpacing;i.fillStyle=e(n.titleFontColor,a),i.font=s.fontString(r,n._titleFontStyle,n._titleFontFamily);var u,d;for(u=0,d=o.length;u<d;++u)i.fillText(o[u],t.x,t.y),t.y+=r+l,u+1===o.length&&(t.y+=n.titleMarginBottom-l)}},drawBody:function(t,n,i,a){var o=n.bodyFontSize,r=n.bodySpacing,l=n.body;i.textAlign=n._bodyAlign,i.textBaseline="top";var u=e(n.bodyFontColor,a);i.fillStyle=u,i.font=s.fontString(o,n._bodyFontStyle,n._bodyFontFamily);var d=0,c=function(e){i.fillText(e,t.x+d,t.y),t.y+=o+r};s.each(n.beforeBody,c);var h=n.displayColors;d=h?o+2:0,s.each(l,function(r,l){s.each(r.before,c),s.each(r.lines,function(r){h&&(i.fillStyle=e(n.legendColorBackground,a),i.fillRect(t.x,t.y,o,o),i.strokeStyle=e(n.labelColors[l].borderColor,a),i.strokeRect(t.x,t.y,o,o),i.fillStyle=e(n.labelColors[l].backgroundColor,a),i.fillRect(t.x+1,t.y+1,o-2,o-2),i.fillStyle=u),c(r)}),s.each(r.after,c)}),d=0,s.each(n.afterBody,c),t.y-=r},drawFooter:function(t,n,i,a){var o=n.footer;o.length&&(t.y+=n.footerMarginTop,i.textAlign=n._footerAlign,i.textBaseline="top",i.fillStyle=e(n.footerFontColor,a),i.font=s.fontString(n.footerFontSize,n._footerFontStyle,n._footerFontFamily),s.each(o,function(e){i.fillText(e,t.x,t.y),t.y+=n.footerFontSize+n.footerSpacing}))},drawBackground:function(t,n,i,a,o){i.fillStyle=e(n.backgroundColor,o),i.strokeStyle=e(n.borderColor,o),i.lineWidth=n.borderWidth;var r=n.xAlign,l=n.yAlign,s=t.x,u=t.y,d=a.width,c=a.height,h=n.cornerRadius;i.beginPath(),i.moveTo(s+h,u),"top"===l&&this.drawCaret(t,a),i.lineTo(s+d-h,u),i.quadraticCurveTo(s+d,u,s+d,u+h),"center"===l&&"right"===r&&this.drawCaret(t,a),i.lineTo(s+d,u+c-h),i.quadraticCurveTo(s+d,u+c,s+d-h,u+c),"bottom"===l&&this.drawCaret(t,a),i.lineTo(s+h,u+c),i.quadraticCurveTo(s,u+c,s,u+c-h),"center"===l&&"left"===r&&this.drawCaret(t,a),i.lineTo(s,u+h),i.quadraticCurveTo(s,u,s+h,u),i.closePath(),i.fill(),n.borderWidth>0&&i.stroke()},draw:function(){var t=this._chart.ctx,e=this._view;if(0!==e.opacity){var n={width:e.width,height:e.height},i={x:e.x,y:e.y},a=Math.abs(e.opacity<.001)?0:e.opacity,o=e.title.length||e.beforeBody.length||e.body.length||e.afterBody.length||e.footer.length;this._options.enabled&&o&&(this.drawBackground(i,e,t,n,a),i.x+=e.xPadding,i.y+=e.yPadding,this.drawTitle(i,e,t,a),this.drawBody(i,e,t,a),this.drawFooter(i,e,t,a))}},handleEvent:function(t){var e=this,n=e._options,i=!1;if(e._lastActive=e._lastActive||[],"mouseout"===t.type?e._active=[]:e._active=e._chart.getElementsAtEventForMode(t,n.mode,n),i=!s.arrayEquals(e._active,e._lastActive),!i)return!1;if(e._lastActive=e._active,n.enabled||n.custom){e._eventPosition={x:t.x,y:t.y};var a=e._model;e.update(!0),e.pivot(),i|=a.x!==e._model.x||a.y!==e._model.y}return i}}),t.Tooltip.positioners={average:function(t){if(!t.length)return!1;var e,n,i=0,a=0,o=0;for(e=0,n=t.length;e<n;++e){var r=t[e];if(r&&r.hasValue()){var l=r.tooltipPosition();i+=l.x,a+=l.y,++o}}return{x:Math.round(i/o),y:Math.round(a/o)}},nearest:function(t,e){var n,i,a,o=e.x,r=e.y,l=Number.POSITIVE_INFINITY;for(i=0,a=t.length;i<a;++i){var u=t[i];if(u&&u.hasValue()){var d=u.getCenterPoint(),c=s.distanceBetweenPoints(e,d);c<l&&(l=c,n=u)}}if(n){var h=n.tooltipPosition();o=h.x,r=h.y}return{x:o,y:r}}}}},{}],35:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers,n=t.defaults.global;n.elements.arc={backgroundColor:n.defaultColor,borderColor:"#fff",borderWidth:2},t.elements.Arc=t.Element.extend({inLabelRange:function(t){var e=this._view;return!!e&&Math.pow(t-e.x,2)<Math.pow(e.radius+e.hoverRadius,2)},inRange:function(t,n){var i=this._view;if(i){for(var a=e.getAngleFromPoint(i,{x:t,y:n}),o=a.angle,r=a.distance,l=i.startAngle,s=i.endAngle;s<l;)s+=2*Math.PI;for(;o>s;)o-=2*Math.PI;for(;o<l;)o+=2*Math.PI;var u=o>=l&&o<=s,d=r>=i.innerRadius&&r<=i.outerRadius;return u&&d}return!1},getCenterPoint:function(){var t=this._view,e=(t.startAngle+t.endAngle)/2,n=(t.innerRadius+t.outerRadius)/2;return{x:t.x+Math.cos(e)*n,y:t.y+Math.sin(e)*n}},getArea:function(){var t=this._view;return Math.PI*((t.endAngle-t.startAngle)/(2*Math.PI))*(Math.pow(t.outerRadius,2)-Math.pow(t.innerRadius,2))},tooltipPosition:function(){var t=this._view,e=t.startAngle+(t.endAngle-t.startAngle)/2,n=(t.outerRadius-t.innerRadius)/2+t.innerRadius;return{x:t.x+Math.cos(e)*n,y:t.y+Math.sin(e)*n}},draw:function(){var t=this._chart.ctx,e=this._view,n=e.startAngle,i=e.endAngle;t.beginPath(),t.arc(e.x,e.y,e.outerRadius,n,i),t.arc(e.x,e.y,e.innerRadius,i,n,!0),t.closePath(),t.strokeStyle=e.borderColor,t.lineWidth=e.borderWidth,t.fillStyle=e.backgroundColor,t.fill(),t.lineJoin="bevel",e.borderWidth&&t.stroke()}})}},{}],36:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers,n=t.defaults.global;t.defaults.global.elements.line={tension:.4,backgroundColor:n.defaultColor,borderWidth:3,borderColor:n.defaultColor,borderCapStyle:"butt",borderDash:[],borderDashOffset:0,borderJoinStyle:"miter",capBezierPoints:!0,fill:!0},t.elements.Line=t.Element.extend({draw:function(){var t,i,a,o,r=this,l=r._view,s=r._chart.ctx,u=l.spanGaps,d=r._children.slice(),c=n.elements.line,h=-1;for(r._loop&&d.length&&d.push(d[0]),s.save(),s.lineCap=l.borderCapStyle||c.borderCapStyle,s.setLineDash&&s.setLineDash(l.borderDash||c.borderDash),s.lineDashOffset=l.borderDashOffset||c.borderDashOffset,s.lineJoin=l.borderJoinStyle||c.borderJoinStyle,s.lineWidth=l.borderWidth||c.borderWidth,s.strokeStyle=l.borderColor||n.defaultColor,s.beginPath(),h=-1,t=0;t<d.length;++t)i=d[t],a=e.previousItem(d,t),o=i._view,0===t?o.skip||(s.moveTo(o.x,o.y),h=t):(a=h===-1?a:d[h],o.skip||(h!==t-1&&!u||h===-1?s.moveTo(o.x,o.y):e.canvas.lineTo(s,a._view,i._view),h=t));s.stroke(),s.restore()}})}},{}],37:[function(t,e,n){"use strict";e.exports=function(t){function e(t){var e=this._view;return!!e&&Math.pow(t-e.x,2)<Math.pow(e.radius+e.hitRadius,2)}function n(t){var e=this._view;return!!e&&Math.pow(t-e.y,2)<Math.pow(e.radius+e.hitRadius,2)}var i=t.helpers,a=t.defaults.global,o=a.defaultColor;a.elements.point={radius:3,pointStyle:"circle",backgroundColor:o,borderWidth:1,borderColor:o,hitRadius:1,hoverRadius:4,hoverBorderWidth:1},t.elements.Point=t.Element.extend({inRange:function(t,e){var n=this._view;return!!n&&Math.pow(t-n.x,2)+Math.pow(e-n.y,2)<Math.pow(n.hitRadius+n.radius,2)},inLabelRange:e,inXRange:e,inYRange:n,getCenterPoint:function(){var t=this._view;return{x:t.x,y:t.y}},getArea:function(){return Math.PI*Math.pow(this._view.radius,2)},tooltipPosition:function(){var t=this._view;return{x:t.x,y:t.y,padding:t.radius+t.borderWidth}},draw:function(e){var n=this._view,r=this._model,l=this._chart.ctx,s=n.pointStyle,u=n.radius,d=n.x,c=n.y,h=t.helpers.color,f=1.01,g=0;n.skip||(l.strokeStyle=n.borderColor||o,l.lineWidth=i.getValueOrDefault(n.borderWidth,a.elements.point.borderWidth),l.fillStyle=n.backgroundColor||o,void 0!==e&&(r.x<e.left||e.right*f<r.x||r.y<e.top||e.bottom*f<r.y)&&(r.x<e.left?g=(d-r.x)/(e.left-r.x):e.right*f<r.x?g=(r.x-d)/(r.x-e.right):r.y<e.top?g=(c-r.y)/(e.top-r.y):e.bottom*f<r.y&&(g=(r.y-c)/(r.y-e.bottom)),g=Math.round(100*g)/100,l.strokeStyle=h(l.strokeStyle).alpha(g).rgbString(),l.fillStyle=h(l.fillStyle).alpha(g).rgbString()),t.canvasHelpers.drawPoint(l,s,u,d,c))}})}},{}],38:[function(t,e,n){"use strict";e.exports=function(t){function e(t){return void 0!==t._view.width}function n(t){var n,i,a,o,r=t._view;if(e(t)){var l=r.width/2;n=r.x-l,i=r.x+l,a=Math.min(r.y,r.base),o=Math.max(r.y,r.base)}else{var s=r.height/2;n=Math.min(r.x,r.base),i=Math.max(r.x,r.base),a=r.y-s,o=r.y+s}return{left:n,top:a,right:i,bottom:o}}var i=t.defaults.global;i.elements.rectangle={backgroundColor:i.defaultColor,borderWidth:0,borderColor:i.defaultColor,borderSkipped:"bottom"},t.elements.Rectangle=t.Element.extend({draw:function(){function t(t){return v[(x+t)%4]}var e,n,i,a,o,r,l,s=this._chart.ctx,u=this._view,d=u.borderWidth;if(u.horizontal?(e=u.base,n=u.x,i=u.y-u.height/2,a=u.y+u.height/2,o=n>e?1:-1,r=1,l=u.borderSkipped||"left"):(e=u.x-u.width/2,n=u.x+u.width/2,i=u.y,a=u.base,o=1,r=a>i?1:-1,l=u.borderSkipped||"bottom"),d){var c=Math.min(Math.abs(e-n),Math.abs(i-a));d=d>c?c:d;var h=d/2,f=e+("left"!==l?h*o:0),g=n+("right"!==l?-h*o:0),p=i+("top"!==l?h*r:0),m=a+("bottom"!==l?-h*r:0);f!==g&&(i=p,a=m),p!==m&&(e=f,n=g)}s.beginPath(),s.fillStyle=u.backgroundColor,s.strokeStyle=u.borderColor,s.lineWidth=d;var v=[[e,a],[e,i],[n,i],[n,a]],b=["bottom","left","top","right"],x=b.indexOf(l,0);x===-1&&(x=0);var y=t(0);s.moveTo(y[0],y[1]);for(var k=1;k<4;k++)y=t(k),s.lineTo(y[0],y[1]);s.fill(),d&&s.stroke()},height:function(){var t=this._view;return t.base-t.y},inRange:function(t,e){var i=!1;if(this._view){var a=n(this);i=t>=a.left&&t<=a.right&&e>=a.top&&e<=a.bottom}return i},inLabelRange:function(t,i){var a=this;if(!a._view)return!1;var o=!1,r=n(a);return o=e(a)?t>=r.left&&t<=r.right:i>=r.top&&i<=r.bottom},inXRange:function(t){var e=n(this);return t>=e.left&&t<=e.right},inYRange:function(t){var e=n(this);return t>=e.top&&t<=e.bottom},getCenterPoint:function(){var t,n,i=this._view;return e(this)?(t=i.x,n=(i.y+i.base)/2):(t=(i.x+i.base)/2,n=i.y),{x:t,y:n}},getArea:function(){var t=this._view;return t.width*Math.abs(t.y-t.base)},tooltipPosition:function(){var t=this._view;return{x:t.x,y:t.y}}})}},{}],39:[function(t,e,n){"use strict";e.exports=function(t){function e(t,e){var n=s.getStyle(t,e),i=n&&n.match(/^(\d+)(\.\d+)?px$/);return i?Number(i[1]):void 0}function n(t,n){var i=t.style,a=t.getAttribute("height"),o=t.getAttribute("width");if(t._chartjs={initial:{height:a,width:o,style:{display:i.display,height:i.height,width:i.width}}},i.display=i.display||"block",null===o||""===o){var r=e(t,"width");void 0!==r&&(t.width=r)}if(null===a||""===a)if(""===t.style.height)t.height=t.width/(n.options.aspectRatio||2);else{var l=e(t,"height");void 0!==r&&(t.height=l)}return t}function i(t,e,n,i,a){return{type:t,chart:e,native:a||null,x:void 0!==n?n:null,y:void 0!==i?i:null}}function a(t,e){var n=u[t.type]||t.type,a=s.getRelativePosition(t,e);return i(n,e,a.x,a.y,t)}function o(t){var e=document.createElement("iframe");return e.className="chartjs-hidden-iframe",e.style.cssText="display:block;overflow:hidden;border:0;margin:0;top:0;left:0;bottom:0;right:0;height:100%;width:100%;position:absolute;pointer-events:none;z-index:-1;",e.tabIndex=-1,s.addEvent(e,"load",function(){s.addEvent(e.contentWindow||e,"resize",t),t()}),e}function r(t,e,n){var a=t._chartjs={ticking:!1},r=function(){a.ticking||(a.ticking=!0,s.requestAnimFrame.call(window,function(){if(a.resizer)return a.ticking=!1,e(i("resize",n))}))};a.resizer=o(r),t.insertBefore(a.resizer,t.firstChild)}function l(t){if(t&&t._chartjs){var e=t._chartjs.resizer;e&&(e.parentNode.removeChild(e),t._chartjs.resizer=null),delete t._chartjs}}var s=t.helpers,u={touchstart:"mousedown",touchmove:"mousemove",touchend:"mouseup",pointerenter:"mouseenter",pointerdown:"mousedown",pointermove:"mousemove",pointerup:"mouseup",pointerleave:"mouseout",pointerout:"mouseout"};return{acquireContext:function(t,e){"string"==typeof t?t=document.getElementById(t):t.length&&(t=t[0]),t&&t.canvas&&(t=t.canvas);var i=t&&t.getContext&&t.getContext("2d");return i&&i.canvas===t?(n(t,e),i):null},releaseContext:function(t){var e=t.canvas;if(e._chartjs){var n=e._chartjs.initial;["height","width"].forEach(function(t){var i=n[t];void 0===i||null===i?e.removeAttribute(t):e.setAttribute(t,i)}),s.each(n.style||{},function(t,n){e.style[n]=t}),e.width=e.width,delete e._chartjs}},addEventListener:function(t,e,n){var i=t.canvas;if("resize"===e)return void r(i.parentNode,n,t);var o=n._chartjs||(n._chartjs={}),l=o.proxies||(o.proxies={}),u=l[t.id+"_"+e]=function(e){n(a(e,t))};s.addEvent(i,e,u)},removeEventListener:function(t,e,n){var i=t.canvas;if("resize"===e)return void l(i.parentNode,n);var a=n._chartjs||{},o=a.proxies||{},r=o[t.id+"_"+e];r&&s.removeEvent(i,e,r)}}}},{}],40:[function(t,e,n){"use strict";var i=t(39);e.exports=function(t){t.platform={acquireContext:function(){},releaseContext:function(){},addEventListener:function(){},removeEventListener:function(){}},t.helpers.extend(t.platform,i(t))}},{39:39}],41:[function(t,e,n){"use strict";e.exports=function(t){function e(t,e,n){var i,a=t._model||{},o=a.fill;if(void 0===o&&(o=!!a.backgroundColor),o===!1||null===o)return!1;if(o===!0)return"origin";if(i=parseFloat(o,10),isFinite(i)&&Math.floor(i)===i)return"-"!==o[0]&&"+"!==o[0]||(i=e+i),!(i===e||i<0||i>=n)&&i;switch(o){case"bottom":return"start";case"top":return"end";case"zero":return"origin";case"origin":case"start":case"end":return o;default:return!1}}function n(t){var e,n=t.el._model||{},i=t.el._scale||{},a=t.fill,o=null;if(isFinite(a))return null;if("start"===a?o=void 0===n.scaleBottom?i.bottom:n.scaleBottom:"end"===a?o=void 0===n.scaleTop?i.top:n.scaleTop:void 0!==n.scaleZero?o=n.scaleZero:i.getBasePosition?o=i.getBasePosition():i.getBasePixel&&(o=i.getBasePixel()),void 0!==o&&null!==o){if(void 0!==o.x&&void 0!==o.y)return o;if("number"==typeof o&&isFinite(o))return e=i.isHorizontal(),{x:e?o:null,y:e?null:o}}return null}function i(t,e,n){var i,a=t[e],o=a.fill,r=[e];if(!n)return o;for(;o!==!1&&r.indexOf(o)===-1;){if(!isFinite(o))return o;if(i=t[o],!i)return!1;if(i.visible)return o;r.push(o),o=i.fill}return!1}function a(t){var e=t.fill,n="dataset";return e===!1?null:(isFinite(e)||(n="boundary"),d[n](t))}function o(t){return t&&!t.skip}function r(t,e,n,i,a){var o;if(i&&a){for(t.moveTo(e[0].x,e[0].y),o=1;o<i;++o)u.canvas.lineTo(t,e[o-1],e[o]);for(t.lineTo(n[a-1].x,n[a-1].y),o=a-1;o>0;--o)u.canvas.lineTo(t,n[o],n[o-1],!0)}}function l(t,e,n,i,a,l){var s,u,d,c,h,f,g,p=e.length,m=i.spanGaps,v=[],b=[],x=0,y=0;for(t.beginPath(),s=0,u=p+!!l;s<u;++s)d=s%p,c=e[d]._view,h=n(c,d,i),f=o(c),g=o(h),f&&g?(x=v.push(c),y=b.push(h)):x&&y&&(m?(f&&v.push(c),g&&b.push(h)):(r(t,v,b,x,y),x=y=0,v=[],b=[]));r(t,v,b,x,y),t.closePath(),t.fillStyle=a,t.fill()}t.defaults.global.plugins.filler={propagate:!0};var s=t.defaults,u=t.helpers,d={dataset:function(t){var e=t.fill,n=t.chart,i=n.getDatasetMeta(e),a=i&&n.isDatasetVisible(e),o=a&&i.dataset._children||[];return o.length?function(t,e){return o[e]._view||null}:null},boundary:function(t){var e=t.boundary,n=e?e.x:null,i=e?e.y:null;return function(t){return{x:null===n?t.x:n,y:null===i?t.y:i}}}};return{id:"filler",afterDatasetsUpdate:function(o,r){var l,s,u,d,c=(o.data.datasets||[]).length,h=r.propagate,f=[];for(s=0;s<c;++s)l=o.getDatasetMeta(s),u=l.dataset,d=null,u&&u._model&&u instanceof t.elements.Line&&(d={visible:o.isDatasetVisible(s),fill:e(u,s,c),chart:o,el:u}),l.$filler=d,f.push(d);for(s=0;s<c;++s)d=f[s],d&&(d.fill=i(f,s,h),d.boundary=n(d),d.mapper=a(d))},beforeDatasetDraw:function(t,e){var n=e.meta.$filler;if(n){var i=n.el,a=i._view,o=i._children||[],r=n.mapper,u=a.backgroundColor||s.global.defaultColor;r&&u&&o.length&&l(t.ctx,o,r,a,u,i._loop)}}}}},{}],42:[function(t,e,n){"use strict";e.exports=function(t){function e(t,e){return t.usePointStyle?e*Math.SQRT2:t.boxWidth}function n(e,n){var i=new t.Legend({ctx:e.ctx,options:n,chart:e});a.configure(e,i,n),a.addBox(e,i),e.legend=i}var i=t.helpers,a=t.layoutService,o=i.noop;return t.defaults.global.legend={display:!0,position:"top",fullWidth:!0,reverse:!1,weight:1e3,onClick:function(t,e){var n=e.datasetIndex,i=this.chart,a=i.getDatasetMeta(n);a.hidden=null===a.hidden?!i.data.datasets[n].hidden:null,i.update()},onHover:null,labels:{boxWidth:40,padding:10,generateLabels:function(t){var e=t.data;return i.isArray(e.datasets)?e.datasets.map(function(e,n){return{text:e.label,fillStyle:i.isArray(e.backgroundColor)?e.backgroundColor[0]:e.backgroundColor,hidden:!t.isDatasetVisible(n),lineCap:e.borderCapStyle,lineDash:e.borderDash,lineDashOffset:e.borderDashOffset,lineJoin:e.borderJoinStyle,lineWidth:e.borderWidth,strokeStyle:e.borderColor,pointStyle:e.pointStyle,datasetIndex:n}},this):[]}}},t.Legend=t.Element.extend({initialize:function(t){i.extend(this,t),this.legendHitBoxes=[],this.doughnutMode=!1},beforeUpdate:o,update:function(t,e,n){var i=this;return i.beforeUpdate(),i.maxWidth=t,i.maxHeight=e,i.margins=n,i.beforeSetDimensions(),i.setDimensions(),i.afterSetDimensions(),i.beforeBuildLabels(),i.buildLabels(),i.afterBuildLabels(),i.beforeFit(),i.fit(),i.afterFit(),i.afterUpdate(),i.minSize},afterUpdate:o,beforeSetDimensions:o,setDimensions:function(){var t=this;t.isHorizontal()?(t.width=t.maxWidth,t.left=0,t.right=t.width):(t.height=t.maxHeight,t.top=0,t.bottom=t.height),t.paddingLeft=0,t.paddingTop=0,t.paddingRight=0,t.paddingBottom=0,t.minSize={width:0,height:0}},afterSetDimensions:o,beforeBuildLabels:o,buildLabels:function(){var t=this,e=t.options.labels,n=e.generateLabels.call(t,t.chart);e.filter&&(n=n.filter(function(n){return e.filter(n,t.chart.data)})),t.options.reverse&&n.reverse(),t.legendItems=n},afterBuildLabels:o,beforeFit:o,fit:function(){var n=this,a=n.options,o=a.labels,r=a.display,l=n.ctx,s=t.defaults.global,u=i.getValueOrDefault,d=u(o.fontSize,s.defaultFontSize),c=u(o.fontStyle,s.defaultFontStyle),h=u(o.fontFamily,s.defaultFontFamily),f=i.fontString(d,c,h),g=n.legendHitBoxes=[],p=n.minSize,m=n.isHorizontal();if(m?(p.width=n.maxWidth,p.height=r?10:0):(p.width=r?10:0,p.height=n.maxHeight),r)if(l.font=f,m){var v=n.lineWidths=[0],b=n.legendItems.length?d+o.padding:0;l.textAlign="left",l.textBaseline="top",i.each(n.legendItems,function(t,i){var a=e(o,d),r=a+d/2+l.measureText(t.text).width;v[v.length-1]+r+o.padding>=n.width&&(b+=d+o.padding,v[v.length]=n.left),g[i]={left:0,top:0,width:r,height:d},v[v.length-1]+=r+o.padding}),p.height+=b}else{var x=o.padding,y=n.columnWidths=[],k=o.padding,w=0,M=0,S=d+x;i.each(n.legendItems,function(t,n){var i=e(o,d),a=i+d/2+l.measureText(t.text).width;M+S>p.height&&(k+=w+o.padding,y.push(w),w=0,M=0),w=Math.max(w,a),M+=S,g[n]={left:0,top:0,width:a,height:d}}),k+=w,y.push(w),p.width+=k}n.width=p.width,n.height=p.height},afterFit:o,isHorizontal:function(){return"top"===this.options.position||"bottom"===this.options.position},draw:function(){var n=this,a=n.options,o=a.labels,r=t.defaults.global,l=r.elements.line,s=n.width,u=n.lineWidths;if(a.display){var d,c=n.ctx,h=i.getValueOrDefault,f=h(o.fontColor,r.defaultFontColor),g=h(o.fontSize,r.defaultFontSize),p=h(o.fontStyle,r.defaultFontStyle),m=h(o.fontFamily,r.defaultFontFamily),v=i.fontString(g,p,m);c.textAlign="left",c.textBaseline="top",c.lineWidth=.5,c.strokeStyle=f,c.fillStyle=f,c.font=v;var b=e(o,g),x=n.legendHitBoxes,y=function(e,n,i){if(!(isNaN(b)||b<=0)){c.save(),c.fillStyle=h(i.fillStyle,r.defaultColor),c.lineCap=h(i.lineCap,l.borderCapStyle),c.lineDashOffset=h(i.lineDashOffset,l.borderDashOffset),c.lineJoin=h(i.lineJoin,l.borderJoinStyle),c.lineWidth=h(i.lineWidth,l.borderWidth),c.strokeStyle=h(i.strokeStyle,r.defaultColor);var o=0===h(i.lineWidth,l.borderWidth);if(c.setLineDash&&c.setLineDash(h(i.lineDash,l.borderDash)),a.labels&&a.labels.usePointStyle){var s=g*Math.SQRT2/2,u=s/Math.SQRT2,d=e+u,f=n+u;t.canvasHelpers.drawPoint(c,i.pointStyle,s,d,f)}else o||c.strokeRect(e,n,b,g),c.fillRect(e,n,b,g);c.restore()}},k=function(t,e,n,i){c.fillText(n.text,b+g/2+t,e),n.hidden&&(c.beginPath(),c.lineWidth=2,c.moveTo(b+g/2+t,e+g/2),c.lineTo(b+g/2+t+i,e+g/2),c.stroke())},w=n.isHorizontal();d=w?{x:n.left+(s-u[0])/2,y:n.top+o.padding,line:0}:{x:n.left+o.padding,y:n.top+o.padding,line:0};var M=g+o.padding;i.each(n.legendItems,function(t,e){var i=c.measureText(t.text).width,a=b+g/2+i,r=d.x,l=d.y;w?r+a>=s&&(l=d.y+=M,d.line++,r=d.x=n.left+(s-u[d.line])/2):l+M>n.bottom&&(r=d.x=r+n.columnWidths[d.line]+o.padding,l=d.y=n.top+o.padding,d.line++),y(r,l,t),x[e].left=r,x[e].top=l,k(r,l,t,i),w?d.x+=a+o.padding:d.y+=M})}},handleEvent:function(t){var e=this,n=e.options,i="mouseup"===t.type?"click":t.type,a=!1;if("mousemove"===i){if(!n.onHover)return}else{if("click"!==i)return;if(!n.onClick)return}var o=t.x,r=t.y;if(o>=e.left&&o<=e.right&&r>=e.top&&r<=e.bottom)for(var l=e.legendHitBoxes,s=0;s<l.length;++s){var u=l[s];if(o>=u.left&&o<=u.left+u.width&&r>=u.top&&r<=u.top+u.height){if("click"===i){n.onClick.call(e,t.native,e.legendItems[s]),a=!0;break}if("mousemove"===i){n.onHover.call(e,t.native,e.legendItems[s]),a=!0;break}}}return a}}),{id:"legend",beforeInit:function(t){var e=t.options.legend;e&&n(t,e)},beforeUpdate:function(e){var o=e.options.legend,r=e.legend;o?(o=i.configMerge(t.defaults.global.legend,o),r?(a.configure(e,r,o),r.options=o):n(e,o)):r&&(a.removeBox(e,r),delete e.legend)},afterEvent:function(t,e){var n=t.legend;n&&n.handleEvent(e)}}}},{}],43:[function(t,e,n){"use strict";e.exports=function(t){function e(e,n){var a=new t.Title({ctx:e.ctx,options:n,chart:e});i.configure(e,a,n),i.addBox(e,a),e.titleBlock=a}var n=t.helpers,i=t.layoutService,a=n.noop;return t.defaults.global.title={display:!1,position:"top",fullWidth:!0,weight:2e3,fontStyle:"bold",padding:10,text:""},t.Title=t.Element.extend({initialize:function(t){var e=this;n.extend(e,t),e.legendHitBoxes=[];
+},beforeUpdate:a,update:function(t,e,n){var i=this;return i.beforeUpdate(),i.maxWidth=t,i.maxHeight=e,i.margins=n,i.beforeSetDimensions(),i.setDimensions(),i.afterSetDimensions(),i.beforeBuildLabels(),i.buildLabels(),i.afterBuildLabels(),i.beforeFit(),i.fit(),i.afterFit(),i.afterUpdate(),i.minSize},afterUpdate:a,beforeSetDimensions:a,setDimensions:function(){var t=this;t.isHorizontal()?(t.width=t.maxWidth,t.left=0,t.right=t.width):(t.height=t.maxHeight,t.top=0,t.bottom=t.height),t.paddingLeft=0,t.paddingTop=0,t.paddingRight=0,t.paddingBottom=0,t.minSize={width:0,height:0}},afterSetDimensions:a,beforeBuildLabels:a,buildLabels:a,afterBuildLabels:a,beforeFit:a,fit:function(){var e=this,i=n.getValueOrDefault,a=e.options,o=t.defaults.global,r=a.display,l=i(a.fontSize,o.defaultFontSize),s=e.minSize;e.isHorizontal()?(s.width=e.maxWidth,s.height=r?l+2*a.padding:0):(s.width=r?l+2*a.padding:0,s.height=e.maxHeight),e.width=s.width,e.height=s.height},afterFit:a,isHorizontal:function(){var t=this.options.position;return"top"===t||"bottom"===t},draw:function(){var e=this,i=e.ctx,a=n.getValueOrDefault,o=e.options,r=t.defaults.global;if(o.display){var l,s,u,d=a(o.fontSize,r.defaultFontSize),c=a(o.fontStyle,r.defaultFontStyle),h=a(o.fontFamily,r.defaultFontFamily),f=n.fontString(d,c,h),g=0,p=e.top,m=e.left,v=e.bottom,b=e.right;i.fillStyle=a(o.fontColor,r.defaultFontColor),i.font=f,e.isHorizontal()?(l=m+(b-m)/2,s=p+(v-p)/2,u=b-m):(l="left"===o.position?m+d/2:b-d/2,s=p+(v-p)/2,u=v-p,g=Math.PI*("left"===o.position?-.5:.5)),i.save(),i.translate(l,s),i.rotate(g),i.textAlign="center",i.textBaseline="middle",i.fillText(o.text,0,0,u),i.restore()}}}),{id:"title",beforeInit:function(t){var n=t.options.title;n&&e(t,n)},beforeUpdate:function(a){var o=a.options.title,r=a.titleBlock;o?(o=n.configMerge(t.defaults.global.title,o),r?(i.configure(a,r,o),r.options=o):e(a,o)):r&&(t.layoutService.removeBox(a,r),delete a.titleBlock)}}}},{}],44:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers,n={position:"bottom"},i=t.Scale.extend({getLabels:function(){var t=this.chart.data;return(this.isHorizontal()?t.xLabels:t.yLabels)||t.labels},determineDataLimits:function(){var t=this,n=t.getLabels();t.minIndex=0,t.maxIndex=n.length-1;var i;void 0!==t.options.ticks.min&&(i=e.indexOf(n,t.options.ticks.min),t.minIndex=i!==-1?i:t.minIndex),void 0!==t.options.ticks.max&&(i=e.indexOf(n,t.options.ticks.max),t.maxIndex=i!==-1?i:t.maxIndex),t.min=n[t.minIndex],t.max=n[t.maxIndex]},buildTicks:function(){var t=this,e=t.getLabels();t.ticks=0===t.minIndex&&t.maxIndex===e.length-1?e:e.slice(t.minIndex,t.maxIndex+1)},getLabelForIndex:function(t,e){var n=this,i=n.chart.data,a=n.isHorizontal();return i.yLabels&&!a?n.getRightValue(i.datasets[e].data[t]):n.ticks[t-n.minIndex]},getPixelForValue:function(t,e,n,i){var a,o=this,r=Math.max(o.maxIndex+1-o.minIndex-(o.options.gridLines.offsetGridLines?0:1),1);if(void 0!==t&&null!==t&&(a=o.isHorizontal()?t.x:t.y),void 0!==a||void 0!==t&&isNaN(e)){var l=o.getLabels();t=a||t;var s=l.indexOf(t);e=s!==-1?s:e}if(o.isHorizontal()){var u=o.width/r,d=u*(e-o.minIndex);return(o.options.gridLines.offsetGridLines&&i||o.maxIndex===o.minIndex&&i)&&(d+=u/2),o.left+Math.round(d)}var c=o.height/r,h=c*(e-o.minIndex);return o.options.gridLines.offsetGridLines&&i&&(h+=c/2),o.top+Math.round(h)},getPixelForTick:function(t,e){return this.getPixelForValue(this.ticks[t],t+this.minIndex,null,e)},getValueForPixel:function(t){var e,n=this,i=Math.max(n.ticks.length-(n.options.gridLines.offsetGridLines?0:1),1),a=n.isHorizontal(),o=(a?n.width:n.height)/i;return t-=a?n.left:n.top,n.options.gridLines.offsetGridLines&&(t-=o/2),e=t<=0?0:Math.round(t/o)},getBasePixel:function(){return this.bottom}});t.scaleService.registerScaleType("category",i,n)}},{}],45:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers,n={position:"left",ticks:{callback:t.Ticks.formatters.linear}},i=t.LinearScaleBase.extend({determineDataLimits:function(){function t(t){return l?t.xAxisID===n.id:t.yAxisID===n.id}var n=this,i=n.options,a=n.chart,o=a.data,r=o.datasets,l=n.isHorizontal(),s=0,u=1;n.min=null,n.max=null;var d=i.stacked;if(void 0===d&&e.each(r,function(e,n){if(!d){var i=a.getDatasetMeta(n);a.isDatasetVisible(n)&&t(i)&&void 0!==i.stack&&(d=!0)}}),i.stacked||d){var c={};e.each(r,function(o,r){var l=a.getDatasetMeta(r),s=[l.type,void 0===i.stacked&&void 0===l.stack?r:"",l.stack].join(".");void 0===c[s]&&(c[s]={positiveValues:[],negativeValues:[]});var u=c[s].positiveValues,d=c[s].negativeValues;a.isDatasetVisible(r)&&t(l)&&e.each(o.data,function(t,e){var a=+n.getRightValue(t);isNaN(a)||l.data[e].hidden||(u[e]=u[e]||0,d[e]=d[e]||0,i.relativePoints?u[e]=100:a<0?d[e]+=a:u[e]+=a)})}),e.each(c,function(t){var i=t.positiveValues.concat(t.negativeValues),a=e.min(i),o=e.max(i);n.min=null===n.min?a:Math.min(n.min,a),n.max=null===n.max?o:Math.max(n.max,o)})}else e.each(r,function(i,o){var r=a.getDatasetMeta(o);a.isDatasetVisible(o)&&t(r)&&e.each(i.data,function(t,e){var i=+n.getRightValue(t);isNaN(i)||r.data[e].hidden||(null===n.min?n.min=i:i<n.min&&(n.min=i),null===n.max?n.max=i:i>n.max&&(n.max=i))})});n.min=isFinite(n.min)?n.min:s,n.max=isFinite(n.max)?n.max:u,this.handleTickRangeOptions()},getTickLimit:function(){var n,i=this,a=i.options.ticks;if(i.isHorizontal())n=Math.min(a.maxTicksLimit?a.maxTicksLimit:11,Math.ceil(i.width/50));else{var o=e.getValueOrDefault(a.fontSize,t.defaults.global.defaultFontSize);n=Math.min(a.maxTicksLimit?a.maxTicksLimit:11,Math.ceil(i.height/(2*o)))}return n},handleDirectionalChanges:function(){this.isHorizontal()||this.ticks.reverse()},getLabelForIndex:function(t,e){return+this.getRightValue(this.chart.data.datasets[e].data[t])},getPixelForValue:function(t){var e,n=this,i=n.start,a=+n.getRightValue(t),o=n.end-i;return n.isHorizontal()?(e=n.left+n.width/o*(a-i),Math.round(e)):(e=n.bottom-n.height/o*(a-i),Math.round(e))},getValueForPixel:function(t){var e=this,n=e.isHorizontal(),i=n?e.width:e.height,a=(n?t-e.left:e.bottom-t)/i;return e.start+(e.end-e.start)*a},getPixelForTick:function(t){return this.getPixelForValue(this.ticksAsNumbers[t])}});t.scaleService.registerScaleType("linear",i,n)}},{}],46:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers,n=e.noop;t.LinearScaleBase=t.Scale.extend({handleTickRangeOptions:function(){var t=this,n=t.options,i=n.ticks;if(i.beginAtZero){var a=e.sign(t.min),o=e.sign(t.max);a<0&&o<0?t.max=0:a>0&&o>0&&(t.min=0)}void 0!==i.min?t.min=i.min:void 0!==i.suggestedMin&&(null===t.min?t.min=i.suggestedMin:t.min=Math.min(t.min,i.suggestedMin)),void 0!==i.max?t.max=i.max:void 0!==i.suggestedMax&&(null===t.max?t.max=i.suggestedMax:t.max=Math.max(t.max,i.suggestedMax)),t.min===t.max&&(t.max++,i.beginAtZero||t.min--)},getTickLimit:n,handleDirectionalChanges:n,buildTicks:function(){var n=this,i=n.options,a=i.ticks,o=n.getTickLimit();o=Math.max(2,o);var r={maxTicks:o,min:a.min,max:a.max,stepSize:e.getValueOrDefault(a.fixedStepSize,a.stepSize)},l=n.ticks=t.Ticks.generators.linear(r,n);n.handleDirectionalChanges(),n.max=e.max(l),n.min=e.min(l),a.reverse?(l.reverse(),n.start=n.max,n.end=n.min):(n.start=n.min,n.end=n.max)},convertTicksToLabels:function(){var e=this;e.ticksAsNumbers=e.ticks.slice(),e.zeroLineIndex=e.ticks.indexOf(0),t.Scale.prototype.convertTicksToLabels.call(e)}})}},{}],47:[function(t,e,n){"use strict";e.exports=function(t){var e=t.helpers,n={position:"left",ticks:{callback:t.Ticks.formatters.logarithmic}},i=t.Scale.extend({determineDataLimits:function(){function t(t){return u?t.xAxisID===n.id:t.yAxisID===n.id}var n=this,i=n.options,a=i.ticks,o=n.chart,r=o.data,l=r.datasets,s=e.getValueOrDefault,u=n.isHorizontal();n.min=null,n.max=null,n.minNotZero=null;var d=i.stacked;if(void 0===d&&e.each(l,function(e,n){if(!d){var i=o.getDatasetMeta(n);o.isDatasetVisible(n)&&t(i)&&void 0!==i.stack&&(d=!0)}}),i.stacked||d){var c={};e.each(l,function(a,r){var l=o.getDatasetMeta(r),s=[l.type,void 0===i.stacked&&void 0===l.stack?r:"",l.stack].join(".");o.isDatasetVisible(r)&&t(l)&&(void 0===c[s]&&(c[s]=[]),e.each(a.data,function(t,e){var a=c[s],o=+n.getRightValue(t);isNaN(o)||l.data[e].hidden||(a[e]=a[e]||0,i.relativePoints?a[e]=100:a[e]+=o)}))}),e.each(c,function(t){var i=e.min(t),a=e.max(t);n.min=null===n.min?i:Math.min(n.min,i),n.max=null===n.max?a:Math.max(n.max,a)})}else e.each(l,function(i,a){var r=o.getDatasetMeta(a);o.isDatasetVisible(a)&&t(r)&&e.each(i.data,function(t,e){var i=+n.getRightValue(t);isNaN(i)||r.data[e].hidden||(null===n.min?n.min=i:i<n.min&&(n.min=i),null===n.max?n.max=i:i>n.max&&(n.max=i),0!==i&&(null===n.minNotZero||i<n.minNotZero)&&(n.minNotZero=i))})});n.min=s(a.min,n.min),n.max=s(a.max,n.max),n.min===n.max&&(0!==n.min&&null!==n.min?(n.min=Math.pow(10,Math.floor(e.log10(n.min))-1),n.max=Math.pow(10,Math.floor(e.log10(n.max))+1)):(n.min=1,n.max=10))},buildTicks:function(){var n=this,i=n.options,a=i.ticks,o={min:a.min,max:a.max},r=n.ticks=t.Ticks.generators.logarithmic(o,n);n.isHorizontal()||r.reverse(),n.max=e.max(r),n.min=e.min(r),a.reverse?(r.reverse(),n.start=n.max,n.end=n.min):(n.start=n.min,n.end=n.max)},convertTicksToLabels:function(){this.tickValues=this.ticks.slice(),t.Scale.prototype.convertTicksToLabels.call(this)},getLabelForIndex:function(t,e){return+this.getRightValue(this.chart.data.datasets[e].data[t])},getPixelForTick:function(t){return this.getPixelForValue(this.tickValues[t])},getPixelForValue:function(t){var n,i,a,o=this,r=o.start,l=+o.getRightValue(t),s=o.options,u=s.ticks;return o.isHorizontal()?(a=e.log10(o.end)-e.log10(r),0===l?i=o.left:(n=o.width,i=o.left+n/a*(e.log10(l)-e.log10(r)))):(n=o.height,0!==r||u.reverse?0===o.end&&u.reverse?(a=e.log10(o.start)-e.log10(o.minNotZero),i=l===o.end?o.top:l===o.minNotZero?o.top+.02*n:o.top+.02*n+.98*n/a*(e.log10(l)-e.log10(o.minNotZero))):0===l?i=u.reverse?o.top:o.bottom:(a=e.log10(o.end)-e.log10(r),n=o.height,i=o.bottom-n/a*(e.log10(l)-e.log10(r))):(a=e.log10(o.end)-e.log10(o.minNotZero),i=l===r?o.bottom:l===o.minNotZero?o.bottom-.02*n:o.bottom-.02*n-.98*n/a*(e.log10(l)-e.log10(o.minNotZero)))),i},getValueForPixel:function(t){var n,i,a=this,o=e.log10(a.end)-e.log10(a.start);return a.isHorizontal()?(i=a.width,n=a.start*Math.pow(10,(t-a.left)*o/i)):(i=a.height,n=Math.pow(10,(a.bottom-t)*o/i)/a.start),n}});t.scaleService.registerScaleType("logarithmic",i,n)}},{}],48:[function(t,e,n){"use strict";e.exports=function(t){function e(t){var e=t.options;return e.angleLines.display||e.pointLabels.display?t.chart.data.labels.length:0}function n(t){var e=t.options.pointLabels,n=f.getValueOrDefault(e.fontSize,g.defaultFontSize),i=f.getValueOrDefault(e.fontStyle,g.defaultFontStyle),a=f.getValueOrDefault(e.fontFamily,g.defaultFontFamily),o=f.fontString(n,i,a);return{size:n,style:i,family:a,font:o}}function i(t,e,n){return f.isArray(n)?{w:f.longestText(t,t.font,n),h:n.length*e+1.5*(n.length-1)*e}:{w:t.measureText(n).width,h:e}}function a(t,e,n,i,a){return t===i||t===a?{start:e-n/2,end:e+n/2}:t<i||t>a?{start:e-n-5,end:e}:{start:e,end:e+n+5}}function o(t){var o,r,l,s=n(t),u=Math.min(t.height/2,t.width/2),d={r:t.width,l:0,t:t.height,b:0},c={};t.ctx.font=s.font,t._pointLabelSizes=[];var h=e(t);for(o=0;o<h;o++){l=t.getPointPosition(o,u),r=i(t.ctx,s.size,t.pointLabels[o]||""),t._pointLabelSizes[o]=r;var g=t.getIndexAngle(o),p=f.toDegrees(g)%360,m=a(p,l.x,r.w,0,180),v=a(p,l.y,r.h,90,270);m.start<d.l&&(d.l=m.start,c.l=g),m.end>d.r&&(d.r=m.end,c.r=g),v.start<d.t&&(d.t=v.start,c.t=g),v.end>d.b&&(d.b=v.end,c.b=g)}t.setReductions(u,d,c)}function r(t){var e=Math.min(t.height/2,t.width/2);t.drawingArea=Math.round(e),t.setCenterPoint(0,0,0,0)}function l(t){return 0===t||180===t?"center":t<180?"left":"right"}function s(t,e,n,i){if(f.isArray(e))for(var a=n.y,o=1.5*i,r=0;r<e.length;++r)t.fillText(e[r],n.x,a),a+=o;else t.fillText(e,n.x,n.y)}function u(t,e,n){90===t||270===t?n.y-=e.h/2:(t>270||t<90)&&(n.y-=e.h)}function d(t){var i=t.ctx,a=f.getValueOrDefault,o=t.options,r=o.angleLines,d=o.pointLabels;i.lineWidth=r.lineWidth,i.strokeStyle=r.color;var c=t.getDistanceFromCenterForValue(o.reverse?t.min:t.max),h=n(t);i.textBaseline="top";for(var p=e(t)-1;p>=0;p--){if(r.display){var m=t.getPointPosition(p,c);i.beginPath(),i.moveTo(t.xCenter,t.yCenter),i.lineTo(m.x,m.y),i.stroke(),i.closePath()}if(d.display){var v=t.getPointPosition(p,c+5),b=a(d.fontColor,g.defaultFontColor);i.font=h.font,i.fillStyle=b;var x=t.getIndexAngle(p),y=f.toDegrees(x);i.textAlign=l(y),u(y,t._pointLabelSizes[p],v),s(i,t.pointLabels[p]||"",v,h.size)}}}function c(t,n,i,a){var o=t.ctx;if(o.strokeStyle=f.getValueAtIndexOrDefault(n.color,a-1),o.lineWidth=f.getValueAtIndexOrDefault(n.lineWidth,a-1),t.options.gridLines.circular)o.beginPath(),o.arc(t.xCenter,t.yCenter,i,0,2*Math.PI),o.closePath(),o.stroke();else{var r=e(t);if(0===r)return;o.beginPath();var l=t.getPointPosition(0,i);o.moveTo(l.x,l.y);for(var s=1;s<r;s++)l=t.getPointPosition(s,i),o.lineTo(l.x,l.y);o.closePath(),o.stroke()}}function h(t){return f.isNumber(t)?t:0}var f=t.helpers,g=t.defaults.global,p={display:!0,animate:!0,position:"chartArea",angleLines:{display:!0,color:"rgba(0, 0, 0, 0.1)",lineWidth:1},gridLines:{circular:!1},ticks:{showLabelBackdrop:!0,backdropColor:"rgba(255,255,255,0.75)",backdropPaddingY:2,backdropPaddingX:2,callback:t.Ticks.formatters.linear},pointLabels:{display:!0,fontSize:10,callback:function(t){return t}}},m=t.LinearScaleBase.extend({setDimensions:function(){var t=this,e=t.options,n=e.ticks;t.width=t.maxWidth,t.height=t.maxHeight,t.xCenter=Math.round(t.width/2),t.yCenter=Math.round(t.height/2);var i=f.min([t.height,t.width]),a=f.getValueOrDefault(n.fontSize,g.defaultFontSize);t.drawingArea=e.display?i/2-(a/2+n.backdropPaddingY):i/2},determineDataLimits:function(){var t=this,e=t.chart,n=Number.POSITIVE_INFINITY,i=Number.NEGATIVE_INFINITY;f.each(e.data.datasets,function(a,o){if(e.isDatasetVisible(o)){var r=e.getDatasetMeta(o);f.each(a.data,function(e,a){var o=+t.getRightValue(e);isNaN(o)||r.data[a].hidden||(n=Math.min(o,n),i=Math.max(o,i))})}}),t.min=n===Number.POSITIVE_INFINITY?0:n,t.max=i===Number.NEGATIVE_INFINITY?0:i,t.handleTickRangeOptions()},getTickLimit:function(){var t=this.options.ticks,e=f.getValueOrDefault(t.fontSize,g.defaultFontSize);return Math.min(t.maxTicksLimit?t.maxTicksLimit:11,Math.ceil(this.drawingArea/(1.5*e)))},convertTicksToLabels:function(){var e=this;t.LinearScaleBase.prototype.convertTicksToLabels.call(e),e.pointLabels=e.chart.data.labels.map(e.options.pointLabels.callback,e)},getLabelForIndex:function(t,e){return+this.getRightValue(this.chart.data.datasets[e].data[t])},fit:function(){this.options.pointLabels.display?o(this):r(this)},setReductions:function(t,e,n){var i=this,a=e.l/Math.sin(n.l),o=Math.max(e.r-i.width,0)/Math.sin(n.r),r=-e.t/Math.cos(n.t),l=-Math.max(e.b-i.height,0)/Math.cos(n.b);a=h(a),o=h(o),r=h(r),l=h(l),i.drawingArea=Math.min(Math.round(t-(a+o)/2),Math.round(t-(r+l)/2)),i.setCenterPoint(a,o,r,l)},setCenterPoint:function(t,e,n,i){var a=this,o=a.width-e-a.drawingArea,r=t+a.drawingArea,l=n+a.drawingArea,s=a.height-i-a.drawingArea;a.xCenter=Math.round((r+o)/2+a.left),a.yCenter=Math.round((l+s)/2+a.top)},getIndexAngle:function(t){var n=2*Math.PI/e(this),i=this.chart.options&&this.chart.options.startAngle?this.chart.options.startAngle:0,a=i*Math.PI*2/360;return t*n+a},getDistanceFromCenterForValue:function(t){var e=this;if(null===t)return 0;var n=e.drawingArea/(e.max-e.min);return e.options.reverse?(e.max-t)*n:(t-e.min)*n},getPointPosition:function(t,e){var n=this,i=n.getIndexAngle(t)-Math.PI/2;return{x:Math.round(Math.cos(i)*e)+n.xCenter,y:Math.round(Math.sin(i)*e)+n.yCenter}},getPointPositionForValue:function(t,e){return this.getPointPosition(t,this.getDistanceFromCenterForValue(e))},getBasePosition:function(){var t=this,e=t.min,n=t.max;return t.getPointPositionForValue(0,t.beginAtZero?0:e<0&&n<0?n:e>0&&n>0?e:0)},draw:function(){var t=this,e=t.options,n=e.gridLines,i=e.ticks,a=f.getValueOrDefault;if(e.display){var o=t.ctx,r=a(i.fontSize,g.defaultFontSize),l=a(i.fontStyle,g.defaultFontStyle),s=a(i.fontFamily,g.defaultFontFamily),u=f.fontString(r,l,s);f.each(t.ticks,function(l,s){if(s>0||e.reverse){var d=t.getDistanceFromCenterForValue(t.ticksAsNumbers[s]),h=t.yCenter-d;if(n.display&&0!==s&&c(t,n,d,s),i.display){var f=a(i.fontColor,g.defaultFontColor);if(o.font=u,i.showLabelBackdrop){var p=o.measureText(l).width;o.fillStyle=i.backdropColor,o.fillRect(t.xCenter-p/2-i.backdropPaddingX,h-r/2-i.backdropPaddingY,p+2*i.backdropPaddingX,r+2*i.backdropPaddingY)}o.textAlign="center",o.textBaseline="middle",o.fillStyle=f,o.fillText(l,t.xCenter,h)}}}),(e.angleLines.display||e.pointLabels.display)&&d(t)}}});t.scaleService.registerScaleType("radialLinear",m,p)}},{}],49:[function(t,e,n){"use strict";var i=t(1);i="function"==typeof i?i:window.moment,e.exports=function(t){function e(t,e){var n=t.options.time;if("string"==typeof n.parser)return i(e,n.parser);if("function"==typeof n.parser)return n.parser(e);if("function"==typeof e.getMonth||"number"==typeof e)return i(e);if(e.isValid&&e.isValid())return e;var a=n.format;return"string"!=typeof a&&a.call?(console.warn("options.time.format is deprecated and replaced by options.time.parser."),a(e)):i(e,a)}function n(t,e,n,i){for(var a,o=Object.keys(l),r=o.length,s=o.indexOf(t);s<r;s++){a=o[s];var u=l[a],d=u.steps&&u.steps[u.steps.length-1]||u.maxStep;if(void 0===d||Math.ceil((n-e)/(d*u.size))<=i)break}return a}function a(t,e,n,i){var a=l[n],o=a.size,r=Math.ceil((e-t)/o),s=1,u=e-t;if(a.steps)for(var d=a.steps.length,c=0;c<d&&r>i;c++)s=a.steps[c],r=Math.ceil(u/(o*s));else for(;r>i&&i>0;)++s,r=Math.ceil(u/(o*s));return s}function o(t,e,n){var a=[];if(t.maxTicks){var o=t.stepSize;a.push(void 0!==t.min?t.min:n.min);for(var r=i(n.min);r.add(o,t.unit).valueOf()<n.max;)a.push(r.valueOf());var l=t.max||n.max;a[a.length-1]!==l&&a.push(l)}return a}var r=t.helpers,l={millisecond:{size:1,steps:[1,2,5,10,20,50,100,250,500]},second:{size:1e3,steps:[1,2,5,10,30]},minute:{size:6e4,steps:[1,2,5,10,30]},hour:{size:36e5,steps:[1,2,3,6,12]},day:{size:864e5,steps:[1,2,5]},week:{size:6048e5,maxStep:4},month:{size:2628e6,maxStep:3},quarter:{size:7884e6,maxStep:4},year:{size:3154e7,maxStep:!1}},s={position:"bottom",time:{parser:!1,format:!1,unit:!1,round:!1,displayFormat:!1,isoWeekday:!1,minUnit:"millisecond",displayFormats:{millisecond:"h:mm:ss.SSS a",second:"h:mm:ss a",minute:"h:mm:ss a",hour:"MMM D, hA",day:"ll",week:"ll",month:"MMM YYYY",quarter:"[Q]Q - YYYY",year:"YYYY"}},ticks:{autoSkip:!1}};t.Ticks.generators.time=function(t,e){var n,a,r=t.isoWeekday;return"week"===t.unit&&r!==!1?(n=i(e.min).startOf("isoWeek").isoWeekday(r).valueOf(),a=i(e.max).startOf("isoWeek").isoWeekday(r),e.max-a>0&&a.add(1,"week"),a=a.valueOf()):(n=i(e.min).startOf(t.unit).valueOf(),a=i(e.max).startOf(t.unit),e.max-a>0&&a.add(1,t.unit),a=a.valueOf()),o(t,e,{min:n,max:a})};var u=t.Scale.extend({initialize:function(){if(!i)throw new Error("Chart.js - Moment.js could not be found! You must include it before Chart.js to use the time scale. Download at https://momentjs.com");t.Scale.prototype.initialize.call(this)},determineDataLimits:function(){var t,n=this,i=n.options.time,a=Number.MAX_SAFE_INTEGER,o=Number.MIN_SAFE_INTEGER,l=n.chart.data,s={labels:[],datasets:[]};r.each(l.labels,function(r,l){var u=e(n,r);u.isValid()&&(i.round&&u.startOf(i.round),t=u.valueOf(),a=Math.min(t,a),o=Math.max(t,o),s.labels[l]=t)}),r.each(l.datasets,function(l,u){var d=[];"object"==typeof l.data[0]&&null!==l.data[0]&&n.chart.isDatasetVisible(u)?r.each(l.data,function(r,l){var s=e(n,n.getRightValue(r));s.isValid()&&(i.round&&s.startOf(i.round),t=s.valueOf(),a=Math.min(t,a),o=Math.max(t,o),d[l]=t)}):d=s.labels.slice(),s.datasets[u]=d}),n.dataMin=a,n.dataMax=o,n._parsedData=s},buildTicks:function(){var i,o,l=this,s=l.options.time,u=l.dataMin,d=l.dataMax;if(s.min){var c=e(l,s.min);s.round&&c.round(s.round),i=c.valueOf()}s.max&&(o=e(l,s.max).valueOf());var h=l.getLabelCapacity(i||u),f=s.unit||n(s.minUnit,i||u,o||d,h);l.displayFormat=s.displayFormats[f];var g=s.stepSize||a(i||u,o||d,f,h);l.ticks=t.Ticks.generators.time({maxTicks:h,min:i,max:o,stepSize:g,unit:f,isoWeekday:s.isoWeekday},{min:u,max:d}),l.max=r.max(l.ticks),l.min=r.min(l.ticks)},getLabelForIndex:function(t,n){var i=this,a=i.chart.data.labels&&t<i.chart.data.labels.length?i.chart.data.labels[t]:"",o=i.chart.data.datasets[n].data[t];return null!==o&&"object"==typeof o&&(a=i.getRightValue(o)),i.options.time.tooltipFormat&&(a=e(i,a).format(i.options.time.tooltipFormat)),a},tickFormatFunction:function(t,e,n){var i=t.format(this.displayFormat),a=this.options.ticks,o=r.getValueOrDefault(a.callback,a.userCallback);return o?o(i,e,n):i},convertTicksToLabels:function(){var t=this;t.ticksAsTimestamps=t.ticks,t.ticks=t.ticks.map(function(t){return i(t)}).map(t.tickFormatFunction,t)},getPixelForOffset:function(t){var e=this,n=e.max-e.min,i=n?(t-e.min)/n:0;if(e.isHorizontal()){var a=e.width*i;return e.left+Math.round(a)}var o=e.height*i;return e.top+Math.round(o)},getPixelForValue:function(t,n,i){var a=this,o=null;if(void 0!==n&&void 0!==i&&(o=a._parsedData.datasets[i][n]),null===o&&(t&&t.isValid||(t=e(a,a.getRightValue(t))),t&&t.isValid&&t.isValid()&&(o=t.valueOf())),null!==o)return a.getPixelForOffset(o)},getPixelForTick:function(t){return this.getPixelForOffset(this.ticksAsTimestamps[t])},getValueForPixel:function(t){var e=this,n=e.isHorizontal()?e.width:e.height,a=(t-(e.isHorizontal()?e.left:e.top))/n;return i(e.min+a*(e.max-e.min))},getLabelWidth:function(e){var n=this,i=n.options.ticks,a=n.ctx.measureText(e).width,o=Math.cos(r.toRadians(i.maxRotation)),l=Math.sin(r.toRadians(i.maxRotation)),s=r.getValueOrDefault(i.fontSize,t.defaults.global.defaultFontSize);return a*o+s*l},getLabelCapacity:function(t){var e=this;e.displayFormat=e.options.time.displayFormats.millisecond;var n=e.tickFormatFunction(i(t),0,[]),a=e.getLabelWidth(n),o=e.isHorizontal()?e.width:e.height,r=o/a;return r}});t.scaleService.registerScaleType("time",u,s)}},{1:1}]},{},[7])(7)});
diff --git a/website/assets/js/in-view.min.js b/website/assets/js/in-view.min.js
new file mode 100644
index 000000000..f23f1d946
--- /dev/null
+++ b/website/assets/js/in-view.min.js
@@ -0,0 +1,6 @@
+/*!
+ * in-view 0.6.1 - Get notified when a DOM element enters or exits the viewport.
+ * Copyright (c) 2016 Cam Wiegert <cam@camwiegert.com> - https://camwiegert.github.io/in-view
+ * License: MIT
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.inView=e():t.inView=e()}(this,function(){return function(t){function e(r){if(n[r])return n[r].exports;var i=n[r]={exports:{},id:r,loaded:!1};return t[r].call(i.exports,i,i.exports,e),i.loaded=!0,i.exports}var n={};return e.m=t,e.c=n,e.p="",e(0)}([function(t,e,n){"use strict";function r(t){return t&&t.__esModule?t:{"default":t}}var i=n(2),o=r(i);t.exports=o["default"]},function(t,e){function n(t){var e=typeof t;return null!=t&&("object"==e||"function"==e)}t.exports=n},function(t,e,n){"use strict";function r(t){return t&&t.__esModule?t:{"default":t}}Object.defineProperty(e,"__esModule",{value:!0});var i=n(9),o=r(i),u=n(3),f=r(u),s=n(4),c=function(){if("undefined"!=typeof window){var t=100,e=["scroll","resize","load"],n={history:[]},r={offset:{},threshold:0,test:s.inViewport},i=(0,o["default"])(function(){n.history.forEach(function(t){n[t].check()})},t);e.forEach(function(t){return addEventListener(t,i)}),window.MutationObserver&&addEventListener("DOMContentLoaded",function(){new MutationObserver(i).observe(document.body,{attributes:!0,childList:!0,subtree:!0})});var u=function(t){if("string"==typeof t){var e=[].slice.call(document.querySelectorAll(t));return n.history.indexOf(t)>-1?n[t].elements=e:(n[t]=(0,f["default"])(e,r),n.history.push(t)),n[t]}};return u.offset=function(t){if(void 0===t)return r.offset;var e=function(t){return"number"==typeof t};return["top","right","bottom","left"].forEach(e(t)?function(e){return r.offset[e]=t}:function(n){return e(t[n])?r.offset[n]=t[n]:null}),r.offset},u.threshold=function(t){return"number"==typeof t&&t>=0&&t<=1?r.threshold=t:r.threshold},u.test=function(t){return"function"==typeof t?r.test=t:r.test},u.is=function(t){return r.test(t,r)},u.offset(0),u}};e["default"]=c()},function(t,e){"use strict";function n(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}Object.defineProperty(e,"__esModule",{value:!0});var r=function(){function t(t,e){for(var n=0;n<e.length;n++){var r=e[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(t,r.key,r)}}return function(e,n,r){return n&&t(e.prototype,n),r&&t(e,r),e}}(),i=function(){function t(e,r){n(this,t),this.options=r,this.elements=e,this.current=[],this.handlers={enter:[],exit:[]},this.singles={enter:[],exit:[]}}return r(t,[{key:"check",value:function(){var t=this;return this.elements.forEach(function(e){var n=t.options.test(e,t.options),r=t.current.indexOf(e),i=r>-1,o=n&&!i,u=!n&&i;o&&(t.current.push(e),t.emit("enter",e)),u&&(t.current.splice(r,1),t.emit("exit",e))}),this}},{key:"on",value:function(t,e){return this.handlers[t].push(e),this}},{key:"once",value:function(t,e){return this.singles[t].unshift(e),this}},{key:"emit",value:function(t,e){for(;this.singles[t].length;)this.singles[t].pop()(e);for(var n=this.handlers[t].length;--n>-1;)this.handlers[t][n](e);return this}}]),t}();e["default"]=function(t,e){return new i(t,e)}},function(t,e){"use strict";function n(t,e){var n=t.getBoundingClientRect(),r=n.top,i=n.right,o=n.bottom,u=n.left,f=n.width,s=n.height,c={t:o,r:window.innerWidth-u,b:window.innerHeight-r,l:i},a={x:e.threshold*f,y:e.threshold*s};return c.t>e.offset.top+a.y&&c.r>e.offset.right+a.x&&c.b>e.offset.bottom+a.y&&c.l>e.offset.left+a.x}Object.defineProperty(e,"__esModule",{value:!0}),e.inViewport=n},function(t,e){(function(e){var n="object"==typeof e&&e&&e.Object===Object&&e;t.exports=n}).call(e,function(){return this}())},function(t,e,n){var r=n(5),i="object"==typeof self&&self&&self.Object===Object&&self,o=r||i||Function("return this")();t.exports=o},function(t,e,n){function r(t,e,n){function r(e){var n=x,r=m;return x=m=void 0,E=e,w=t.apply(r,n)}function a(t){return E=t,j=setTimeout(h,e),M?r(t):w}function l(t){var n=t-O,r=t-E,i=e-n;return _?c(i,g-r):i}function d(t){var n=t-O,r=t-E;return void 0===O||n>=e||n<0||_&&r>=g}function h(){var t=o();return d(t)?p(t):void(j=setTimeout(h,l(t)))}function p(t){return j=void 0,T&&x?r(t):(x=m=void 0,w)}function v(){void 0!==j&&clearTimeout(j),E=0,x=O=m=j=void 0}function y(){return void 0===j?w:p(o())}function b(){var t=o(),n=d(t);if(x=arguments,m=this,O=t,n){if(void 0===j)return a(O);if(_)return j=setTimeout(h,e),r(O)}return void 0===j&&(j=setTimeout(h,e)),w}var x,m,g,w,j,O,E=0,M=!1,_=!1,T=!0;if("function"!=typeof t)throw new TypeError(f);return e=u(e)||0,i(n)&&(M=!!n.leading,_="maxWait"in n,g=_?s(u(n.maxWait)||0,e):g,T="trailing"in n?!!n.trailing:T),b.cancel=v,b.flush=y,b}var i=n(1),o=n(8),u=n(10),f="Expected a function",s=Math.max,c=Math.min;t.exports=r},function(t,e,n){var r=n(6),i=function(){return r.Date.now()};t.exports=i},function(t,e,n){function r(t,e,n){var r=!0,f=!0;if("function"!=typeof t)throw new TypeError(u);return o(n)&&(r="leading"in n?!!n.leading:r,f="trailing"in n?!!n.trailing:f),i(t,e,{leading:r,maxWait:e,trailing:f})}var i=n(7),o=n(1),u="Expected a function";t.exports=r},function(t,e){function n(t){return t}t.exports=n}])});
diff --git a/website/assets/js/main.js b/website/assets/js/main.js
index 616fbb1df..42199538f 100644
--- a/website/assets/js/main.js
+++ b/website/assets/js/main.js
@@ -1,23 +1,324 @@
 //- 💫 MAIN JAVASCRIPT
+//- Note: Will be compiled using Babel before deployment.
 
 'use strict'
 
-{
-    const nav = document.querySelector('.js-nav')
-    const fixedClass = 'is-fixed'
-    let vh, scrollY = 0, scrollUp = false
+const $ = document.querySelector.bind(document);
+const $$ = document.querySelectorAll.bind(document);
 
-    const updateVh = () => Math.max(document.documentElement.clientHeight, window.innerHeight || 0)
 
-    const updateNav = () => {
-        const vh = updateVh()
-        const newScrollY = (window.pageYOffset || document.scrollTop) - (document.clientTop || 0)
-        if (newScrollY != scrollY) scrollUp = newScrollY <= scrollY
-        scrollY = newScrollY
-
-        if(scrollUp && !(isNaN(scrollY) || scrollY <= vh)) nav.classList.add(fixedClass)
-        else if (!scrollUp || (isNaN(scrollY) || scrollY <= vh/2)) nav.classList.remove(fixedClass)
+class ProgressBar {
+    /**
+     * Animated reading progress bar.
+     * @param {String} selector – CSS selector of progress bar element.
+     */
+    constructor(selector) {
+        this.el = $(selector);
+        this.scrollY = 0;
+        this.sizes = this.updateSizes();
+        this.el.setAttribute('max', 100);
+        this.init();
     }
 
-    window.addEventListener('scroll', () => requestAnimationFrame(updateNav))
+    init() {
+        window.addEventListener('scroll', () => {
+            this.scrollY = (window.pageYOffset || document.scrollTop) - (document.clientTop || 0);
+            requestAnimationFrame(this.update.bind(this));
+        }, false);
+        window.addEventListener('resize', () => {
+            this.sizes = this.updateSizes();
+            requestAnimationFrame(this.update.bind(this));
+        })
+    }
+
+    update() {
+        const offset = 100 - ((this.sizes.height - this.scrollY - this.sizes.vh) / this.sizes.height * 100);
+        this.el.setAttribute('value', (this.scrollY == 0) ? 0 : offset || 0);
+    }
+
+    updateSizes() {
+        const body = document.body;
+        const html = document.documentElement;
+        return {
+            height: Math.max(body.scrollHeight, body.offsetHeight, html.clientHeight, html.scrollHeight, html.offsetHeight),
+            vh: Math.max(html.clientHeight, window.innerHeight || 0)
+        }
+    }
+}
+
+
+class SectionHighlighter {
+    /**
+     * Hightlight section in viewport in sidebar, using in-view library.
+     * @param {String} sectionAttr - Data attribute of sections.
+     * @param {String} navAttr - Data attribute of navigation items.
+     * @param {String} activeClass – Class name of active element.
+     */
+    constructor(sectionAttr, navAttr, activeClass = 'is-active') {
+        this.sections = [...$$(`[${navAttr}]`)];
+        this.navAttr = navAttr;
+        this.sectionAttr = sectionAttr;
+        this.activeClass = activeClass;
+        inView(`[${sectionAttr}]`).on('enter', this.highlightSection.bind(this));
+    }
+
+    highlightSection(section) {
+        const id = section.getAttribute(this.sectionAttr);
+        const el = $(`[${this.navAttr}="${id}"]`);
+        if (el) {
+            this.sections.forEach(el => el.classList.remove(this.activeClass));
+            el.classList.add(this.activeClass);
+        }
+    }
+}
+
+
+class Templater {
+    /**
+     * Mini templating engine based on data attributes. Selects elements based
+     * on a data-tpl and data-tpl-key attribute and can set textContent
+     * and innterHtml.
+     *
+     * @param {String} templateId - Template section, e.g. value of data-tpl.
+     */
+    constructor(templateId) {
+        this.templateId = templateId;
+    }
+
+    get(key) {
+        return $(`[data-tpl="${this.templateId}"][data-tpl-key="${key}"]`);
+    }
+
+    fill(key, value, html = false) {
+        const el = this.get(key);
+        if (html) el.innerHTML = value || '';
+        else el.textContent = value || '';
+        return el;
+    }
+}
+
+
+class ModelLoader {
+    /**
+     * Load model meta from GitHub and update model details on site. Uses the
+     * Templater mini template engine to update DOM.
+     *
+     * @param {String} repo - Path tp GitHub repository containing releases.
+     * @param {Array} models - List of model IDs, e.g. "en_core_web_sm".
+     * @param {Object} licenses - License IDs mapped to URLs.
+     * @param {Object} accKeys - Available accuracy keys mapped to display labels.
+     */
+    constructor(repo, models = [], licenses = {}, accKeys = {}) {
+        this.url = `https://raw.githubusercontent.com/${repo}/master`;
+        this.repo = `https://github.com/${repo}`;
+        this.modelIds = models;
+        this.licenses = licenses;
+        this.accKeys = accKeys;
+        this.chartColor = '#09a3d5';
+        this.chartOptions = {
+            type: 'bar',
+            options: { responsive: true, scales: {
+                yAxes: [{ label: 'Accuracy', ticks: { suggestedMin: 70 }}],
+                xAxes: [{ barPercentage: 0.425 }]
+            }}
+        }
+        Chart.defaults.global.legend.position = 'bottom';
+        Chart.defaults.global.defaultFontFamily = "-apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'";
+        this.init();
+    }
+
+    init() {
+        this.modelIds.forEach(modelId =>
+            new Templater(modelId).get('table').setAttribute('data-loading', ''));
+        fetch(`${this.url}/compatibility.json`)
+            .then(res => this.handleResponse(res))
+            .then(json => json.ok ? this.getModels(json['spacy']) : this.modelIds.forEach(modelId => this.showError(modelId)))
+    }
+
+    handleResponse(res) {
+        if (res.ok) return res.json().then(json => Object.assign({}, json, { ok: res.ok }))
+        else return ({ ok: res.ok })
+    }
+
+    getModels(compat) {
+        this.compat = compat;
+        for (let modelId of this.modelIds) {
+            const version = this.getLatestVersion(modelId, compat);
+            if (!version) {
+                this.showError(modelId); return;
+            }
+            fetch(`${this.url}/meta/${modelId}-${version}.json`)
+                .then(res => this.handleResponse(res))
+                .then(json => json.ok ? this.render(json) : this.showError(modelId))
+        }
+        // make sure scroll positions for progress bar etc. are recalculated
+        window.dispatchEvent(new Event('resize'));
+    }
+
+    showError(modelId) {
+        const template = new Templater(modelId);
+        template.get('table').removeAttribute('data-loading');
+        template.get('error').style.display = 'block';
+        for (let key of ['sources', 'pipeline', 'author', 'license']) {
+            template.get(key).parentElement.parentElement.style.display = 'none';
+        }
+    }
+
+    /**
+     * Update model details in tables. Currently quite hacky :(
+     */
+    render({ lang, name, version, sources, pipeline, url, author, license, accuracy, size, description, notes }) {
+        const modelId = `${lang}_${name}`;
+        const model = `${modelId}-${version}`;
+        const template = new Templater(modelId);
+
+        const getSources = s => (s instanceof Array) ? s.join(', ') : s;
+        const getPipeline = p => p.map(comp => `<code>${comp}</code>`).join(', ');
+        const getLink = (t, l) => `<a href="${l}" target="_blank">${t}</a>`;
+
+        const keys = { version, size, description, notes }
+        Object.keys(keys).forEach(key => template.fill(key, keys[key]));
+
+        if (sources) template.fill('sources', getSources(sources));
+        if (pipeline && pipeline.length) template.fill('pipeline', getPipeline(pipeline), true);
+        else template.get('pipeline').parentElement.parentElement.style.display = 'none';
+
+        if (author) template.fill('author', url ? getLink(author, url) : author, true);
+        if (license) template.fill('license', this.licenses[license] ? getLink(license, this.licenses[license]) : license, true);
+
+        template.get('download').setAttribute('href', `${this.repo}/releases/tag/${model}`);
+        if (accuracy) this.renderAccuracy(template, accuracy, modelId);
+        this.renderCompat(template, modelId);
+        template.get('table').removeAttribute('data-loading');
+    }
+
+    renderCompat(template, modelId) {
+        template.get('compat-wrapper').style.display = 'table-row';
+        const options = Object.keys(this.compat).map(v => `<option value="${v}">v${v}</option>`).join('');
+        template
+            .fill('compat', '<option selected disabled>spaCy version</option>' + options, true)
+            .addEventListener('change', ev => {
+                const result = this.compat[ev.target.value][modelId];
+                if (result) template.fill('compat-versions', `<code>${modelId}-${result[0]}</code>`, true);
+                else template.fill('compat-versions', '');
+            });
+    }
+
+    renderAccuracy(template, accuracy, modelId, compare=false) {
+        template.get('accuracy-wrapper').style.display = 'block';
+        const metaKeys = Object.keys(this.accKeys).map(k => accuracy[k] ? k : false).filter(k => k);
+        for (let key of metaKeys) {
+            template.fill(key, accuracy[key].toFixed(2)).parentElement.style.display = 'table-row';
+        }
+
+        this.chartOptions.options.legend = { display: compare }
+        new Chart(`chart_${modelId}`, Object.assign({}, this.chartOptions, { data: {
+            datasets: [{
+                label: modelId,
+                data: metaKeys.map(key => accuracy[key].toFixed(2)),
+                backgroundColor: this.chartColor
+            }],
+            labels: metaKeys.map(key => this.accKeys[key])
+        }}))
+    }
+
+    getLatestVersion(model, compat = {}) {
+        for (let spacy_v of Object.keys(compat)) {
+            const models = compat[spacy_v];
+            if (models[model]) return models[model][0];
+        }
+    }
+}
+
+
+class Changelog {
+    /**
+     * Fetch and render changelog from GitHub. Clones a template node (table row)
+     * to avoid doubling templating markup in JavaScript.
+     *
+     * @param {String} user - GitHub username.
+     * @param {String} repo - Repository to fetch releases from.
+     */
+    constructor(user, repo) {
+        this.url = `https://api.github.com/repos/${user}/${repo}/releases`;
+        this.template = new Templater('changelog');
+        fetch(this.url)
+            .then(res => this.handleResponse(res))
+            .then(json => json.ok ? this.render(json) : false)
+    }
+
+    /**
+     * Get template section from template row. Slightly hacky, but does make sense.
+     */
+    $(item, id) {
+        return item.querySelector(`[data-changelog="${id}"]`);
+    }
+
+    handleResponse(res) {
+        if (res.ok) return res.json().then(json => Object.assign({}, json, { ok: res.ok }))
+        else return ({ ok: res.ok })
+    }
+
+    render(json) {
+        this.template.get('error').style.display = 'none';
+        this.template.get('table').style.display = 'block';
+        this.row = this.template.get('item');
+        this.releases = this.template.get('releases');
+        this.prereleases = this.template.get('prereleases');
+        Object.values(json)
+            .filter(release => release.name)
+            .forEach(release => this.renderRelease(release));
+        this.row.remove();
+        // make sure scroll positions for progress bar etc. are recalculated
+        window.dispatchEvent(new Event('resize'));
+    }
+
+    /**
+     * Clone the template row and populate with content from API response.
+     * https://developer.github.com/v3/repos/releases/#list-releases-for-a-repository
+     *
+     * @param {String} name - Release title.
+     * @param {String} tag (tag_name) - Release tag.
+     * @param {String} url (html_url) - URL to the release page on GitHub.
+     * @param {String} date (published_at) - Timestamp of release publication.
+     * @param {Boolean} pre (prerelease) - Whether the release is a prerelease.
+     */
+    renderRelease({ name, tag_name: tag, html_url: url, published_at: date, prerelease: pre }) {
+        const container = pre ? this.prereleases : this.releases;
+        const row = this.row.cloneNode(true);
+        this.$(row, 'date').textContent = date.split('T')[0];
+        this.$(row, 'tag').innerHTML = `<a href="${url}" target="_blank"><code>${tag}</code></a>`;
+        this.$(row, 'title').textContent = (name.split(': ').length == 2) ? name.split(': ')[1] : name;
+        container.appendChild(row);
+    }
+}
+
+
+class GitHubEmbed {
+    /**
+     * Embed code from GitHub repositories, similar to Gist embeds. Fetches the
+     * raw text and places it inside element.
+     * Usage: <pre><code data-gh-embed="spacy/master/examples/x.py"></code><pre>
+     *
+     * @param {String} user - GitHub user or organization.
+     * @param {String} attr - Data attribute used to select containers. Attribute
+     *                        value should be path to file relative to user.
+     */
+    constructor(user, attr) {
+        this.url = `https://raw.githubusercontent.com/${user}`;
+        this.attr = attr;
+        this.error = `\nCan't fetch code example from GitHub :(\n\nPlease use the link below to view the example. If you've come across\na broken link, we always appreciate a pull request to the repository,\nor a report on the issue tracker. Thanks!`;
+        [...$$(`[${this.attr}]`)].forEach(el => this.embed(el));
+    }
+
+    embed(el) {
+        el.parentElement.setAttribute('data-loading', '');
+        fetch(`${this.url}/${el.getAttribute(this.attr)}`)
+            .then(res => res.text().then(text => ({ text, ok: res.ok })))
+            .then(({ text, ok }) => {
+                el.textContent = ok ? text : this.error;
+                if (ok && window.Prism) Prism.highlightElement(el);
+            })
+        el.parentElement.removeAttribute('data-loading');
+    }
 }
diff --git a/website/assets/js/prism.js b/website/assets/js/prism.min.js
similarity index 100%
rename from website/assets/js/prism.js
rename to website/assets/js/prism.min.js
diff --git a/website/assets/js/quickstart.js b/website/assets/js/quickstart.min.js
similarity index 100%
rename from website/assets/js/quickstart.js
rename to website/assets/js/quickstart.min.js

From 9af604f0da5676103b5cbbae2147abdb1e5ce089 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 14:20:13 +0200
Subject: [PATCH 164/213] Update layout templates, partials and mixins

---
 website/404.jade                    |   3 +-
 website/_includes/_footer.jade      |  12 +-
 website/_includes/_functions.jade   |  70 ++++++--
 website/_includes/_mixins-base.jade | 124 +++++++++-----
 website/_includes/_mixins.jade      | 249 ++++++++++++++++------------
 website/_includes/_navigation.jade  |  20 +--
 website/_includes/_newsletter.jade  |   9 +-
 website/_includes/_page-docs.jade   |  61 ++++---
 website/_includes/_page_models.jade |  77 +++++++++
 website/_includes/_sidebar.jade     |  24 ++-
 website/_layout.jade                |  19 ++-
 11 files changed, 436 insertions(+), 232 deletions(-)
 create mode 100644 website/_includes/_page_models.jade

diff --git a/website/404.jade b/website/404.jade
index 33b936a08..af4e7d0f2 100644
--- a/website/404.jade
+++ b/website/404.jade
@@ -8,4 +8,5 @@ include _includes/_mixins
         | does not exist!
 
     h2.c-landing__title.u-heading-3.u-padding-small
-        a(href="javascript:history.go(-1)") Click here to go back.
+        +button(false, true, "secondary-light")(href="javascript:history.go(-1)")
+            |  Click here to go back
diff --git a/website/_includes/_footer.jade b/website/_includes/_footer.jade
index e933f37a8..4d0d34cb5 100644
--- a/website/_includes/_footer.jade
+++ b/website/_includes/_footer.jade
@@ -1,8 +1,6 @@
 //- 💫 INCLUDES > FOOTER
 
-include _mixins
-
-footer.o-footer.u-text.u-border-dotted
+footer.o-footer.u-text
     +grid.o-content
         each group, label in FOOTER
             +grid-col("quarter")
@@ -13,18 +11,18 @@ footer.o-footer.u-text.u-border-dotted
                         li
                             +a(url)=item
 
-        if SECTION != "docs"
+        if SECTION == "index"
             +grid-col("quarter")
                 include _newsletter
 
-    if SECTION == "docs"
+    if SECTION != "index"
         .o-content.o-block.u-border-dotted
             include _newsletter
 
     .o-inline-list.u-text-center.u-text-tiny.u-color-subtle
         span &copy; 2016-#{new Date().getFullYear()} #[+a(COMPANY_URL, true)=COMPANY]
 
-        +a(COMPANY_URL, true)
-            +svg("graphics", "explosion", 45).o-icon.u-color-theme.u-grayscale
+        +a(COMPANY_URL, true)(aria-label="Explosion AI")
+            +icon("explosion", 45).o-icon.u-color-theme.u-grayscale
 
         +a(COMPANY_URL + "/legal", true) Legal / Imprint
diff --git a/website/_includes/_functions.jade b/website/_includes/_functions.jade
index e88e678cb..5209dbbec 100644
--- a/website/_includes/_functions.jade
+++ b/website/_includes/_functions.jade
@@ -1,35 +1,71 @@
 //- 💫 INCLUDES > FUNCTIONS
 
-//- More descriptive variables for current.path and current.source
+//- Descriptive variables, available in the global scope
 
 - CURRENT = current.source
 - SECTION = current.path[0]
-- SUBSECTION = current.path[1]
+- LANGUAGES = public.models._data.LANGUAGES
+- MODELS = public.models._data.MODELS
+- CURRENT_MODELS = MODELS[current.source] || []
+
+- MODEL_COUNT = Object.keys(MODELS).map(m => Object.keys(MODELS[m]).length).reduce((a, b) => a + b)
+- MODEL_LANG_COUNT = Object.keys(MODELS).length
+- LANG_COUNT = Object.keys(LANGUAGES).length
+
+- MODEL_META = public.models._data.MODEL_META
+- MODEL_LICENSES = public.models._data.MODEL_LICENSES
+- MODEL_ACCURACY = public.models._data.MODEL_ACCURACY
+- EXAMPLE_SENTENCES = public.models._data.EXAMPLE_SENTENCES
+
+- IS_PAGE = (SECTION != "index") && !landing
+- IS_MODELS = (SECTION == "models" && LANGUAGES[current.source])
+- HAS_MODELS = IS_MODELS && CURRENT_MODELS.length
 
 
 //- Add prefixes to items of an array (for modifier CSS classes)
+    array   - [array] list of class names or options, e.g. ["foot"]
+    prefix  - [string] prefix to add to each class, e.g. "c-table__row"
+    RETURNS - [array] list of modified class names
 
 -   function prefixArgs(array, prefix) {
--       return array.map(function(arg) {
--           return prefix + '--' + arg;
--       }).join(' ');
+-       return array.map(arg => prefix + '--' + arg).join(' ');
+-   }
+
+
+//- Convert API paths (semi-temporary fix for renamed sections)
+    path    - [string] link path supplied to +api mixin
+    RETURNS - [string] new link path to correct location
+
+-   function convertAPIPath(path) {
+-       if (path.startsWith('spacy#') || path.startsWith('displacy#') || path.startsWith('util#')) {
+-           var comps = path.split('#');
+-           return "top-level#" + comps[0] + '.' + comps[1];
+-       }
+-       else if (path.startsWith('cli#')) {
+-           return "top-level#" + path.split('#')[1];
+-       }
+-       return path;
+-   }
+
+
+//- Get model components from ID. Components can then be looked up in LANGUAGES
+    and MODEL_META respectively, to get their human-readable form.
+    id      - [string] model ID, e.g. "en_core_web_sm"
+    RETURNS - [object] object keyed by components lang, type, genre and size
+
+-   function getModelComponents(id) {
+-       var comps = id.split('_');
+-       return {'lang': comps[0], 'type': comps[1], 'genre': comps[2], 'size': comps[3]}
 -   }
 
 
 //- Generate GitHub links
+    repo     - [string] name of repo owned by explosion
+    filepath - [string] logical path to file relative to repository root
+    branch   - [string] optional branch, defaults to "master"
+    RETURNS  - [string] the correct link to the file on GitHub
 
 -   function gh(repo, filepath, branch) {
 -       var branch = ALPHA ? 'develop' : branch
--       return 'https://github.com/' + SOCIAL.github + '/' + repo + (filepath ? '/blob/' + (branch || 'master') + '/' + filepath : '' );
--   }
-
-
-//- Get social images
-
--   function getSocialImg() {
--       var base = SITE_URL + '/assets/img/social/preview_'
--       var image = ALPHA ? 'alpha' : 'default'
--       if (preview) image = preview
--       else if (SECTION == 'docs' && !ALPHA) image = 'docs'
--       return base + image + '.jpg'
+-       return 'https://github.com/' + SOCIAL.github + '/' + (repo || '') + (filepath ? '/blob/' + (branch || 'master') + '/' + filepath : '' );
 -   }
diff --git a/website/_includes/_mixins-base.jade b/website/_includes/_mixins-base.jade
index 7534a6f4e..752423d79 100644
--- a/website/_includes/_mixins-base.jade
+++ b/website/_includes/_mixins-base.jade
@@ -1,5 +1,13 @@
 //- 💫 MIXINS > BASE
 
+//- Section
+    id - [string] anchor assigned to section (used for breadcrumb navigation)
+
+mixin section(id)
+    section.o-section(id="section-" + id data-section=id)
+        block
+
+
 //- Aside wrapper
     label - [string] aside label
 
@@ -11,34 +19,26 @@ mixin aside-wrapper(label)
 
             block
 
-//- Date
-    input - [string] date in the format YYYY-MM-DD
 
-mixin date(input)
-    - var date = new Date(input)
-    - var months = [ 'January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December' ]
-
-    time(datetime=JSON.parse(JSON.stringify(date)))&attributes(attributes)=months[date.getMonth()] + ' ' + date.getDate() + ', ' + date.getFullYear()
-
-
-//- SVG from map
-    file   - [string] SVG file name in /assets/img/
+//- SVG from map (uses embedded SVG sprite)
     name   - [string] SVG symbol id
     width  - [integer] width in px
     height - [integer] height in px (default: same as width)
 
-mixin svg(file, name, width, height)
+mixin svg(name, width, height)
     svg(aria-hidden="true" viewBox="0 0 #{width} #{height || width}" width=width height=(height || width))&attributes(attributes)
-        use(xlink:href="/assets/img/#{file}.svg##{name}")
+        use(xlink:href="#svg_#{name}")
 
 
 //- Icon
-    name - [string] icon name, should be SVG symbol ID
-    size - [integer] icon width and height (default: 20)
+    name   - [string] icon name (will be used as symbol id: #svg_{name})
+    width  - [integer] icon width (default: 20)
+    height - [integer] icon height (defaults to width)
 
-mixin icon(name, size)
-    - var size = size || 20
-    +svg("icons", name, size).o-icon(style="min-width: #{size}px")&attributes(attributes)
+mixin icon(name, width, height)
+    - var width = width || 20
+    - var height = height || width
+    +svg(name, width, height).o-icon(style="min-width: #{width}px")&attributes(attributes)
 
 
 //- Pro/Con/Neutral icon
@@ -46,8 +46,8 @@ mixin icon(name, size)
     size - [integer] icon size (optional)
 
 mixin procon(icon, size)
-    - colors = { pro: "green", con: "red", neutral: "yellow" }
-    +icon(icon, size)(class="u-color-#{colors[icon] || 'subtle'}" aria-label=icon)&attributes(attributes)
+    - colors = { pro: "green", con: "red", neutral: "subtle" }
+    +icon("circle", size || 16)(class="u-color-#{colors[icon] || 'subtle'}" aria-label=icon)&attributes(attributes)
 
 
 //- Headlines Helper Mixin
@@ -80,8 +80,7 @@ mixin headline(level)
 
 mixin permalink(id)
     if id
-        a.u-permalink(id=id href="##{id}")
-            +icon("anchor").u-permalink__icon
+        a.u-permalink(href="##{id}")
             block
 
     else
@@ -109,7 +108,7 @@ mixin quickstart(groups, headline, description, hide_results)
                     .c-quickstart__fields
                         for option in group.options
                             input.c-quickstart__input(class="c-quickstart__input--" + (group.input_style ? group.input_style : group.multiple ? "check" : "radio") type=group.multiple ? "checkbox" : "radio" name=group.id id="qs-#{option.id}" value=option.id checked=option.checked)
-                            label.c-quickstart__label(for="qs-#{option.id}")!=option.title
+                            label.c-quickstart__label.u-text-tiny(for="qs-#{option.id}")!=option.title
                                 if option.meta
                                     |  #[span.c-quickstart__label__meta (#{option.meta})]
                                 if option.help
@@ -122,12 +121,10 @@ mixin quickstart(groups, headline, description, hide_results)
                 code.c-code-block__content.c-quickstart__code(data-qs-results="")
                     block
 
-    .c-quickstart__info.u-text-tiny.o-block.u-text-right
-        |  Like this widget? Check out #[+a("https://github.com/ines/quickstart").u-link quickstart.js]!
-
 
 //- Quickstart code item
-    data [object] - Rendering conditions (keyed by option group ID, value: option)
+    data  - [object] Rendering conditions (keyed by option group ID, value: option)
+    style - [string] modifier ID for line style
 
 mixin qs(data, style)
     - args = {}
@@ -148,6 +145,13 @@ mixin terminal(label)
         +code.x-terminal__code
             block
 
+//- Chart.js
+    id - [string] chart ID, will be assigned as #chart_{id}
+
+mixin chart(id)
+    figure.o-block&attributes(attributes)
+        canvas(id="chart_#{id}" width="800" height="400" style="max-width: 100%")
+
 
 //- Gitter chat button and widget
     button - [string] text shown on button
@@ -156,26 +160,24 @@ mixin terminal(label)
 mixin gitter(button, label)
     aside.js-gitter.c-chat.is-collapsed(data-title=(label || button))
 
-    button.js-gitter-button.c-chat__button.u-text-small
-        +icon("chat").o-icon--inline
+    button.js-gitter-button.c-chat__button.u-text-tag
+        +icon("chat", 16).o-icon--inline
         !=button
 
 
 //- Badge
-    name - [string] "pipy" or "conda"
+    image - [string] path to badge image
+    url   - [string] badge link
 
-mixin badge(name)
-    - site = BADGES[name]
-
-    if site
-        +a(site.link).u-padding-small
-            img(src=site.badge alt="{name} version" height="20")
+mixin badge(image, url)
+    +a(url).u-padding-small.u-hide-link&attributes(attributes)
+        img.o-badge(src=image alt=url height="20")
 
 
-//- Logo
+//- spaCy logo
 
 mixin logo()
-    +svg("graphics", "spacy", 675, 215).o-logo&attributes(attributes)
+    +svg("spacy", 675, 215).o-logo&attributes(attributes)
 
 
 //- Landing
@@ -186,18 +188,56 @@ mixin landing-header()
             .c-landing__content
                 block
 
+mixin landing-banner(headline, label)
+    .c-landing__banner.u-padding.o-block.u-color-light
+        +grid.c-landing__banner__content.o-no-block
+            +grid-col("third")
+                h3.u-heading.u-heading-1
+                    if label
+                        div
+                            span.u-text-label.u-text-label--light=label
+                    !=headline
 
-mixin landing-badge(url, graphic, alt, size)
-    +a(url)(aria-label=alt title=alt).c-landing__badge
-        +svg("graphics", graphic, size || 225)
+            +grid-col("two-thirds").c-landing__banner__text
+                block
+
+
+mixin landing-logos(title, logos)
+    .o-content.u-text-center&attributes(attributes)
+        h3.u-heading.u-text-label.u-color-dark=title
+
+        each row, i in logos
+            - var is_last = i == logos.length - 1
+            +grid("center").o-inline-list.o-no-block(class=is_last ? "o-no-block" : null)
+                each details, name in row
+                    +a(details[0]).u-padding-medium
+                        +icon(name, details[1], details[2])
+
+                if is_last
+                    block
 
 
 //- Under construction (temporary)
     Marks sections that still need to be completed for the v2.0 release.
 
 mixin under-construction()
-    +infobox("🚧 Under construction")
+    +infobox("Under construction", "🚧")
         |  This section is still being written and will be updated for the v2.0
         |  release. Is there anything that you think should definitely mentioned or
         |  explained here? Any examples you'd like to see? #[strong Let us know]
         |  on the #[+a(gh("spacy") + "/issues/1105") v2.0 alpha thread] on GitHub!
+
+
+//- Alpha infobox (temporary)
+    Added in the templates to notify user that they're visiting the alpha site.
+
+mixin alpha-info()
+    +infobox("You are viewing the spaCy v2.0.0 alpha docs", "⚠️")
+        strong This page is part of the alpha documentation for spaCy v2.0.
+        |  It does not reflect the state of the latest stable release.
+        |  Because v2.0 is still under development, the implementation
+        |  may differ from the intended state described here. See the
+        |  #[+a(gh("spaCy") + "/releases/tag/v2.0.0-alpha") release notes]
+        |  for details on how to install and test the new version. To
+        |  read the official docs for spaCy v1.x,
+        |  #[+a("https://spacy.io/docs") go here].
diff --git a/website/_includes/_mixins.jade b/website/_includes/_mixins.jade
index b140151b2..4876c6b6b 100644
--- a/website/_includes/_mixins.jade
+++ b/website/_includes/_mixins.jade
@@ -8,11 +8,15 @@ include _mixins-base
     level - [integer] headline level, corresponds to h1, h2, h3 etc.
     id    - [string] unique identifier, creates permalink (optional)
 
-mixin h(level, id)
-    +headline(level).u-heading&attributes(attributes)
+mixin h(level, id, source)
+    +headline(level).u-heading(id=id)&attributes(attributes)
         +permalink(id)
             block
 
+        if source
+            +button(gh("spacy", source), false, "secondary", "small").u-nowrap.u-float-right
+                span Source #[+icon("code", 14).o-icon--inline]
+
 
 //- External links
     url     - [string] link href
@@ -38,21 +42,23 @@ mixin src(url)
 
 
 //- API link (with added tag and automatically generated path)
-    path - [string] path to API docs page relative to /docs/api/
+    path - [string] path to API docs page relative to /api/
 
 mixin api(path)
-    +a("/docs/api/" + path, true)(target="_self").u-no-border.u-inline-block.u-nowrap
+    - path = convertAPIPath(path)
+    +a("/api/" + path, true)(target="_self").u-no-border.u-inline-block.u-nowrap
         block
 
-        |  #[+icon("book", 18).o-icon--inline.u-color-theme]
+        |  #[+icon("book", 16).o-icon--inline.u-color-theme]
 
 
 //- Help icon with tooltip
-    tooltip - [string] Tooltip text
+    tooltip   - [string] Tooltip text
+    icon_size - [integer] Optional size of help icon in px.
 
-mixin help(tooltip)
+mixin help(tooltip, icon_size)
     span(data-tooltip=tooltip)&attributes(attributes)
-        +icon("help", 16).i-icon--inline
+        +icon("help", icon_size || 16).o-icon--inline
 
 
 //- Aside for text
@@ -68,24 +74,43 @@ mixin aside(label)
     label    - [string] aside title (optional or false for no label)
     language - [string] language for syntax highlighting (default: "python")
                supports basic relevant languages available for PrismJS
+    prompt   - [string] prompt displayed before first line, e.g. "$"
 
-mixin aside-code(label, language)
+mixin aside-code(label, language, prompt)
     +aside-wrapper(label)
-        +code(false, language).o-no-block
+        +code(false, language, prompt).o-no-block
             block
 
 
 //- Infobox
     label - [string] infobox title (optional or false for no title)
+    emoji - [string] optional emoji displayed before the title, necessary as
+            argument to be able to wrap it for spacing
 
-mixin infobox(label)
+mixin infobox(label, emoji)
     aside.o-box.o-block.u-text-small
         if label
-            h3.u-text-label.u-color-theme=label
+            h3.u-heading.u-text-label.u-color-theme
+                if emoji
+                    span.o-emoji=emoji
+                |  #{label}
 
         block
 
 
+//- Logos displayed in the top corner of some infoboxes
+    logos - [array] List of icon ID, width, height and link.
+
+mixin infobox-logos(...logos)
+    .o-box__logos.u-text-right.u-float-right
+        for logo in logos
+            if logo[3]
+                |  #[+a(logo[3]).u-inline-block.u-hide-link.u-padding-small #[+icon(logo[0], logo[1], logo[2]).u-color-dark]]
+            else
+                |  #[+icon(logo[0], logo[1], logo[2]).u-color-dark]
+
+
+
 //- Link button
     url      - [string] link href
     trusted  - [boolean] if not set / false, rel="noopener nofollow" is added
@@ -94,7 +119,7 @@ mixin infobox(label)
                see assets/css/_components/_buttons.sass
 
 mixin button(url, trusted, ...style)
-    - external = url.includes("http")
+    - external = url && url.includes("http")
     a.c-button.u-text-label(href=url class=prefixArgs(style, "c-button") role="button" target=external ? "_blank" : null rel=external && !trusted ? "noopener nofollow" : null)&attributes(attributes)
         block
 
@@ -103,31 +128,33 @@ mixin button(url, trusted, ...style)
     label    - [string] aside title (optional or false for no label)
     language - [string] language for syntax highlighting (default: "python")
                supports basic relevant languages available for PrismJS
-    prompt    - [string] prompt or icon to display next to code block, (mostly used for old/new)
+    prompt   - [string] prompt displayed before first line, e.g. "$"
     height   - [integer] optional height to clip code block to
+    icon     - [string] icon displayed next to code block (e.g. "accept" for new code)
+    wrap     - [boolean] wrap text and disable horizontal scrolling
 
-mixin code(label, language, prompt, height)
+mixin code(label, language, prompt, height, icon, wrap)
     pre.c-code-block.o-block(class="lang-#{(language || DEFAULT_SYNTAX)}" class=icon ? "c-code-block--has-icon" : null style=height ? "height: #{height}px" : null)&attributes(attributes)
         if label
             h4.u-text-label.u-text-label--dark=label
-        - var icon = (prompt == 'accept' || prompt == 'reject')
+        - var icon = icon || (prompt == 'accept' || prompt == 'reject')
         if icon
             - var classes = {'accept': 'u-color-green', 'reject': 'u-color-red'}
             .c-code-block__icon(class=classes[icon] || null class=classes[icon] ? "c-code-block__icon--border" : null)
                 +icon(icon, 18)
 
-        code.c-code-block__content(data-prompt=icon ? null : prompt)
+        code.c-code-block__content(class=wrap ? "u-wrap" : null data-prompt=icon ? null : prompt)
             block
 
 
 //- Code blocks to display old/new versions
 
 mixin code-old()
-    +code(false, false, "reject").o-block-small
+    +code(false, false, false, false, "reject").o-block-small
         block
 
 mixin code-new()
-    +code(false, false, "accept").o-block-small
+    +code(false, false, false, false, "accept").o-block-small
         block
 
 
@@ -138,12 +165,33 @@ mixin code-new()
 
 mixin codepen(slug, height, default_tab)
     figure.o-block(style="min-height: #{height}px")&attributes(attributes)
-        .codepen(data-height=height data-theme-id="26467" data-slug-hash=slug data-default-tab=(default_tab || "result") data-embed-version="2" data-user=SOCIAL.codepen)
+        .codepen(data-height=height data-theme-id="31335" data-slug-hash=slug data-default-tab=(default_tab || "result") data-embed-version="2" data-user=SOCIAL.codepen)
             +a("https://codepen.io/" + SOCIAL.codepen + "/" + slug) View on CodePen
 
         script(async src="https://assets.codepen.io/assets/embed/ei.js")
 
 
+//- GitHub embed
+    repo     - [string] repository owned by explosion organization
+    file     - [string] logical path to file, relative to repository root
+    alt_file - [string] alternative file path used in footer and link button
+    height   - [integer] height of code preview in px
+
+mixin github(repo, file, alt_file, height)
+    - var branch = ALPHA ? "develop" : "master"
+    - var height = height || 250
+
+    figure.o-block
+        pre.c-code-block.o-block-small(class="lang-#{(language || DEFAULT_SYNTAX)}" style="height: #{height}px; min-height: #{height}px")
+            code.c-code-block__content(data-gh-embed="#{repo}/#{branch}/#{file}")
+
+        footer.o-grid.u-text
+            .o-block-small.u-flex-full #[+icon("github")] #[code=repo + '/' + (alt_file || file)]
+            div
+                +button(gh(repo, alt_file || file), false, "primary", "small") View on GitHub
+
+
+
 //- Images / figures
     url     - [string] url or path to image
     width   - [integer] image width in px, for better rendering (default: 500)
@@ -168,10 +216,26 @@ mixin image-caption()
         block
 
 
-//- Label
+//- Graphic or illustration with button
+    original - [string] Path to original image
+
+mixin graphic(original)
+    +image
+        block
+        if original
+            .u-text-right
+                +button(original, false, "secondary", "small") View large graphic
+
+
+//- Labels
 
 mixin label()
-    .u-text-label.u-color-subtle&attributes(attributes)
+    .u-text-label.u-color-dark&attributes(attributes)
+        block
+
+
+mixin label-inline()
+    strong.u-text-label.u-color-dark&attributes(attributes)
         block
 
 
@@ -188,8 +252,10 @@ mixin tag()
 mixin tag-model(...capabs)
     - var intro = "To use this functionality, spaCy needs a model to be installed"
     - var ext = capabs.length ? " that supports the following capabilities: " + capabs.join(', ') : ""
-    +tag Requires model
-    +help(intro + ext + ".").u-color-theme
+
+    span.u-nowrap
+        +tag Needs model
+        +help(intro + ext + ".").u-color-theme
 
 
 //- "New" tag to label features new in a specific version
@@ -219,15 +285,9 @@ mixin list(type, start)
 
 //- List item (only used within +list)
 
-mixin item(procon)
-    if procon
-        li&attributes(attributes)
-            +procon(procon).c-list__icon
-            block
-
-    else
-        li.c-list__item&attributes(attributes)
-            block
+mixin item()
+    li.c-list__item&attributes(attributes)
+        block
 
 
 //- Table
@@ -237,9 +297,9 @@ mixin table(head)
     table.c-table.o-block&attributes(attributes)
 
         if head
-            +row
+            +row("head")
                 each column in head
-                    th.c-table__head-cell.u-text-label=column
+                    +head-cell=column
 
         block
 
@@ -251,10 +311,11 @@ mixin row(...style)
         block
 
 
-//- Footer table row (only ued within +table)
 
-mixin footrow()
-    tr.c-table__row.c-table__row--foot&attributes(attributes)
+//- Header table cell (only used within +row)
+
+mixin head-cell()
+    th.c-table__head-cell.u-text-label&attributes(attributes)
         block
 
 
@@ -284,71 +345,58 @@ mixin grid-col(width)
 
 
 //- Card (only used within +grid)
-    title     - [string] card title
-    details   - [object] url, image, author, description, tags etc.
-                (see /docs/usage/_data.json)
+    title  - [string] card title
+    url    - [string] link for card
+    author - [string] optional author, displayed as byline at the bottom
+    icon   - [string] optional ID of icon displayed with card
+    width  - [string] optional width of grid column, defaults to "half"
 
-mixin card(title, details)
-    +grid-col("half").o-card.u-text&attributes(attributes)
-        if details.image
-            +a(details.url).o-block-small
-                img(src=details.image alt=title width="300" role="presentation")
-
-        if title
-            +a(details.url)
-                +h(3)=title
-
-                    if details.author
-                        .u-text-small.u-color-subtle by #{details.author}
-
-        if details.description || details.tags
-            ul
-                if details.description
-                    li=details.description
-
-                if details.tags
-                    li
-                        each tag in details.tags
-                            span.u-text-tag #{tag}
-                            | &nbsp;
-
-        block
+mixin card(title, url, author, icon, width)
+    +grid-col(width || "half").o-box.o-grid.o-grid--space.u-text&attributes(attributes)
+        +a(url)
+            h4.u-heading.u-text-label
+                if icon
+                    +icon(icon, 25).u-float-right
+                if title
+                    span.u-color-dark=title
+            .o-block-small.u-text-small
+                block
+        if author
+            .u-color-subtle.u-text-tiny by #{author}
 
 
-//- Simpler card list item (only used within +list)
-    title     - [string] card title
-    details   - [object] url, image, author, description, tags etc.
-                (see /docs/usage/_data.json)
+//- Table of contents, to be used with +item mixins for links
+    col - [string] width of column (see +grid-col)
 
-mixin card-item(title, details)
-    +item&attributes(attributes)
-        +a(details.url)=title
-
-        if details.description
-            br
-            span=details.description
-
-        if details.author
-            br
-            span.u-text-small.u-color-subtle by #{details.author}
+mixin table-of-contents(col)
+    +grid-col(col || "half")
+        +infobox
+            +label.o-block-small Table of contents
+            +list("numbers").u-text-small.o-no-block
+                block
 
 
-//- Table row for models table
+//- Bibliography
+    id - [string] ID of bibliography component, for anchor links. Can be used if
+         there's more than one bibliography on one page.
 
-mixin model-row(name, lang, procon, size, license, default_model, divider)
-    - var licenses = { "CC BY-SA": "https://creativecommons.org/licenses/by-sa/3.0/", "CC BY-NC": "https://creativecommons.org/licenses/by-nc/3.0/" }
+mixin bibliography(id)
+    section(id=id || "bibliography")
+        +infobox
+            +label.o-block-small Bibliography
+            +list("numbers").u-text-small.o-no-block
+                block
 
-    +row(divider ? "divider": null)
-        +cell #[code=name]
-            if default_model
-                |  #[span.u-color-theme(title="default model") #[+icon("star", 16)]]
-        +cell=lang
-        each icon in procon
-            +cell.u-text-center #[+procon(icon ? "pro" : "con")]
-        +cell.u-text-right=size
-        +cell
-            if license in licenses
-                +a(licenses[license])=license
+
+//- Footnote
+    id      - [string / integer] ID of footnote.
+    bib_id  - [string] ID of bibliography component, defaults to "bibliography".
+    tooltip - [string] optional text displayed as tooltip
+
+mixin fn(id, bib_id, tooltip)
+    sup.u-padding-small(id="bib" + id data-tooltip=tooltip)
+        span.u-text-tag
+            +a("#" + (bib_id || "bibliography")).u-hide-link #{id}
 
 
 //- Table rows for annotation specs
@@ -383,14 +431,3 @@ mixin annotation-row(annots, style)
             else
                 +cell=cell
         block
-
-
-//- Table of contents, to be used with +item mixins for links
-    col - [string] width of column (see +grid-col)
-
-mixin table-of-contents(col)
-    +grid-col(col || "half")
-        +infobox
-            +label.o-block-small Table of contents
-            +list("numbers").u-text-small.o-no-block
-                block
diff --git a/website/_includes/_navigation.jade b/website/_includes/_navigation.jade
index f113ca3f4..c7f2c956f 100644
--- a/website/_includes/_navigation.jade
+++ b/website/_includes/_navigation.jade
@@ -1,19 +1,15 @@
 //- 💫 INCLUDES > TOP NAVIGATION
 
-include _mixins
-
 nav.c-nav.u-text.js-nav(class=landing ? "c-nav--theme" : null)
-    a(href='/') #[+logo]
-
-    if SUBSECTION != "index"
-        .u-text-label.u-padding-small.u-hidden-xs=SUBSECTION
+    a(href="/" aria-label=SITENAME) #[+logo]
 
     ul.c-nav__menu
-        - var NAV = ALPHA ? { "Usage": "/docs/usage", "Reference": "/docs/api" } : NAVIGATION
-
-        each url, item in NAV
-            li.c-nav__menu__item(class=(url == "/") ? "u-hidden-xs" : null)
+        - var current_url = '/' + current.path[0]
+        each url, item in NAVIGATION
+            li.c-nav__menu__item(class=(current_url == url) ? "is-active" : null)
                 +a(url)=item
 
-        li.c-nav__menu__item
-            +a(gh("spaCy"))(aria-label="GitHub").u-hidden-xs #[+icon("github", 20)]
+        li.c-nav__menu__item.u-hidden-xs
+            +a(gh("spaCy"))(aria-label="GitHub") #[+icon("github", 20)]
+
+    progress.c-progress.js-progress(value="0" max="1")
diff --git a/website/_includes/_newsletter.jade b/website/_includes/_newsletter.jade
index 9bfe88d39..ca8333f86 100644
--- a/website/_includes/_newsletter.jade
+++ b/website/_includes/_newsletter.jade
@@ -1,6 +1,6 @@
 //- 💫 INCLUDES > NEWSLETTER
 
-ul.o-block
+ul.o-block-small
     li.u-text-label.u-color-subtle Stay in the loop!
     li Receive updates about new releases, tutorials and more.
 
@@ -10,7 +10,6 @@ form.o-grid#mc-embedded-subscribe-form(action="//#{MAILCHIMP.user}.list-manage.c
     div(style="position: absolute; left: -5000px;" aria-hidden="true")
         input(type="text" name="b_#{MAILCHIMP.id}_#{MAILCHIMP.list}" tabindex="-1" value="")
 
-    .o-grid-col.u-border.u-padding-small
-        input#mce-EMAIL.u-text(type="email" name="EMAIL" placeholder="Your email")
-
-        button#mc-embedded-subscribe.u-text-label.u-color-theme(type="submit" name="subscribe") Sign up
+    .o-grid-col.o-grid.o-grid--nowrap.o-field.u-padding-small
+        input#mce-EMAIL.o-field__input.u-text(type="email" name="EMAIL" placeholder="Your email" aria-label="Your email")
+        button#mc-embedded-subscribe.o-field__button.u-text-label.u-color-theme.u-nowrap(type="submit" name="subscribe") Sign up
diff --git a/website/_includes/_page-docs.jade b/website/_includes/_page-docs.jade
index 7afbc6bdc..703102487 100644
--- a/website/_includes/_page-docs.jade
+++ b/website/_includes/_page-docs.jade
@@ -1,47 +1,56 @@
 //- 💫 INCLUDES > DOCS PAGE TEMPLATE
 
-- sidebar_content = (SUBSECTION != "index") ? public.docs[SUBSECTION]._data.sidebar : public.docs._data.sidebar || FOOTER
+- sidebar_content = (public[SECTION] ? public[SECTION]._data.sidebar : public._data[SECTION] ? public._data[SECTION].sidebar : false) || FOOTER
 
 include _sidebar
 
 main.o-main.o-main--sidebar.o-main--aside
     article.o-content
         +grid.o-no-block
-            +grid-col(source ? "two-thirds" : "full")
-                +h(1)=title
-                    if tag
-                        +tag=tag
+            +h(1).u-heading--title=title.replace("'", "’")
+                if tag
+                    +tag=tag
+                if tag_new
+                    +tag-new(tag_new)
+
+                if teaser
+                    .u-heading__teaser.u-text-small.u-color-dark=teaser
+                else if IS_MODELS
+                    .u-heading__teaser.u-text-small.u-color-dark
+                        |  Available statistical models for
+                        |  #[code=current.source] (#{LANGUAGES[current.source]}).
 
             if source
-                +grid-col("third").u-text-right
-                    .o-inline-list
-                        +button(gh("spacy", source), false, "secondary").u-text-tag Source #[+icon("code", 14)]
+                .o-block.u-text-right
+                    +button(gh("spacy", source), false, "secondary", "small").u-nowrap
+                        |  Source #[+icon("code", 14)]
 
+        //-if ALPHA
+        //-    +alpha-info
 
-        if ALPHA
-            +infobox("⚠️ You are viewing the spaCy v2.0.0 alpha docs")
-                strong This page is part of the alpha documentation for spaCy v2.0.
-                |  It does not reflect the state of the latest stable release.
-                |  Because v2.0 is still under development, the implementation
-                |  may differ from the intended state described here. See the
-                |  #[+a(gh("spaCy") + "/releases/tag/v2.0.0-alpha") release notes]
-                |  for details on how to install and test the new version. To
-                |  read the official docs for spaCy v1.x,
-                |  #[+a("https://spacy.io/docs") go here].
-
-        !=yield
+        if IS_MODELS
+            include _page_models
+        else
+            !=yield
 
     +grid.o-content.u-text
         +grid-col("half")
-            if next && public.docs[SUBSECTION]._data[next]
-                - data = public.docs[SUBSECTION]._data[next]
-
+            if !IS_MODELS
                 .o-inline-list
-                    span #[strong.u-text-label Read next:] #[+a(next).u-link=data.title]
+                    +button(gh("spacy", "website/" + current.path.join('/') + ".jade"), false, "secondary", "small")
+                        |  #[span.o-icon Suggest edits] #[+icon("code", 14)]
 
         +grid-col("half").u-text-right
-            .o-inline-list
-                +button(gh("spacy", "website/" + current.path.join('/') + ".jade"), false, "secondary").u-text-tag Suggest edits #[+icon("code", 14)]
+            if next && public[SECTION]._data[next]
+                - data = public[SECTION]._data[next]
+
+                +grid("vcenter")
+                    +a(next).u-text-small.u-flex-full
+                        h4.u-text-label.u-color-dark Read next
+                        |  #{data.title}
+
+                    +a(next).c-icon-button.c-icon-button--right(aria-hidden="true")
+                        +icon("arrow-right", 24)
 
     +gitter("spaCy chat")
 
diff --git a/website/_includes/_page_models.jade b/website/_includes/_page_models.jade
new file mode 100644
index 000000000..6370f1b94
--- /dev/null
+++ b/website/_includes/_page_models.jade
@@ -0,0 +1,77 @@
+//- 💫 INCLUDES > MODELS PAGE TEMPLATE
+
+for id in CURRENT_MODELS
+    +section(id)
+        +grid("vcenter").o-no-block(id=id)
+            +grid-col("two-thirds")
+                +h(2)
+                    +a("#" + id).u-permalink=id
+
+            +grid-col("third").u-text-right
+                .u-color-subtle.u-text-tiny
+                    +button(gh("spacy-models") + "/releases", true, "secondary", "small")(data-tpl=id data-tpl-key="download")
+                        |  Release details
+                    .u-padding-small Latest: #[code(data-tpl=id data-tpl-key="version") n/a]
+
+        +aside-code("Installation", "bash", "$").
+            spacy download #{id}
+
+        - var comps = getModelComponents(id)
+
+        p(data-tpl=id data-tpl-key="description")
+
+        div(data-tpl=id data-tpl-key="error" style="display: none")
+            +infobox
+                |  Unable to load model details from GitHub. To find out more
+                |  about this model, see the overview of the
+                |  #[+a(gh("spacy-models") + "/releases") latest model releases].
+
+        +table(data-tpl=id data-tpl-key="table")
+            +row
+                +cell #[+label Language]
+                +cell #[+tag=comps.lang] #{LANGUAGES[comps.lang]}
+            for comp, label in {"Type": comps.type, "Genre": comps.genre}
+                +row
+                    +cell #[+label=label]
+                    +cell #[+tag=comp] #{MODEL_META[comp]}
+            +row
+                +cell #[+label Size]
+                +cell #[+tag=comps.size] #[span(data-tpl=id data-tpl-key="size") #[em n/a]]
+
+            each label in ["Pipeline", "Sources", "Author", "License"]
+                - var field = label.toLowerCase()
+                +row
+                    +cell.u-nowrap
+                        +label=label
+                            if MODEL_META[field]
+                                |  #[+help(MODEL_META[field]).u-color-subtle]
+                    +cell
+                        span(data-tpl=id data-tpl-key=field) #[em n/a]
+
+            +row(data-tpl=id data-tpl-key="compat-wrapper" style="display: none")
+                +cell
+                    +label Compat #[+help("Latest compatible model version for your spaCy installation").u-color-subtle]
+                +cell
+                    .o-field.u-float-left
+                        select.o-field__select.u-text-small(data-tpl=id data-tpl-key="compat")
+                    .o-empty(data-tpl=id data-tpl-key="compat-versions") &nbsp;
+
+        section(data-tpl=id data-tpl-key="accuracy-wrapper" style="display: none")
+            +grid.o-no-block
+                +grid-col("third")
+                    +h(4) Accuracy
+                    +table.o-no-block
+                        for label, field in MODEL_ACCURACY
+                            +row(style="display: none")
+                                +cell.u-nowrap
+                                    +label=label
+                                        if MODEL_META[field]
+                                            |  #[+help(MODEL_META[field]).u-color-subtle]
+                                +cell.u-text-right(data-tpl=id data-tpl-key=field)
+                                    |  n/a
+
+                +grid-col("two-thirds")
+                    +h(4) Comparison
+                    +chart(id).u-padding-small
+
+        p.u-text-small.u-color-dark(data-tpl=id data-tpl-key="notes")
diff --git a/website/_includes/_sidebar.jade b/website/_includes/_sidebar.jade
index 241a77132..1bca2cb80 100644
--- a/website/_includes/_sidebar.jade
+++ b/website/_includes/_sidebar.jade
@@ -1,13 +1,23 @@
 //- 💫 INCLUDES > SIDEBAR
 
-include _mixins
-
 menu.c-sidebar.js-sidebar.u-text
     if sidebar_content
-        each items, menu in sidebar_content
-            ul.c-sidebar__section.o-block
-                li.u-text-label.u-color-subtle=menu
+        each items, sectiontitle in sidebar_content
+            ul.c-sidebar__section.o-block-small
+                li.u-text-label.u-color-dark=sectiontitle
 
                 each url, item in items
-                    li(class=(CURRENT == url || (CURRENT == "index" && url == "./")) ? "is-active" : null)
-                        +a(url)=item
+                    - var is_current = CURRENT == url || (CURRENT == "index" && url == "./")
+                    li.c-sidebar__item
+                        +a(url)(class=is_current ? "is-active" : null)=item
+
+                        if is_current
+                            if IS_MODELS && CURRENT_MODELS.length
+                                - menu = Object.assign({}, ...CURRENT_MODELS.map(id => ({ [id]: id })))
+                            if menu
+                                ul.c-sidebar__crumb.u-hidden-sm
+                                    - var counter = 0
+                                    for id, title in menu
+                                        - counter++
+                                        li.c-sidebar__crumb__item(data-nav=id class=(counter == 1) ? "is-active" : null)
+                                            +a("#section-" + id)=title
diff --git a/website/_layout.jade b/website/_layout.jade
index b198c8333..31c6ce6c3 100644
--- a/website/_layout.jade
+++ b/website/_layout.jade
@@ -2,11 +2,15 @@
 
 include _includes/_mixins
 
+- title = IS_MODELS ? LANGUAGES[current.source] || title : title
+- social_img = SITE_URL + "/assets/img/social/preview_" + (preview || ALPHA ? "alpha" : "default") + ".jpg"
+
 doctype html
 html(lang="en")
     title
-        if SECTION == "docs" && SUBSECTION && SUBSECTION != "index"
-            | #{title} | #{SITENAME} #{SUBSECTION == "api" ? "API" : "Usage"} Documentation
+        if SECTION == "api" || SECTION == "usage" || SECTION == "models"
+            - var title_section = (SECTION == "api") ? "API" : SECTION.charAt(0).toUpperCase() + SECTION.slice(1)
+            | #{title} | #{SITENAME} #{title_section} Documentation
 
         else if SECTION != "index"
             | #{title} | #{SITENAME}
@@ -24,23 +28,20 @@ html(lang="en")
     meta(property="og:url" content="#{SITE_URL}/#{current.path.join('/')}")
     meta(property="og:title" content="#{title} - spaCy")
     meta(property="og:description" content=description)
-    meta(property="og:image" content=getSocialImg())
+    meta(property="og:image" content=social_img)
 
     meta(name="twitter:card" content="summary_large_image")
     meta(name="twitter:site" content="@" + SOCIAL.twitter)
     meta(name="twitter:title" content="#{title} - spaCy")
     meta(name="twitter:description" content=description)
-    meta(name="twitter:image" content=getSocialImg())
+    meta(name="twitter:image" content=social_img)
 
     link(rel="shortcut icon" href="/assets/img/favicon.ico")
     link(rel="icon" type="image/x-icon" href="/assets/img/favicon.ico")
 
-    if ALPHA && SECTION == "docs"
+    if SECTION == "api"
         link(href="/assets/css/style_green.css?v#{V_CSS}" rel="stylesheet")
 
-    else if SUBSECTION == "usage"
-        link(href="/assets/css/style_red.css?v#{V_CSS}" rel="stylesheet")
-
     else
         link(href="/assets/css/style.css?v#{V_CSS}" rel="stylesheet")
 
@@ -48,7 +49,7 @@ html(lang="en")
         include _includes/_svg
         include _includes/_navigation
 
-        if SECTION == "docs"
+        if !landing
             include _includes/_page-docs
 
         else

From 3f4fd2c5d5d61d9a939a563ff3747b57fba6ef25 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 14:26:20 +0200
Subject: [PATCH 165/213] Update usage documentation

---
 website/docs/_data.json                       |  28 -
 website/docs/index.jade                       |  25 -
 website/docs/usage/_data.json                 | 420 --------------
 website/docs/usage/_models-list.jade          |  24 -
 website/docs/usage/deep-learning.jade         |  92 ---
 website/docs/usage/index.jade                 | 353 ------------
 website/docs/usage/production-use.jade        | 147 -----
 website/docs/usage/showcase.jade              |  44 --
 website/docs/usage/spacy-101.jade             | 430 --------------
 website/docs/usage/text-classification.jade   |   5 -
 website/docs/usage/training-ner.jade          | 114 ----
 website/docs/usage/tutorials.jade             |  38 --
 website/docs/usage/v2.jade                    | 531 ------------------
 .../_adding-languages/_language-data.jade}    | 354 +++---------
 website/usage/_adding-languages/_testing.jade |  76 +++
 .../usage/_adding-languages/_training.jade    |  93 +++
 website/usage/_data.json                      | 195 +++++++
 website/usage/_deep-learning/_dynet.jade      |  11 +
 .../usage/_deep-learning/_pre-processing.jade |   3 +
 website/usage/_deep-learning/_pytorch.jade    |  91 +++
 .../usage/_deep-learning/_scikit-learn.jade   |  15 +
 .../_deep-learning/_tensorflow-keras.jade     |  11 +
 website/usage/_deep-learning/_thinc.jade      |  66 +++
 .../_facts-figures/_benchmarks-choi-2015.jade |  45 ++
 .../_facts-figures/_benchmarks-models.jade    |  48 ++
 website/usage/_facts-figures/_benchmarks.jade | 206 +++++++
 .../_facts-figures/_feature-comparison.jade   |  58 ++
 .../_facts-figures/_other-libraries.jade      |  70 +++
 website/usage/_install/_changelog.jade        |  31 +
 website/usage/_install/_instructions.jade     | 185 ++++++
 website/usage/_install/_quickstart.jade       |  26 +
 website/usage/_install/_troubleshooting.jade  | 147 +++++
 .../_dependency-parse.jade}                   |  25 +-
 .../_named-entities.jade}                     |  78 +--
 .../_linguistic-features/_pos-tagging.jade}   |  26 +-
 .../_rule-based-matching.jade}                |  29 +-
 .../_linguistic-features/_tokenization.jade}  |  95 +++-
 website/usage/_models/_available-models.jade  |  22 +
 website/usage/_models/_install-basics.jade    |  33 ++
 .../_models/_install.jade}                    |  87 +--
 website/usage/_models/_production.jade        |  81 +++
 website/usage/_models/_quickstart.jade        |  17 +
 .../_processing-pipelines/_examples.jade      | 126 +++++
 .../_multithreading.jade                      |  40 ++
 .../_processing-pipelines/_pipelines.jade}    | 198 ++-----
 .../_processing-pipelines/_serialization.jade |  38 ++
 .../_processing-pipelines/_user-hooks.jade    |  61 ++
 .../usage/_spacy-101/_architecture.jade       | 116 ++--
 website/usage/_spacy-101/_community-faq.jade  | 141 +++++
 .../usage/_spacy-101/_language-data.jade      |  30 +-
 .../_spacy-101/_lightning-tour.jade}          |  56 +-
 .../usage/_spacy-101/_named-entities.jade     |   4 +-
 .../usage/_spacy-101/_pipelines.jade          |  16 +-
 .../usage/_spacy-101/_pos-deps.jade           |   4 +-
 .../usage/_spacy-101/_serialization.jade      |   0
 .../usage/_spacy-101/_similarity.jade         |   0
 .../usage/_spacy-101/_tokenization.jade       |   8 +-
 .../usage/_spacy-101/_training.jade           |   6 +-
 .../{docs => }/usage/_spacy-101/_vocab.jade   |   6 +-
 .../usage/_spacy-101/_word-vectors.jade       |   4 +-
 .../_training/_basics.jade}                   |  34 +-
 website/usage/_training/_ner.jade             |  61 ++
 .../_training/_saving-loading.jade}           |  90 ++-
 website/usage/_training/_similarity.jade      |   3 +
 website/usage/_training/_tagger-parser.jade   |   3 +
 website/usage/_training/_textcat.jade         |  13 +
 .../usage/_vectors-similarity/_basics.jade    |  15 +
 .../usage/_vectors-similarity/_custom.jade    |  91 +++
 website/usage/_vectors-similarity/_gpu.jade   |  30 +
 .../_vectors-similarity/_in-context.jade}     |  45 +-
 website/usage/adding-languages.jade           |  59 ++
 website/usage/deep-learning.jade              |  29 +
 website/usage/examples.jade                   |  73 +++
 website/usage/facts-figures.jade              |  32 ++
 website/usage/index.jade                      |  27 +
 website/usage/linguistic-features.jade        |  38 ++
 website/usage/models.jade                     |  37 ++
 website/usage/processing-pipelines.jade       |  25 +
 website/usage/resources.jade                  | 125 +++++
 website/usage/spacy-101.jade                  | 300 ++++++++++
 website/usage/text-classification.jade        |   9 +
 website/usage/training.jade                   |  33 ++
 website/usage/v2.jade                         | 520 +++++++++++++++++
 website/usage/vectors-similarity.jade         |  18 +
 website/{docs => }/usage/visualizers.jade     |  10 +-
 85 files changed, 3906 insertions(+), 3143 deletions(-)
 delete mode 100644 website/docs/_data.json
 delete mode 100644 website/docs/index.jade
 delete mode 100644 website/docs/usage/_data.json
 delete mode 100644 website/docs/usage/_models-list.jade
 delete mode 100644 website/docs/usage/deep-learning.jade
 delete mode 100644 website/docs/usage/index.jade
 delete mode 100644 website/docs/usage/production-use.jade
 delete mode 100644 website/docs/usage/showcase.jade
 delete mode 100644 website/docs/usage/spacy-101.jade
 delete mode 100644 website/docs/usage/text-classification.jade
 delete mode 100644 website/docs/usage/training-ner.jade
 delete mode 100644 website/docs/usage/tutorials.jade
 delete mode 100644 website/docs/usage/v2.jade
 rename website/{docs/usage/adding-languages.jade => usage/_adding-languages/_language-data.jade} (62%)
 create mode 100644 website/usage/_adding-languages/_testing.jade
 create mode 100644 website/usage/_adding-languages/_training.jade
 create mode 100644 website/usage/_data.json
 create mode 100644 website/usage/_deep-learning/_dynet.jade
 create mode 100644 website/usage/_deep-learning/_pre-processing.jade
 create mode 100644 website/usage/_deep-learning/_pytorch.jade
 create mode 100644 website/usage/_deep-learning/_scikit-learn.jade
 create mode 100644 website/usage/_deep-learning/_tensorflow-keras.jade
 create mode 100644 website/usage/_deep-learning/_thinc.jade
 create mode 100644 website/usage/_facts-figures/_benchmarks-choi-2015.jade
 create mode 100644 website/usage/_facts-figures/_benchmarks-models.jade
 create mode 100644 website/usage/_facts-figures/_benchmarks.jade
 create mode 100644 website/usage/_facts-figures/_feature-comparison.jade
 create mode 100644 website/usage/_facts-figures/_other-libraries.jade
 create mode 100644 website/usage/_install/_changelog.jade
 create mode 100644 website/usage/_install/_instructions.jade
 create mode 100644 website/usage/_install/_quickstart.jade
 create mode 100644 website/usage/_install/_troubleshooting.jade
 rename website/{docs/usage/dependency-parse.jade => usage/_linguistic-features/_dependency-parse.jade} (93%)
 rename website/{docs/usage/entity-recognition.jade => usage/_linguistic-features/_named-entities.jade} (74%)
 rename website/{docs/usage/pos-tagging.jade => usage/_linguistic-features/_pos-tagging.jade} (76%)
 rename website/{docs/usage/rule-based-matching.jade => usage/_linguistic-features/_rule-based-matching.jade} (95%)
 rename website/{docs/usage/customizing-tokenizer.jade => usage/_linguistic-features/_tokenization.jade} (76%)
 create mode 100644 website/usage/_models/_available-models.jade
 create mode 100644 website/usage/_models/_install-basics.jade
 rename website/{docs/usage/models.jade => usage/_models/_install.jade} (67%)
 create mode 100644 website/usage/_models/_production.jade
 create mode 100644 website/usage/_models/_quickstart.jade
 create mode 100644 website/usage/_processing-pipelines/_examples.jade
 create mode 100644 website/usage/_processing-pipelines/_multithreading.jade
 rename website/{docs/usage/language-processing-pipeline.jade => usage/_processing-pipelines/_pipelines.jade} (56%)
 create mode 100644 website/usage/_processing-pipelines/_serialization.jade
 create mode 100644 website/usage/_processing-pipelines/_user-hooks.jade
 rename website/{docs => }/usage/_spacy-101/_architecture.jade (83%)
 create mode 100644 website/usage/_spacy-101/_community-faq.jade
 rename website/{docs => }/usage/_spacy-101/_language-data.jade (86%)
 rename website/{docs/usage/lightning-tour.jade => usage/_spacy-101/_lightning-tour.jade} (82%)
 rename website/{docs => }/usage/_spacy-101/_named-entities.jade (90%)
 rename website/{docs => }/usage/_spacy-101/_pipelines.jade (89%)
 rename website/{docs => }/usage/_spacy-101/_pos-deps.jade (95%)
 rename website/{docs => }/usage/_spacy-101/_serialization.jade (100%)
 rename website/{docs => }/usage/_spacy-101/_similarity.jade (100%)
 rename website/{docs => }/usage/_spacy-101/_tokenization.jade (90%)
 rename website/{docs => }/usage/_spacy-101/_training.jade (94%)
 rename website/{docs => }/usage/_spacy-101/_vocab.jade (96%)
 rename website/{docs => }/usage/_spacy-101/_word-vectors.jade (98%)
 rename website/{docs/usage/training.jade => usage/_training/_basics.jade} (89%)
 create mode 100644 website/usage/_training/_ner.jade
 rename website/{docs/usage/saving-loading.jade => usage/_training/_saving-loading.jade} (70%)
 create mode 100644 website/usage/_training/_similarity.jade
 create mode 100644 website/usage/_training/_tagger-parser.jade
 create mode 100644 website/usage/_training/_textcat.jade
 create mode 100644 website/usage/_vectors-similarity/_basics.jade
 create mode 100644 website/usage/_vectors-similarity/_custom.jade
 create mode 100644 website/usage/_vectors-similarity/_gpu.jade
 rename website/{docs/usage/word-vectors-similarities.jade => usage/_vectors-similarity/_in-context.jade} (72%)
 create mode 100644 website/usage/adding-languages.jade
 create mode 100644 website/usage/deep-learning.jade
 create mode 100644 website/usage/examples.jade
 create mode 100644 website/usage/facts-figures.jade
 create mode 100644 website/usage/index.jade
 create mode 100644 website/usage/linguistic-features.jade
 create mode 100644 website/usage/models.jade
 create mode 100644 website/usage/processing-pipelines.jade
 create mode 100644 website/usage/resources.jade
 create mode 100644 website/usage/spacy-101.jade
 create mode 100644 website/usage/text-classification.jade
 create mode 100644 website/usage/training.jade
 create mode 100644 website/usage/v2.jade
 create mode 100644 website/usage/vectors-similarity.jade
 rename website/{docs => }/usage/visualizers.jade (97%)

diff --git a/website/docs/_data.json b/website/docs/_data.json
deleted file mode 100644
index bc33ebc4c..000000000
--- a/website/docs/_data.json
+++ /dev/null
@@ -1,28 +0,0 @@
-{
-    "index": {
-        "title" : "Documentation",
-
-        "sections": {
-            "Usage": {
-                "url": "/docs/usage",
-                "svg": "computer",
-                "description": "How to use spaCy and its features."
-            },
-            "API": {
-                "url": "/docs/api",
-                "svg": "brain",
-                "description": "The detailed reference for spaCy's API."
-            },
-            "Tutorials": {
-                "url": "/docs/usage/tutorials",
-                "svg": "eye",
-                "description": "End-to-end examples, with code you can modify and run."
-            },
-            "Showcase & Demos": {
-                "url": "/docs/usage/showcase",
-                "svg": "bubble",
-                "description": "Demos, libraries and products from the spaCy community."
-            }
-        }
-    }
-}
diff --git a/website/docs/index.jade b/website/docs/index.jade
deleted file mode 100644
index d5a8c6deb..000000000
--- a/website/docs/index.jade
+++ /dev/null
@@ -1,25 +0,0 @@
-//- 💫 DOCS
-
-include ../_includes/_mixins
-
-+aside("Help us improve the docs")
-    |  Did you spot a mistake or come across explanations that
-    |  are unclear? You can find a "Suggest edits" button at the
-    |  bottom of each page that points you to the source.
-    |  We always appreciate
-    |  #[+a(gh("spaCy") + "/pulls") pull requests].#[br]#[br]
-    |  Have you built something cool with spaCy, or did you
-    |  write a tutorial to help others use spaCy?
-    |  #[a(href="mailto:#{EMAIL}") Let us know!]
-
-+grid
-    each details, title in sections
-        +card(false, false)
-            a(href=details.url)
-                +svg("graphics", details.svg, 300, 150).u-color-theme
-
-            a(href=details.url)
-                +h(3)=title
-
-            p=details.description
-            +button(details.url, true, "primary")(target="_self") View
diff --git a/website/docs/usage/_data.json b/website/docs/usage/_data.json
deleted file mode 100644
index c8373a095..000000000
--- a/website/docs/usage/_data.json
+++ /dev/null
@@ -1,420 +0,0 @@
-{
-    "sidebar": {
-        "Get started": {
-            "Installation": "./",
-            "Models": "models",
-            "spaCy 101": "spacy-101",
-            "Lightning tour": "lightning-tour",
-            "What's new in v2.0": "v2"
-        },
-        "Guides": {
-            "POS tagging": "pos-tagging",
-            "Using the parse": "dependency-parse",
-            "Entity recognition": "entity-recognition",
-            "Vectors & similarity": "word-vectors-similarities",
-            "Custom tokenization": "customizing-tokenizer",
-            "Rule-based matching": "rule-based-matching",
-            "Adding languages": "adding-languages",
-            "Processing pipelines": "language-processing-pipeline",
-            "Text classification": "text-classification",
-            "Deep learning": "deep-learning",
-            "Production use": "production-use",
-            "Training": "training",
-            "Training NER": "training-ner",
-            "Saving & loading": "saving-loading",
-            "Visualizers": "visualizers"
-        },
-        "Examples": {
-            "Tutorials": "tutorials",
-            "Showcase": "showcase"
-        }
-    },
-
-    "index": {
-        "title": "Install spaCy",
-        "next": "models",
-        "quickstart": true
-    },
-
-    "models": {
-        "title": "Models",
-        "next": "spacy-101",
-        "quickstart": true
-    },
-
-    "spacy-101": {
-        "title": "spaCy 101 – Everything you need to know",
-        "next": "lightning-tour",
-        "quickstart": true,
-        "preview": "101"
-    },
-
-    "lightning-tour": {
-        "title": "Lightning tour",
-        "next": "v2"
-    },
-
-    "visualizers": {
-        "title": "Visualizers"
-    },
-
-    "v2": {
-        "title": "What's new in v2.0"
-    },
-
-    "pos-tagging": {
-        "title": "Part-of-speech tagging",
-        "next": "dependency-parse"
-    },
-
-    "dependency-parse": {
-        "title": "Using the dependency parse",
-        "next": "entity-recognition"
-    },
-
-    "entity-recognition": {
-        "title": "Named Entity Recognition",
-        "next": "training-ner"
-    },
-
-    "word-vectors-similarities": {
-        "title": "Using word vectors and semantic similarities",
-        "next": "customizing-tokenizer"
-    },
-
-    "customizing-tokenizer": {
-        "title": "Customising the tokenizer",
-        "next": "rule-based-matching"
-    },
-
-    "rule-based-matching": {
-        "title": "Rule-based matching",
-        "next": "adding-languages"
-    },
-
-    "adding-languages": {
-        "title": "Adding languages",
-        "next": "training"
-    },
-
-    "language-processing-pipeline": {
-        "title": "Language processing pipelines",
-        "next": "deep-learning"
-    },
-
-    "deep-learning": {
-        "title": "Hooking a deep learning model into spaCy",
-        "next": "production use"
-    },
-
-    "text-classification": {
-        "title": "Text classification",
-        "next": "training"
-    },
-
-    "production-use": {
-        "title": "Production use",
-        "next": "training"
-    },
-
-    "training": {
-        "title": "Training spaCy's statistical models",
-        "next": "saving-loading"
-    },
-
-    "training-ner": {
-        "title": "Training the Named Entity Recognizer",
-        "next": "saving-loading"
-    },
-
-    "saving-loading": {
-        "title": "Saving, loading and data serialization"
-    },
-
-    "showcase": {
-        "title": "Showcase",
-
-        "libraries": {
-            "spacy_api": {
-                "url": "https://github.com/kootenpv/spacy_api",
-                "author": "Pascal van Kooten",
-                "description": "Server/client to load models in a separate, dedicated process."
-            },
-            "spacy-nlp": {
-                "url": "https://github.com/kengz/spacy-nlp",
-                "author": "Wah Loon Keng",
-                "description": "Expose spaCy NLP text parsing to Node.js (and other languages) via Socket.IO."
-            },
-            "spacy-api-docker": {
-                "url": "https://github.com/jgontrum/spacy-api-docker",
-                "author": "Johannes Gontrum",
-                "description": "spaCy accessed by a REST API, wrapped in a Docker container."
-            },
-            "spacy-nlp-zeromq": {
-                "url": "https://github.com/pasupulaphani/spacy-nlp-docker",
-                "author": "Phaninder Pasupula",
-                "description": "Docker image exposing spaCy with ZeroMQ bindings."
-            },
-            "textacy": {
-                "url": "https://github.com/chartbeat-labs/textacy",
-                "author": " Burton DeWilde (Chartbeat)",
-                "description": "Higher-level NLP built on spaCy."
-            },
-            "visual-qa": {
-                "url": "https://github.com/avisingh599/visual-qa",
-                "author": "Avi Singh",
-                "description": "Keras-based LSTM/CNN models for Visual Question Answering."
-            },
-            "rasa_nlu": {
-                "url": "https://github.com/golastmile/rasa_nlu",
-                "author": "LASTMILE",
-                "description": "High level APIs for building your own language parser using existing NLP and ML libraries."
-            },
-            "spacyr": {
-                "url": "https://github.com/kbenoit/spacyr",
-                "author": "Kenneth Benoit",
-                "description": "An R wrapper for spaCy."
-            }
-        },
-        "visualizations": {
-            "displaCy": {
-                "url": "https://demos.explosion.ai/displacy",
-                "author": "Ines Montani",
-                "description": "An open-source NLP visualiser for the modern web.",
-                "image": "displacy.jpg"
-            },
-            "displaCy ENT": {
-                "url": "https://demos.explosion.ai/displacy-ent",
-                "author": "Ines Montani",
-                "description": "An open-source named entity visualiser for the modern web.",
-                "image": "displacy-ent.jpg"
-            }
-        },
-        "products": {
-            "sense2vec": {
-                "url": "https://demos.explosion.ai/sense2vec",
-                "author": "Matthew Honnibal and Ines Montani",
-                "description": "Semantic analysis of the Reddit hivemind.",
-                "image": "sense2vec.jpg"
-            },
-            "TruthBot": {
-                "url": "http://summerscope.github.io/govhack/2016/truthbot/",
-                "author": "Team Truthbot",
-                "description": "The world's first artificially intelligent fact checking robot.",
-                "image": "truthbot.jpg"
-            },
-            "Laice": {
-                "url": "https://github.com/kendricktan/laice",
-                "author": "Kendrick Tan",
-                "description": "Train your own Natural Language Processor from a browser.",
-                "image": "laice.jpg"
-            },
-            "FoxType": {
-                "url": "https://foxtype.com",
-                "description": "Smart tools for writers.",
-                "image": "foxtype.jpg"
-            },
-            "Kip": {
-                "url": "https://kipthis.com",
-                "description": "An AI chat assistant for group shopping.",
-                "image": "kip.jpg"
-            },
-            "Indico": {
-                "url": "https://indico.io",
-                "description": "Text and image analysis powered by Machine Learning.",
-                "image": "indico.jpg"
-            },
-            "TextAnalysisOnline": {
-                "url": "http://textanalysisonline.com",
-                "description": "Online tool for spaCy's tokenizer, parser, NER and more.",
-                "image": "textanalysis.jpg"
-            }
-        },
-        "books": {
-            "Introduction to Machine Learning with Python: A Guide for Data Scientists": {
-                "url": "https://books.google.de/books?id=vbQlDQAAQBAJ",
-                "author": "Andreas C. Müller and Sarah Guido (O'Reilly, 2016)",
-                "description": "Andreas is a lead developer of Scikit-Learn, and Sarah is a lead data scientist at Mashable. We're proud to get a mention."
-            },
-
-            "Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data": {
-                "url": "https://www.amazon.com/Text-Analytics-Python-Real-World-Actionable/dp/148422387X",
-                "author": "Dipanjan Sarkar (Apress / Springer, 2016)",
-                "description": "Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem."
-            }
-        },
-        "research": {
-            "Distributional semantics for understanding spoken meal descriptions": {
-                "url": "https://www.semanticscholar.org/paper/Distributional-semantics-for-understanding-spoken-Korpusik-Huang/5f55c5535e80d3e5ed7f1f0b89531e32725faff5",
-                "author": "Mandy Korpusik et al. (2016)"
-            },
-
-            "Refactoring the Genia Event Extraction Shared Task Toward a General Framework for IE-Driven KB Development": {
-                "url": "https://www.semanticscholar.org/paper/Refactoring-the-Genia-Event-Extraction-Shared-Task-Kim-Wang/06d94b64a7bd2d3433f57caddad5084435d6a91f",
-                "author": "Jin-Dong Kim et al. (2016)"
-            },
-            "Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec": {
-                "url": "https://www.semanticscholar.org/paper/Mixing-Dirichlet-Topic-Models-and-Word-Embeddings-Moody/bf8116e06f7b498c6abfbf97aeb67d0838c08609",
-                "author": "Christopher E. Moody (2016)"
-            },
-            "Predicting Pre-click Quality for Native Advertisements": {
-                "url": "https://www.semanticscholar.org/paper/Predicting-Pre-click-Quality-for-Native-Zhou-Redi/564985430ff2fbc3a9daa9c2af8997b7f5046da8",
-                "author": "Ke Zhou et al. (2016)"
-            },
-            "Threat detection in online discussions": {
-                "url": "https://www.semanticscholar.org/paper/Threat-detection-in-online-discussions-Wester-%C3%98vrelid/f4150e2fb4d8646ebc2ea84f1a86afa1b593239b",
-                "author": "Aksel Wester et al. (2016)"
-            },
-            "The language of mental health problems in social media": {
-                "url": "https://www.semanticscholar.org/paper/The-language-of-mental-health-problems-in-social-Gkotsis-Oellrich/537db6c2984514d92a754a591841e2e20845985a",
-                "author": "George Gkotsis et al. (2016)"
-            }
-        }
-    },
-
-    "tutorials": {
-        "title": "Tutorials",
-        "next": "showcase",
-
-        "first_steps": {
-            "Setting up an NLP environment with Python": {
-                "url": "https://shirishkadam.com/2016/10/06/setting-up-natural-language-processing-environment-with-python/",
-                "author": "Shirish Kadam"
-            },
-            "NLP with spaCy in 10 lines of code": {
-                "url": "https://github.com/cytora/pycon-nlp-in-10-lines",
-                "author": "Andraz Hribernik et al. (Cytora)",
-                "tags": ["jupyter"]
-            },
-            "Intro to NLP with spaCy": {
-                "url": "https://nicschrading.com/project/Intro-to-NLP-with-spaCy/",
-                "author": "J Nicolas Schrading"
-            },
-            "NLP with spaCy and IPython Notebook": {
-                "url": "http://blog.sharepointexperience.com/2016/01/nlp-and-sharepoint-part-1/",
-                "author": "Dustin Miller (SharePoint)",
-                "tags": ["jupyter"]
-            },
-            "Getting Started with spaCy": {
-                "url": "http://textminingonline.com/getting-started-with-spacy",
-                "author": "TextMiner"
-            },
-            "spaCy – A fast natural language processing library": {
-                "url": "https://bjoernkw.com/2015/11/22/spacy-a-fast-natural-language-processing-library/",
-                "author": "Björn Wilmsmann"
-            },
-            "NLP (almost) From Scratch - POS Network with spaCy": {
-                "url": "http://sujitpal.blogspot.de/2016/07/nlp-almost-from-scratch-implementing.html",
-                "author": "Sujit Pal",
-                "tags": ["gensim", "keras"]
-            },
-            "NLP tasks with various libraries": {
-                "url": "http://clarkgrubb.com/nlp",
-                "author": "Clark Grubb"
-            },
-            "A very (very) short primer on spacy.io": {
-                "url": "http://blog.milonimrod.com/2015/10/a-very-very-short-primer-on-spacyio.html",
-                "author": "Nimrod Milo  "
-            }
-        },
-
-        "deep_dives": {
-            "Modern NLP in Python – What you can learn about food by analyzing a million Yelp reviews": {
-                "url": "http://nbviewer.jupyter.org/github/skipgram/modern-nlp-in-python/blob/master/executable/Modern_NLP_in_Python.ipynb",
-                "author": "Patrick Harrison (S&P Global)",
-                "tags": ["jupyter", "gensim"]
-            },
-            "Deep Learning with custom pipelines and Keras": {
-                "url": "https://explosion.ai/blog/spacy-deep-learning-keras",
-                "author": "Matthew Honnibal",
-                "tags": ["keras", "sentiment"]
-            },
-            "A decomposable attention model for Natural Language Inference": {
-                "url": "https://github.com/explosion/spaCy/tree/master/examples/keras_parikh_entailment",
-                "author": "Matthew Honnibal",
-                "tags": ["keras", "similarity"]
-            },
-
-            "Using the German model": {
-                "url": "https://explosion.ai/blog/german-model",
-                "author": "Wolfgang Seeker",
-                "tags": ["multi-lingual"]
-            },
-            "Sense2vec with spaCy and Gensim": {
-                "url": "https://explosion.ai/blog/sense2vec-with-spacy",
-                "author": "Matthew Honnibal",
-                "tags": ["big data", "gensim"]
-            },
-            "Building your bot's brain with Node.js and spaCy": {
-                "url": "https://explosion.ai/blog/chatbot-node-js-spacy",
-                "author": "Wah Loon Keng",
-                "tags": ["bots", "node.js"]
-            },
-            "An intent classifier with spaCy": {
-                "url": "http://blog.themusio.com/2016/07/18/musios-intent-classifier-2/",
-                "author": "Musio",
-                "tags": ["bots", "keras"]
-            },
-            "Visual Question Answering with spaCy": {
-                "url": "http://iamaaditya.github.io/2016/04/visual_question_answering_demo_notebook",
-                "author": "Aaditya Prakash",
-                "tags": ["vqa", "keras"]
-            },
-            "Extracting time suggestions from emails with spaCy": {
-                "url": "https://medium.com/redsift-outbox/what-time-cc9ce0c2aed2",
-                "author": "Chris Savvopoulos",
-                "tags": ["ner"]
-            },
-
-            "Advanced text analysis with spaCy and Scikit-Learn": {
-                "url": "https://github.com/JonathanReeve/advanced-text-analysis-workshop-2017/blob/master/advanced-text-analysis.ipynb",
-                "author": "Jonathan Reeve",
-                "tags": ["jupyter", "scikit-learn"]
-            }
-        },
-
-        "code": {
-            "Training a new entity type": {
-                "url": "https://github.com/explosion/spaCy/blob/master/examples/training/train_new_entity_type.py",
-                "author": "Matthew Honnibal",
-                "tags": ["ner", "training"]
-            },
-
-            "Training an NER system from scratch": {
-                "url": "https://github.com/explosion/spaCy/blob/master/examples/training/train_ner_standalone.py",
-                "author": "Matthew Honnibal",
-                "tags": ["ner", "training"]
-            },
-
-            "Information extraction": {
-                "url": "https://github.com/explosion/spaCy/blob/master/examples/information_extraction.py",
-                "author": "Matthew Honnibal",
-                "tags": ["snippet"]
-            },
-            "Neural bag of words": {
-                "url": "https://github.com/explosion/spaCy/blob/master/examples/nn_text_class.py",
-                "author": "Matthew Honnibal",
-                "tags": ["sentiment"]
-            },
-            "Part-of-speech tagging": {
-                "url": "https://github.com/explosion/spaCy/blob/master/examples/pos_tag.py",
-                "author": "Matthew Honnibal",
-                "tags": ["pos"]
-            },
-            "Parallel parse": {
-                "url": "https://github.com/explosion/spaCy/blob/master/examples/parallel_parse.py",
-                "author": "Matthew Honnibal",
-                "tags": ["big data"]
-            },
-            "Inventory count": {
-                "url": "https://github.com/explosion/spaCy/tree/master/examples/inventory_count",
-                "author": "Oleg Zd"
-            },
-            "Multi-word matches": {
-                "url": "https://github.com/explosion/spaCy/blob/master/examples/multi_word_matches.py",
-                "author": "Matthew Honnibal",
-                "tags": ["matcher", "out of date"]
-            }
-        }
-    }
-}
diff --git a/website/docs/usage/_models-list.jade b/website/docs/usage/_models-list.jade
deleted file mode 100644
index 195df9f56..000000000
--- a/website/docs/usage/_models-list.jade
+++ /dev/null
@@ -1,24 +0,0 @@
-//- 💫 DOCS > USAGE > MODELS LIST
-
-include ../../_includes/_mixins
-
-p
-    |  Model differences are mostly statistical. In general, we do expect larger
-    |  models to be "better" and more accurate overall. Ultimately, it depends on
-    |  your use case and requirements, and we recommend starting with the default
-    |  models (marked with a star below).
-
-+aside
-    |  Models are now available as #[code .tar.gz] archives #[+a(gh("spacy-models")) from GitHub],
-    |  attached to individual releases. They can be downloaded and loaded manually,
-    |  or using spaCy's #[code download] and #[code link] commands. All models
-    |  follow the naming convention of #[code [language]_[type]_[genre]_[size]].
-    | #[br]#[br]
-
-    +button(gh("spacy-models"), true, "primary").u-text-tag
-        |  View model releases
-
-+table(["Name", "Language", "Voc", "Dep", "Ent", "Vec", "Size", "License"])
-    for models, lang in MODELS
-        for model, i in models
-            +model-row(model.id, model.lang, model.feats, model.size, model.license, model.def || models.length == 1, i == 0)
diff --git a/website/docs/usage/deep-learning.jade b/website/docs/usage/deep-learning.jade
deleted file mode 100644
index 78448e43e..000000000
--- a/website/docs/usage/deep-learning.jade
+++ /dev/null
@@ -1,92 +0,0 @@
-//- 💫 DOCS > USAGE > DEEP LEARNING
-
-include ../../_includes/_mixins
-
-p
-    |  In this example, we'll be using #[+a("https://keras.io/") Keras], as
-    |  it's the most popular deep learning library for Python. Using Keras,
-    |  we will write a custom sentiment analysis model that predicts whether a
-    |  document is positive or negative. Then, we will use it to find which entities
-    |  are commonly associated with positive or negative documents. Here's a
-    |  quick example of how that can look at runtime.
-
-+aside("What's Keras?")
-    |  #[+a("https://keras.io/") Keras] gives you a high-level, declarative
-    |  interface to define neural networks. Models are trained using Google's
-    |  #[+a("https://www.tensorflow.org") TensorFlow] by default.
-    |  #[+a("http://deeplearning.net/software/theano/") Theano] is also
-    |  supported.
-
-+under-construction
-
-p
-    |  For most applications, I it's recommended to use pre-trained word embeddings
-    |  without "fine-tuning". This means that you'll use the same embeddings
-    |  across different models, and avoid learning adjustments to them on your
-    |  training data. The embeddings table is large, and the values provided by
-    |  the pre-trained vectors are already pretty good. Fine-tuning the
-    |  embeddings table is therefore a waste of your "parameter budget". It's
-    |  usually better to make your network larger some other way, e.g. by
-    |  adding another LSTM layer, using attention mechanism, using character
-    |  features, etc.
-
-+h(2, "attribute-hooks") Attribute hooks
-
-+under-construction
-
-p
-    |  Earlier, we saw how to store data in the new generic #[code user_data]
-    |  dict. This generalises well, but it's not terribly satisfying. Ideally,
-    |  we want to let the custom data drive more "native" behaviours. For
-    |  instance, consider the #[code .similarity()] methods provided by spaCy's
-    |  #[+api("doc") #[code Doc]], #[+api("token") #[code Token]] and
-    |  #[+api("span") #[code Span]] objects:
-
-+code("Polymorphic similarity example").
-    span.similarity(doc)
-    token.similarity(span)
-    doc1.similarity(doc2)
-
-p
-    |  By default, this just averages the vectors for each document, and
-    |  computes their cosine. Obviously, spaCy should make it easy for you to
-    |  install your own similarity model. This introduces a tricky design
-    |  challenge. The current solution is to add three more dicts to the
-    |  #[code Doc] object:
-
-+aside("Implementation note")
-    |  The hooks live on the #[code Doc] object because the #[code Span] and
-    |  #[code Token] objects are created lazily, and don't own any data. They
-    |  just proxy to their parent #[code Doc]. This turns out to be convenient
-    |  here — we only have to worry about installing hooks in one place.
-
-+table(["Name", "Description"])
-    +row
-        +cell #[code user_hooks]
-        +cell Customise behaviour of #[code doc.vector], #[code doc.has_vector], #[code doc.vector_norm] or #[code doc.sents]
-
-    +row
-        +cell #[code user_token_hooks]
-        +cell Customise behaviour of #[code token.similarity], #[code token.vector], #[code token.has_vector], #[code token.vector_norm] or #[code token.conjuncts]
-
-    +row
-        +cell #[code user_span_hooks]
-        +cell Customise behaviour of #[code span.similarity], #[code span.vector], #[code span.has_vector], #[code span.vector_norm] or #[code span.root]
-
-p
-    |  To sum up, here's an example of hooking in custom #[code .similarity()]
-    |  methods:
-
-+code("Add custom similarity hooks").
-    class SimilarityModel(object):
-        def __init__(self, model):
-            self._model = model
-
-        def __call__(self, doc):
-            doc.user_hooks['similarity'] = self.similarity
-            doc.user_span_hooks['similarity'] = self.similarity
-            doc.user_token_hooks['similarity'] = self.similarity
-
-        def similarity(self, obj1, obj2):
-            y = self._model([obj1.vector, obj2.vector])
-            return float(y[0])
diff --git a/website/docs/usage/index.jade b/website/docs/usage/index.jade
deleted file mode 100644
index a0aa1dca8..000000000
--- a/website/docs/usage/index.jade
+++ /dev/null
@@ -1,353 +0,0 @@
-//- 💫 DOCS > USAGE
-
-include ../../_includes/_mixins
-
-p
-    |  spaCy is compatible with #[strong 64-bit CPython 2.6+&#8725;3.3+] and
-    |  runs on #[strong Unix/Linux], #[strong macOS/OS X] and
-    |  #[strong Windows]. The latest spaCy releases are
-    |  available over #[+a("https://pypi.python.org/pypi/spacy") pip] (source
-    |  packages only) and #[+a("https://anaconda.org/conda-forge/spacy") conda].
-    |  Installation requires a working build environment. See notes on
-    |  #[a(href="#source-ubuntu") Ubuntu], #[a(href="#source-osx") macOS/OS X]
-    |  and #[a(href="#source-windows") Windows] for details.
-
-+quickstart(QUICKSTART, "Quickstart")
-    +qs({config: 'venv', python: 2}) python -m pip install -U virtualenv
-    +qs({config: 'venv', python: 3}) python -m pip install -U venv
-    +qs({config: 'venv', python: 2}) virtualenv .env
-    +qs({config: 'venv', python: 3}) venv .env
-    +qs({config: 'venv', os: 'mac'}) source .env/bin/activate
-    +qs({config: 'venv', os: 'linux'}) source .env/bin/activate
-    +qs({config: 'venv', os: 'windows'}) .env\Scripts\activate
-
-    +qs({config: 'gpu', os: 'mac'}) export PATH=$PATH:/usr/local/cuda-8.0/bin
-    +qs({config: 'gpu', os: 'linux'}) export PATH=$PATH:/usr/local/cuda-8.0/bin
-
-    +qs({package: 'pip'}) pip install -U spacy
-    +qs({package: 'conda'}) conda install -c conda-forge spacy
-
-    +qs({package: 'source'}) git clone https://github.com/explosion/spaCy
-    +qs({package: 'source'}) cd spaCy
-    +qs({package: 'source'}) pip install -r requirements.txt
-    +qs({package: 'source'}) pip install -e .
-
-    +qs({model: 'en'}) spacy download en
-    +qs({model: 'de'}) spacy download de
-    +qs({model: 'fr'}) spacy download fr
-    +qs({model: 'es'}) spacy download es
-
-+h(2, "installation") Installation instructions
-
-+h(3, "pip") pip
-    +badge("pipy")
-
-p Using pip, spaCy releases are currently only available as source packages.
-
-+code(false, "bash").
-    pip install -U spacy
-
-+aside("Download models")
-    |  After installation you need to download a language model. For more info
-    |  and available models, see the #[+a("/docs/usage/models") docs on models].
-
-    +code.o-no-block.
-        spacy download en
-
-        &gt;&gt;&gt; import spacy
-        &gt;&gt;&gt; nlp = spacy.load('en')
-
-p
-    |  When using pip it is generally recommended to install packages in a
-    |  #[code virtualenv] to avoid modifying system state:
-
-+code(false, "bash").
-    virtualenv .env
-    source .env/bin/activate
-    pip install spacy
-
-+h(3, "conda") conda
-    +badge("conda")
-
-p
-    |  Thanks to our great community, we've finally re-added conda support. You
-    |  can now install spaCy via #[code conda-forge]:
-
-+code(false, "bash").
-    conda config --add channels conda-forge
-    conda install spacy
-
-p
-    |  For the feedstock including the build recipe and configuration, check out
-    |  #[+a("https://github.com/conda-forge/spacy-feedstock") this repository].
-    |  Improvements and pull requests to the recipe and setup are always appreciated.
-
-+h(2, "gpu") Run spaCy with GPU
-
-p
-    |  As of v2.0, spaCy's comes with neural network models that are implemented
-    |  in our machine learning library, #[+a(gh("thinc")) Thinc]. For GPU
-    |  support, we've been grateful to use the work of
-    |  #[+a("http://chainer.org") Chainer]'s CuPy module, which provides
-    |  a NumPy-compatible interface for GPU arrays.
-
-p
-    |  First, install follows the normal CUDA installation procedure. Next, set
-    |  your environment variables so that the installation will be able to find
-    |  CUDA. Finally, install spaCy.
-
-+code(false, "bash").
-   export CUDA_HOME=/usr/local/cuda-8.0 # Or wherever your CUDA is
-   export PATH=$PATH:$CUDA_HOME/bin
-
-   pip install spacy
-   python -c "import thinc.neural.gpu_ops" # Check the GPU ops were built
-
-+h(2, "source") Compile from source
-
-p
-    |  The other way to install spaCy is to clone its
-    |  #[+a(gh("spaCy")) GitHub repository] and build it from source. That is
-    |  the common way if you want to make changes to the code base. You'll need to
-    |  make sure that you have a development environment consisting of a Python
-    |  distribution including header files, a compiler,
-    |  #[+a("https://pip.pypa.io/en/latest/installing/") pip],
-    |  #[+a("https://virtualenv.pypa.io/") virtualenv] and
-    |  #[+a("https://git-scm.com") git] installed. The compiler part is the
-    |  trickiest. How to do that depends on your system. See notes on
-    |  #[a(href="#source-ubuntu") Ubuntu], #[a(href="#source-osx") OS X] and
-    |  #[a(href="#source-windows") Windows] for details.
-
-+code(false, "bash").
-    # make sure you are using recent pip/virtualenv versions
-    python -m pip install -U pip virtualenv
-    git clone #{gh("spaCy")}
-    cd spaCy
-
-    virtualenv .env
-    source .env/bin/activate
-    pip install -r requirements.txt
-    pip install -e .
-
-p
-    |  Compared to regular install via pip, #[+a(gh("spaCy", "requirements.txt")) requirements.txt]
-    |  additionally installs developer dependencies such as Cython.
-
-p
-    |  Instead of the above verbose commands, you can also use the following
-    |  #[+a("http://www.fabfile.org/") Fabric] commands:
-
-+table(["Command", "Description"])
-    +row
-        +cell #[code fab env]
-        +cell Create #[code virtualenv] and delete previous one, if it exists.
-
-    +row
-        +cell #[code fab make]
-        +cell Compile the source.
-
-    +row
-        +cell #[code fab clean]
-        +cell Remove compiled objects, including the generated C++.
-
-    +row
-        +cell #[code fab test]
-        +cell Run basic tests, aborting after first failure.
-
-p
-    |  All commands assume that your #[code virtualenv] is located in a
-    |  directory #[code .env]. If you're using a different directory, you can
-    |  change it via the environment variable #[code VENV_DIR], for example:
-
-+code(false, "bash").
-    VENV_DIR=".custom-env" fab clean make
-
-+h(3, "source-ubuntu") Ubuntu
-
-p Install system-level dependencies via #[code apt-get]:
-
-+code(false, "bash").
-    sudo apt-get install build-essential python-dev git
-
-+h(3, "source-osx") macOS / OS X
-
-p
-    |  Install a recent version of #[+a("https://developer.apple.com/xcode/") XCode],
-    |  including the so-called "Command Line Tools". macOS and OS X ship with
-    |  Python and git preinstalled. To compile spaCy with multi-threading support
-    |  on macOS / OS X, #[+a("https://github.com/explosion/spaCy/issues/267") see here].
-
-+h(3, "source-windows") Windows
-
-p
-    |  Install a version of
-    |  #[+a("https://www.visualstudio.com/vs/visual-studio-express/") Visual Studio Express]
-    |  that matches the version that was used to compile your Python
-    |  interpreter. For official distributions these are:
-
-+table([ "Distribution", "Version"])
-    +row
-        +cell Python 2.7
-        +cell Visual Studio 2008
-
-    +row
-        +cell Python 3.4
-        +cell Visual Studio 2010
-
-    +row
-        +cell Python 3.5+
-        +cell Visual Studio 2015
-
-+h(2, "troubleshooting") Troubleshooting guide
-
-p
-    |  This section collects some of the most common errors you may come
-    |  across when installing, loading and using spaCy, as well as their solutions.
-
-+aside("Help us improve this guide")
-    |  Did you come across a problem like the ones listed here and want to
-    |  share the solution? You can find the "Suggest edits" button at the
-    |  bottom of this page that points you to the source. We always
-    |  appreciate #[+a(gh("spaCy") + "/pulls") pull requests]!
-
-+h(3, "compatible-model") No compatible model found
-
-+code(false, "text").
-    No compatible model found for [lang] (spaCy v#{SPACY_VERSION}).
-
-p
-    |  This usually means that the model you're trying to download does not
-    |  exist, or isn't available for your version of spaCy. Check the
-    |  #[+a(gh("spacy-models", "compatibility.json")) compatibility table]
-    |  to see which models are available for your spaCy version. If you're using
-    |  an old version, consider upgrading to the latest release. Note that while
-    |  spaCy supports tokenization for
-    |  #[+a("/docs/api/language-models/#alpha-support") a variety of languages],
-    |  not all of them come with statistical models. To only use the tokenizer,
-    |  import the language's #[code Language] class instead, for example
-    |  #[code from spacy.fr import French].
-
-+h(3, "symlink-privilege") Symbolic link privilege not held
-
-+code(false, "text").
-    OSError: symbolic link privilege not held
-
-p
-    |  To create #[+a("/docs/usage/models/#usage") shortcut links] that let you
-    |  load models by name, spaCy creates a symbolic link in the
-    |  #[code spacy/data] directory. This means your user needs permission to do
-    |  this. The above error mostly occurs when doing a system-wide installation,
-    |  which will create the symlinks in a system directory. Run the
-    |  #[code download] or #[code link] command as administrator, or use a
-    |  #[code virtualenv] to install spaCy in a user directory, instead
-    |  of doing a system-wide installation.
-
-+h(3, "no-cache-dir") No such option: --no-cache-dir
-
-+code(false, "text").
-    no such option: --no-cache-dir
-
-p
-    |  The #[code download] command uses pip to install the models and sets the
-    |  #[code --no-cache-dir] flag to prevent it from requiring too much memory.
-    |  #[+a("https://pip.pypa.io/en/stable/reference/pip_install/#caching") This setting]
-    |  requires pip v6.0 or newer. Run #[code pip install -U pip] to upgrade to
-    |  the latest version of pip. To see which version you have installed,
-    |  run #[code pip --version].
-
-+h(3, "import-error") Import error
-
-+code(false, "text").
-    Import Error: No module named spacy
-
-p
-    |  This error means that the spaCy module can't be located on your system, or in
-    |  your environment. Make sure you have spaCy installed. If you're using a
-    |  #[code virtualenv], make sure it's activated and check that spaCy is
-    |  installed in that environment – otherwise, you're trying to load a system
-    |  installation. You can also run #[code which python] to find out where
-    |  your Python executable is located.
-
-+h(3, "import-error-models") Import error: models
-
-+code(false, "text").
-    ImportError: No module named 'en_core_web_sm'
-
-p
-    |  As of spaCy v1.7, all models can be installed as Python packages. This means
-    |  that they'll become importable modules of your application. When creating
-    |  #[+a("/docs/usage/models/#usage") shortcut links], spaCy will also try
-    |  to import the model to load its meta data. If this fails, it's usually a
-    |  sign that the package is not installed in the current environment.
-    |  Run #[code pip list] or #[code pip freeze] to check which model packages
-    |  you have installed, and install the
-    |  #[+a("/docs/usage/models#available") correct models] if necessary. If you're
-    |  importing a model manually at the top of a file, make sure to use the name
-    |  of the package, not the shortcut link you've created.
-
-+h(3, "vocab-strings") File not found: vocab/strings.json
-
-+code(false, "text").
-    FileNotFoundError: No such file or directory: [...]/vocab/strings.json
-
-p
-    |  This error may occur when using #[code spacy.load()] to load
-    |  a language model – either because you haven't set up a
-    |  #[+a("/docs/usage/models/#usage") shortcut link] for it, or because it
-    |  doesn't actually exist. Set up a
-    |  #[+a("/docs/usage/models/#usage") shortcut link] for the model
-    |  you want to load. This can either be an installed model package, or a
-    |  local directory containing the model data. If you want to use one of the
-    |  #[+a("/docs/api/language-models/#alpha-support") alpha tokenizers] for
-    |  languages that don't yet have a statistical model, you should import its
-    |  #[code Language] class instead, for example
-    |  #[code from spacy.lang.bn import Bengali].
-
-+h(3, "command-not-found") Command not found
-
-+code(false, "text").
-    command not found: spacy
-
-p
-    |  This error may occur when running the #[code spacy] command from the
-    |  command line. spaCy does not currently add an entry to our #[code PATH]
-    |  environment variable, as this can lead to unexpected results, especially
-    |  when using #[code virtualenv]. Instead, spaCy adds an auto-alias that
-    |  maps #[code spacy] to #[code python -m spacy]. If this is not working as
-    |  expected, run the command with #[code python -m], yourself –
-    |  for example #[code python -m spacy download en]. For more info on this,
-    |  see #[+api("cli#download") download].
-
-+h(3, "module-load") 'module' object has no attribute 'load'
-
-+code(false, "text").
-    AttributeError: 'module' object has no attribute 'load'
-
-p
-    |  While this could technically have many causes, including spaCy being
-    |  broken, the most likely one is that your script's file or directory name
-    |  is "shadowing" the module – e.g. your file is called #[code spacy.py],
-    |  or a directory you're importing from is called #[code spacy]. So, when
-    |  using spaCy, never call anything else #[code spacy].
-
-+h(2, "tests") Run tests
-
-p
-    |  spaCy comes with an #[+a(gh("spacy", "spacy/tests")) extensive test suite].
-    |  First, find out where spaCy is installed:
-
-+code(false, "bash").
-    python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))"
-
-p
-    |  Then run #[code pytest] on that directory. The flags #[code --slow] and
-    |  #[code --model] are optional and enable additional tests.
-
-+code(false, "bash").
-    # make sure you are using recent pytest version
-    python -m pip install -U pytest
-
-    python -m pytest &lt;spacy-directory&gt;                 # basic tests
-    python -m pytest &lt;spacy-directory&gt; --slow          # basic and slow tests
-    python -m pytest &lt;spacy-directory&gt; --models --all  # basic and all model tests
-    python -m pytest &lt;spacy-directory&gt; --models --en   # basic and English model tests
diff --git a/website/docs/usage/production-use.jade b/website/docs/usage/production-use.jade
deleted file mode 100644
index d4a1ffbc2..000000000
--- a/website/docs/usage/production-use.jade
+++ /dev/null
@@ -1,147 +0,0 @@
-//- 💫 DOCS > USAGE > PROCESSING TEXT
-
-include ../../_includes/_mixins
-
-+under-construction
-
-+h(2, "multithreading") Multi-threading with #[code .pipe()]
-
-p
-    |  If you have a sequence of documents to process, you should use the
-    |  #[+api("language#pipe") #[code Language.pipe()]] method. The method takes
-    |  an iterator of texts, and accumulates an internal buffer,
-    |  which it works on in parallel. It then yields the documents in order,
-    |  one-by-one. After a long and bitter struggle, the global interpreter
-    |  lock was freed around spaCy's main parsing loop in v0.100.3. This means
-    |  that #[code .pipe()] will be significantly faster in most
-    |  practical situations, because it allows shared memory parallelism.
-
-+code.
-    for doc in nlp.pipe(texts, batch_size=10000, n_threads=3):
-       pass
-
-p
-    |  To make full use of the #[code .pipe()] function, you might want to
-    |  brush up on #[strong Python generators]. Here are a few quick hints:
-
-+list
-    +item
-        |  Generator comprehensions can be written as
-        |  #[code (item for item in sequence)].
-
-    +item
-        |  The
-        |  #[+a("https://docs.python.org/2/library/itertools.html") #[code itertools] built-in library]
-        |  and the
-        |  #[+a("https://github.com/pytoolz/cytoolz") #[code cytoolz] package]
-        |  provide a lot of handy #[strong generator tools].
-
-    +item
-        |  Often you'll have an input stream that pairs text with some
-        |  important meta data, e.g. a JSON document. To
-        |  #[strong pair up the meta data] with the processed #[code Doc]
-        |  object, you should use the #[code itertools.tee] function to split
-        |  the generator in two, and then #[code izip] the extra stream to the
-        |  document stream.
-
-+h(2, "own-annotations") Bringing your own annotations
-
-p
-    |  spaCy generally assumes by default that your data is raw text. However,
-    |  sometimes your data is partially annotated, e.g. with pre-existing
-    |  tokenization, part-of-speech tags, etc. The most common situation is
-    |  that you have pre-defined tokenization. If you have a list of strings,
-    |  you can create a #[code Doc] object directly. Optionally, you can also
-    |  specify a list of boolean values, indicating whether each word has a
-    |  subsequent space.
-
-+code.
-    doc = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'], spaces=[False, True, False, False])
-
-p
-    |  If provided, the spaces list must be the same length as the words list.
-    |  The spaces list affects the #[code doc.text], #[code span.text],
-    |  #[code token.idx], #[code span.start_char] and #[code span.end_char]
-    |  attributes. If you don't provide a #[code spaces] sequence, spaCy will
-    |  assume that all words are whitespace delimited.
-
-+code.
-    good_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'], spaces=[False, True, False, False])
-    bad_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'])
-    assert bad_spaces.text == u'Hello , world !'
-    assert good_spaces.text == u'Hello, world!'
-
-p
-    |  Once you have a #[+api("doc") #[code Doc]] object, you can write to its
-    |  attributes to set the part-of-speech tags, syntactic dependencies, named
-    |  entities and other attributes. For details, see the respective usage
-    |  pages.
-
-+h(2, "models") Working with models
-
-p
-    |  If your application depends on one or more #[+a("/docs/usage/models") models],
-    |  you'll usually want to integrate them into your continuous integration
-    |  workflow and build process. While spaCy provides a range of useful helpers
-    |  for downloading, linking and loading models, the underlying functionality
-    |  is entirely based on native Python packages. This allows your application
-    |  to handle a model like any other package dependency.
-
-+h(3, "models-download") Downloading and requiring model dependencies
-
-p
-    |  spaCy's built-in #[+api("cli#download") #[code download]] command
-    |  is mostly intended as a convenient, interactive wrapper. It performs
-    |  compatibility checks and prints detailed error messages and warnings.
-    |  However, if you're downloading models as part of an automated build
-    |  process, this only adds an unnecessary layer of complexity. If you know
-    |  which models your application needs, you should be specifying them directly.
-
-p
-    |  Because all models are valid Python packages, you can add them to your
-    |  application's #[code requirements.txt]. If you're running your own
-    |  internal PyPi installation, you can simply upload the models there. pip's
-    |  #[+a("https://pip.pypa.io/en/latest/reference/pip_install/#requirements-file-format") requirements file format]
-    |  supports both package names to download via a PyPi server, as well as direct
-    |  URLs.
-
-+code("requirements.txt", "text").
-    spacy&gt;=2.0.0,&lt;3.0.0
-    -e #{gh("spacy-models")}/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz
-
-p
-    |  All models are versioned and specify their spaCy dependency. This ensures
-    |  cross-compatibility and lets you specify exact version requirements for
-    |  each model. If you've trained your own model, you can use the
-    |  #[+api("cli#package") #[code package]] command to generate the required
-    |  meta data and turn it into a loadable package.
-
-+h(3, "models-loading") Loading and testing models
-
-p
-    |  Downloading models directly via pip won't call spaCy's link
-    |  #[+api("cli#link") #[code link]] command, which creates
-    |  symlinks for model shortcuts. This means that you'll have to run this
-    |  command separately, or use the native #[code import] syntax to load the
-    |  models:
-
-+code.
-    import en_core_web_sm
-    nlp = en_core_web_sm.load()
-
-p
-    |  In general, this approach is recommended for larger code bases, as it's
-    |  more "native", and doesn't depend on symlinks or rely on spaCy's loader
-    |  to resolve string names to model packages. If a model can't be
-    |  imported, Python will raise an #[code ImportError] immediately. And if a
-    |  model is imported but not used, any linter will catch that.
-
-p
-    |  Similarly, it'll give you more flexibility when writing tests that
-    |  require loading models. For example, instead of writing your own
-    |  #[code try] and #[code except] logic around spaCy's loader, you can use
-    |  #[+a("http://pytest.readthedocs.io/en/latest/") pytest]'s
-    |  #[code importorskip()] method to only run a test if a specific model or
-    |  model version is installed. Each model package exposes a #[code __version__]
-    |  attribute which you can also use to perform your own version compatibility
-    |  checks before loading a model.
diff --git a/website/docs/usage/showcase.jade b/website/docs/usage/showcase.jade
deleted file mode 100644
index 66b7e6d86..000000000
--- a/website/docs/usage/showcase.jade
+++ /dev/null
@@ -1,44 +0,0 @@
-//- 💫 DOCS > USAGE > SHOWCASE
-
-include ../../_includes/_mixins
-
-p
-    |  On this page, we'll be featuring demos, libraries and products from
-    |  the spaCy community. Have you done something cool with spaCy?
-    |  #[a(href="mailto:#{EMAIL}") Let us know!]
-
-+h(2, "libraries") Third-party libraries
-
-+list
-    each details, title in libraries
-        +card-item(title, details)
-
-+h(2, "visualizations") Visualizations
-
-+grid
-    each details, name in visualizations
-        - details.image = "/assets/img/showcase/" + details.image
-        +card(name, details)
-
-+h(2, "products") Built with spaCy
-
-+grid
-    each details, name in products
-        - details.image = "/assets/img/showcase/" + details.image
-        +card(name, details)
-
-+h(2, "books") Books
-
-p We're excited to see books featuring spaCy already start to appear.
-
-+list
-    each details, title in books
-        +card-item(title, details)
-
-+h(2, "research") Research systems
-
-p Researchers are using spaCy to build ambitious, next-generation text processing technologies. spaCy is particularly popular amongst the biomedical NLP community, who are working on extracting knowledge from the huge volume of literature in their field. For an up-to-date list of the papers citing spaCy, see #[+a("https://www.semanticscholar.org/search?year%5B%5D=2015&year%5B%5D=2020&q=spacy&sort=relevance&ae=false") Semantic Scholar].
-
-+list
-    each details, title in research
-        +card-item(title, details)
diff --git a/website/docs/usage/spacy-101.jade b/website/docs/usage/spacy-101.jade
deleted file mode 100644
index ac3e808b3..000000000
--- a/website/docs/usage/spacy-101.jade
+++ /dev/null
@@ -1,430 +0,0 @@
-//- 💫 DOCS > USAGE > SPACY 101
-
-include ../../_includes/_mixins
-
-p
-    |  Whether you're new to spaCy, or just want to brush up on some
-    |  NLP basics and implementation details – this page should have you covered.
-    |  Each section will explain one of spaCy's features in simple terms and
-    |  with examples or illustrations. Some sections will also reappear across
-    |  the usage guides as a quick introcution.
-
-+aside("Help us improve the docs")
-    |  Did you spot a mistake or come across explanations that
-    |  are unclear? We always appreciate improvement
-    |  #[+a(gh("spaCy") + "/issues") suggestions] or
-    |  #[+a(gh("spaCy") + "/pulls") pull requests]. You can find a "Suggest
-    |  edits" link at the bottom of each page that points you to the source.
-
-+h(2, "whats-spacy") What's spaCy?
-
-+grid.o-no-block
-    +grid-col("half")
-        p
-            |  spaCy is a #[strong free, open-source library] for advanced
-            |  #[strong Natural Language Processing] (NLP) in Python.
-
-        p
-            |  If you're working with a lot of text, you'll eventually want to
-            |  know more about it. For example, what's it about? What do the
-            |  words mean in context? Who is doing what to whom? What companies
-            |  and products are mentioned? Which texts are similar to each other?
-
-        p
-            |  spaCy is designed specifically for #[strong production use] and
-            |  helps you build applications that process and "understand"
-            |  large volumes of text. It can be used to build
-            |  #[strong information extraction] or
-            |  #[strong natural language understanding] systems, or to
-            |  pre-process text for #[strong deep learning].
-
-    +table-of-contents
-        +item #[+a("#features") Features]
-        +item #[+a("#annotations") Linguistic annotations]
-        +item #[+a("#annotations-token") Tokenization]
-        +item #[+a("#annotations-pos-deps") POS tags and dependencies]
-        +item #[+a("#annotations-ner") Named entities]
-        +item #[+a("#vectors-similarity") Word vectors and similarity]
-        +item #[+a("#pipelines") Pipelines]
-        +item #[+a("#vocab") Vocab, hashes and lexemes]
-        +item #[+a("#serialization") Serialization]
-        +item #[+a("#training") Training]
-        +item #[+a("#language-data") Language data]
-        +item #[+a("#architecture") Architecture]
-        +item #[+a("#community") Community & FAQ]
-
-+h(3, "what-spacy-isnt") What spaCy isn't
-
-+list
-    +item #[strong spaCy is not a platform or "an API"].
-        |  Unlike a platform, spaCy does not provide a software as a service, or
-        |  a web application. It's an open-source library designed to help you
-        |  build NLP applications, not a consumable service.
-    +item #[strong spaCy is not an out-of-the-box chat bot engine].
-        |  While spaCy can be used to power conversational applications, it's
-        |  not designed specifically for chat bots, and only provides the
-        |  underlying text processing capabilities.
-    +item #[strong spaCy is not research software].
-        |  It's built on the latest research, but it's designed to get
-        |  things done. This leads to fairly different design decisions than
-        |  #[+a("https://github./nltk/nltk") NLTK]
-        |  or #[+a("https://stanfordnlp.github.io/CoreNLP/") CoreNLP], which were
-        |  created as platforms for teaching and research. The main difference
-        |  is that spaCy is integrated and opinionated. spaCy tries to avoid asking
-        |  the user to choose between multiple algorithms that deliver equivalent
-        |  functionality. Keeping the menu small lets spaCy deliver generally better
-        |  performance and developer experience.
-    +item #[strong spaCy is not a company].
-        |  It's an open-source library. Our company publishing spaCy and other
-        |  software is called #[+a(COMPANY_URL, true) Explosion AI].
-
-+h(2, "features") Features
-
-p
-    |  In the documentation, you'll come across mentions of spaCy's
-    |  features and capabilities. Some of them refer to linguistic concepts,
-    |  while others are related to more general machine learning functionality.
-
-+aside
-    |  If one of spaCy's functionalities #[strong needs a model], it means that
-    |  you need to have one of the available
-    |  #[+a("/docs/usage/models") statistical models] installed. Models are used
-    |  to #[strong predict] linguistic annotations – for example, if a word is
-    |  a verb or a noun.
-
-+table(["Name", "Description", "Needs model"])
-    +row
-        +cell #[strong Tokenization]
-        +cell Segmenting text into words, punctuations marks etc.
-        +cell #[+procon("con")]
-
-    +row
-        +cell #[strong Part-of-speech] (POS) #[strong Tagging]
-        +cell Assigning word types to tokens, like verb or noun.
-        +cell #[+procon("pro")]
-
-    +row
-        +cell #[strong Dependency Parsing]
-        +cell
-            |  Assigning syntactic dependency labels, describing the relations
-            |  between individual tokens, like subject or object.
-        +cell #[+procon("pro")]
-
-    +row
-        +cell #[strong Lemmatization]
-        +cell
-            |  Assigning the base forms of words. For example, the lemma of
-            |  "was" is "be", and the lemma of "rats" is "rat".
-        +cell #[+procon("pro")]
-
-    +row
-        +cell #[strong Sentence Boundary Detection] (SBD)
-        +cell Finding and segmenting individual sentences.
-        +cell #[+procon("pro")]
-
-    +row
-        +cell #[strong Named Entity Recongition] (NER)
-        +cell
-            |  Labelling named "real-world" objects, like persons, companies or
-            |  locations.
-        +cell #[+procon("pro")]
-
-    +row
-        +cell #[strong Similarity]
-        +cell
-            |  Comparing words, text spans and documents and how similar they
-            |  are to each other.
-        +cell #[+procon("pro")]
-
-    +row
-        +cell #[strong Text classification]
-        +cell Assigning categories or labels to a whole document, or parts of a document.
-        +cell #[+procon("pro")]
-
-    +row
-        +cell #[strong Rule-based Matching]
-        +cell
-            |  Finding sequences of tokens based on their texts and linguistic
-            |  annotations, similar to regular expressions.
-        +cell #[+procon("con")]
-
-    +row
-        +cell #[strong Training]
-        +cell Updating and improving a statistical model's predictions.
-        +cell #[+procon("neutral")]
-
-    +row
-        +cell #[strong Serialization]
-        +cell Saving objects to files or byte strings.
-        +cell #[+procon("neutral")]
-
-+h(2, "annotations") Linguistic annotations
-
-p
-    |  spaCy provides a variety of linguistic annotations to give you
-    |  #[strong insights into a text&apos;s grammatical structure]. This includes the
-    |  word types, like the parts of speech, and how the words are related to
-    |  each other. For example, if you're analysing text, it makes a huge
-    |  difference whether a noun is the subject of a sentence, or the object –
-    |  or whether "google" is used as a verb, or refers to the website or
-    |  company in a specific context.
-
-p
-    |  Once you've downloaded and installed a #[+a("/docs/usage/models") model],
-    |  you can load it via #[+api("spacy#load") #[code spacy.load()]]. This will
-    |  return a #[code Language] object contaning all components and data needed
-    |  to process text. We usually call it #[code nlp]. Calling the #[code nlp]
-    |  object on a string of text will return a processed #[code Doc]:
-
-+code.
-    import spacy
-
-    nlp = spacy.load('en')
-    doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
-
-p
-    |  Even though a #[code Doc] is processed – e.g. split into individual words
-    |  and annotated – it still holds #[strong all information of the original text],
-    |  like whitespace characters. You can always get the offset of a token into the
-    |  original string, or reconstruct the original by joining the tokens and their
-    |  trailing whitespace. This way, you'll never lose any information
-    |  when processing text with spaCy.
-
-+h(3, "annotations-token") Tokenization
-
-include _spacy-101/_tokenization
-
-+infobox
-    |  To learn more about how spaCy's tokenization rules work in detail,
-    |  how to #[strong customise and replace] the default tokenizer and how to
-    |  #[strong add language-specific data], see the usage guides on
-    |  #[+a("/docs/usage/adding-languages") adding languages] and
-    |  #[+a("/docs/usage/customizing-tokenizer") customising the tokenizer].
-
-+h(3, "annotations-pos-deps") Part-of-speech tags and dependencies
-    +tag-model("dependency parse")
-
-include _spacy-101/_pos-deps
-
-+infobox
-    |  To learn more about #[strong part-of-speech tagging] and rule-based
-    |  morphology, and how to #[strong navigate and use the parse tree]
-    |  effectively, see the usage guides on
-    |  #[+a("/docs/usage/pos-tagging") part-of-speech tagging] and
-    |  #[+a("/docs/usage/dependency-parse") using the dependency parse].
-
-+h(3, "annotations-ner") Named Entities
-    +tag-model("named entities")
-
-include _spacy-101/_named-entities
-
-+infobox
-    |  To learn more about entity recognition in spaCy, how to
-    |  #[strong add your own entities] to a document and how to
-    |  #[strong train and update] the entity predictions of a model, see the
-    |  usage guides on
-    |  #[+a("/docs/usage/entity-recognition") named entity recognition] and
-    |  #[+a("/docs/usage/training-ner") training the named entity recognizer].
-
-+h(2, "vectors-similarity") Word vectors and similarity
-    +tag-model("vectors")
-
-include _spacy-101/_similarity
-
-include _spacy-101/_word-vectors
-
-+infobox
-    |  To learn more about word vectors, how to #[strong customise them] and
-    |  how to load #[strong your own vectors] into spaCy, see the usage
-    |  guide on
-    |  #[+a("/docs/usage/word-vectors-similarities") using word vectors and semantic similarities].
-
-+h(2, "pipelines") Pipelines
-
-include _spacy-101/_pipelines
-
-+infobox
-    |  To learn more about #[strong how processing pipelines work] in detail,
-    |  how to enable and disable their components, and how to
-    |  #[strong create your own], see the usage guide on
-    |  #[+a("/docs/usage/language-processing-pipeline") language processing pipelines].
-
-+h(2, "vocab") Vocab, hashes and lexemes
-
-include _spacy-101/_vocab
-
-+h(2, "serialization") Serialization
-
-include _spacy-101/_serialization
-
-+infobox
-    |  To learn more about #[strong serialization] and how to
-    |  #[strong save and load your own models], see the usage guide on
-    |  #[+a("/docs/usage/saving-loading") saving, loading and data serialization].
-
-+h(2, "training") Training
-
-include _spacy-101/_training
-
-+infobox
-    |  To learn more about #[strong training and updating] models, how to create
-    |  training data and how to improve spaCy's named entity recognition models,
-    |  see the usage guides on #[+a("/docs/usage/training") training] and
-    |  #[+a("/docs/usage/training-ner") training the named entity recognizer].
-
-+h(2, "language-data") Language data
-
-include _spacy-101/_language-data
-
-+infobox
-    |  To learn more about the individual components of the language data and
-    |  how to #[strong add a new language] to spaCy in preparation for training
-    |  a language model, see the usage guide on
-    |  #[+a("/docs/usage/adding-languages") adding languages].
-
-+h(2, "architecture") Architecture
-
-include _spacy-101/_architecture.jade
-
-+h(2, "community") Community & FAQ
-
-p
-    |  We're very happy to see the spaCy community grow and include a mix of
-    |  people from all kinds of different backgrounds – computational
-    |  linguistics, data science, deep learning, research and more. If you'd
-    |  like to get involved, below are some answers to the most important
-    |  questions and resources for further reading.
-
-+h(3, "faq-help-code") Help, my code isn't working!
-
-p
-    |  Bugs suck, and we're doing our best to continuously improve the tests
-    |  and fix bugs as soon as possible. Before you submit an issue, do a
-    |  quick search and check if the problem has already been reported. If
-    |  you're having installation or loading problems, make sure to also check
-    |  out the #[+a("/docs/usage#troubleshooting") troubleshooting guide]. Help
-    |  with spaCy is available via the following platforms:
-
-+aside("How do I know if something is a bug?")
-    |  Of course, it's always hard to know for sure, so don't worry – we're not
-    |  going to be mad if a bug report turns out to be a typo in your
-    |  code. As a simple rule, any C-level error without a Python traceback,
-    |  like a #[strong segmentation fault] or #[strong memory error],
-    |  is #[strong always] a spaCy bug.#[br]#[br]
-
-    |  Because models are statistical, their performance will never be
-    |  #[em perfect]. However, if you come across
-    |  #[strong patterns that might indicate an underlying issue], please do
-    |  file a report. Similarly, we also care about behaviours that
-    |  #[strong contradict our docs].
-
-+table(["Platform", "Purpose"])
-    +row
-        +cell #[+a("https://stackoverflow.com/questions/tagged/spacy") StackOverflow]
-        +cell
-            |  #[strong Usage questions] and everything related to problems with
-            |  your specific code. The StackOverflow community is much larger
-            |  than ours, so if your problem can be solved by others, you'll
-            |  receive help much quicker.
-
-    +row
-        +cell #[+a("https://gitter.im/" + SOCIAL.gitter) Gitter chat]
-        +cell
-            |  #[strong General discussion] about spaCy, meeting other community
-            |  members and exchanging #[strong tips, tricks and best practices].
-            |  If we're working on experimental models and features, we usually
-            |  share them on Gitter first.
-
-    +row
-        +cell #[+a(gh("spaCy") + "/issues") GitHub issue tracker]
-        +cell
-            |  #[strong Bug reports] and #[strong improvement suggestions], i.e.
-            |  everything that's likely spaCy's fault. This also includes
-            |  problems with the models beyond statistical imprecisions, like
-            |  patterns that point to a bug.
-
-+infobox
-    |  Please understand that we won't be able to provide individual support via
-    |  email. We also believe that help is much more valuable if it's shared
-    |  publicly, so that #[strong more people can benefit from it]. If you come
-    |  across an issue and you think you might be able to help, consider posting
-    |  a quick update with your solution. No matter how simple, it can easily
-    |  save someone a lot of time and headache – and the next time you need help,
-    |  they might repay the favour.
-
-+h(3, "faq-contributing") How can I contribute to spaCy?
-
-p
-    |  You don't have to be an NLP expert or Python pro to contribute, and we're
-    |  happy to help you get started. If you're new to spaCy, a good place to
-    |  start is the
-    |  #[+a(gh("spaCy") + '/issues?q=is%3Aissue+is%3Aopen+label%3A"help+wanted+%28easy%29"') #[code help wanted (easy)] label]
-    |  on GitHub, which we use to tag bugs and feature requests that are easy
-    |  and self-contained. We also appreciate contributions to the docs – whether
-    |  it's fixing a typo, improving an example or adding additional explanations.
-    |  You'll find a "Suggest edits" link at the bottom of each page that points
-    |  you to the source.
-
-p
-    |  Another way of getting involved is to help us improve the
-    |  #[+a("/docs/usage/adding-languages#language-data") language data] –
-    |  especially if you happen to speak one of the languages currently in
-    |  #[+a("/docs/api/language-models#alpha-support") alpha support]. Even
-    |  adding simple tokenizer exceptions, stop words or lemmatizer data
-    |  can make a big difference. It will also make it easier for us to provide
-    |  a statistical model for the language in the future. Submitting a test
-    |  that documents a bug or performance issue, or covers functionality that's
-    |  especially important for your application is also very helpful. This way,
-    |  you'll also make sure we never accidentally introduce regressions to the
-    |  parts of the library that you care about the most.
-
-p
-    strong
-        |  For more details on the types of contributions we're looking for, the
-        |  code conventions and other useful tips, make sure to check out the
-        |  #[+a(gh("spaCy", "CONTRIBUTING.md")) contributing guidelines].
-
-+infobox("Code of Conduct")
-    |  spaCy adheres to the
-    |  #[+a("http://contributor-covenant.org/version/1/4/") Contributor Covenant Code of Conduct].
-    |  By participating, you are expected to uphold this code.
-
-+h(3, "faq-project-with-spacy")
-    |  I've built something cool with spaCy – how can I get the word out?
-
-p
-    |  First, congrats – we'd love to check it out! When you share your
-    |  project on Twitter, don't forget to tag
-    |  #[+a("https://twitter.com/" + SOCIAL.twitter) @#{SOCIAL.twitter}] so we
-    |  don't miss it. If you think your project would be a good fit for the
-    |  #[+a("/docs/usage/showcase") showcase], #[strong feel free to submit it!]
-    |  Tutorials are also incredibly valuable to other users and a great way to
-    |  get exposure. So we strongly encourage #[strong writing up your experiences],
-    |  or sharing your code and some tips and tricks on your blog. Since our
-    |  website is open-source, you can add your project or tutorial by making a
-    |  pull request on GitHub.
-
-+aside("Contributing to spacy.io")
-    |  All showcase and tutorial links are stored in a
-    |  #[+a(gh("spaCy", "website/docs/usage/_data.json")) JSON file], so you
-    |  won't even have to edit any markup. For more info on how to submit
-    |  your project, see the
-    |  #[+a(gh("spaCy", "CONTRIBUTING.md#submitting-a-project-to-the-showcase")) contributing guidelines]
-    |  and our #[+a(gh("spaCy", "website")) website docs].
-
-p
-    |  If you would like to use the spaCy logo on your site, please get in touch
-    |  and ask us first. However, if you want to show support and tell others
-    |  that your project is using spaCy, you can grab one of our
-    |  #[strong spaCy badges] here:
-
-- SPACY_BADGES =  ["built%20with-spaCy-09a3d5.svg", "made%20with%20❤%20and-spaCy-09a3d5.svg", "spaCy-v2-09a3d5.svg"]
-+quickstart([{id: "badge", input_style: "check", options: SPACY_BADGES.map(function(badge, i) { return {id: i, title: "<img class='o-icon' src='https://img.shields.io/badge/" + badge + "' height='20'/>", checked: (i == 0) ? true : false}}) }], false, false, true)
-    .c-code-block(data-qs-results)
-        for badge, i in SPACY_BADGES
-            - var url = "https://img.shields.io/badge/" + badge
-            +code(false, "text", "star").o-no-block(data-qs-badge=i)=url
-            +code(false, "text", "code").o-no-block(data-qs-badge=i).
-                &lt;a href="#{SITE_URL}"&gt;&lt;img src="#{url}" height="20"&gt;&lt;/a&gt;
-            +code(false, "text", "markdown").o-no-block(data-qs-badge=i).
-                [![spaCy](#{url})](#{SITE_URL})
diff --git a/website/docs/usage/text-classification.jade b/website/docs/usage/text-classification.jade
deleted file mode 100644
index 33e384dbd..000000000
--- a/website/docs/usage/text-classification.jade
+++ /dev/null
@@ -1,5 +0,0 @@
-//- 💫 DOCS > USAGE > TEXT CLASSIFICATION
-
-include ../../_includes/_mixins
-
-+under-construction
diff --git a/website/docs/usage/training-ner.jade b/website/docs/usage/training-ner.jade
deleted file mode 100644
index 3c74f7a9d..000000000
--- a/website/docs/usage/training-ner.jade
+++ /dev/null
@@ -1,114 +0,0 @@
-include ../../_includes/_mixins
-
-p
-    |  All #[+a("/docs/usage/models") spaCy models] support online learning, so
-    |  you can update a pre-trained model with new examples. You can even add
-    |  new classes to an existing model, to recognise a new entity type,
-    |  part-of-speech, or syntactic relation. Updating an existing model is
-    |  particularly useful as a "quick and dirty solution", if you have only a
-    |  few corrections or annotations.
-
-+h(2, "improving-accuracy") Improving accuracy on existing entity types
-
-p
-    |  To update the model, you first need to create an instance of
-    |  #[+api("goldparse") #[code GoldParse]], with the entity labels
-    |  you want to learn. You'll usually need to provide many examples to
-    |  meaningfully improve the system — a few hundred is a good start, although
-    |  more is better.
-
-+image
-    include ../../assets/img/docs/training-loop.svg
-    .u-text-right
-        +button("/assets/img/docs/training-loop.svg", false, "secondary").u-text-tag View large graphic
-
-p
-    |  You should avoid iterating over the same few examples multiple times, or
-    |  the model is likely to "forget" how to annotate other examples. If you
-    |  iterate over the same few examples, you're effectively changing the loss
-    |  function. The optimizer will find a way to minimize the loss on your
-    |  examples, without regard for the consequences on the examples it's no
-    |  longer paying attention to.
-
-p
-    |  One way to avoid this "catastrophic forgetting" problem is to "remind"
-    |  the model of other examples by augmenting your annotations with sentences
-    |  annotated with entities automatically recognised by the original model.
-    |  Ultimately, this is an empirical process: you'll need to
-    |  #[strong experiment on your own data] to find a solution that works best
-    |  for you.
-
-+h(2, "example") Example
-
-+under-construction
-
-+code.
-    import random
-    from spacy.lang.en import English
-    from spacy.gold import GoldParse, biluo_tags_from_offsets
-
-    def main(model_dir=None):
-        train_data = [
-            ('Who is Shaka Khan?',
-                [(len('Who is '), len('Who is Shaka Khan'), 'PERSON')]),
-            ('I like London and Berlin.',
-                [(len('I like '), len('I like London'), 'LOC'),
-                (len('I like London and '), len('I like London and Berlin'), 'LOC')])
-        ]
-        nlp = English(pipeline=['tensorizer', 'ner'])
-        get_data = lambda: reformat_train_data(nlp.tokenizer, train_data)
-        optimizer = nlp.begin_training(get_data)
-        for itn in range(100):
-            random.shuffle(train_data)
-            losses = {}
-            for raw_text, entity_offsets in train_data:
-                doc = nlp.make_doc(raw_text)
-                gold = GoldParse(doc, entities=entity_offsets)
-                nlp.update([doc], [gold], drop=0.5, sgd=optimizer, losses=losses)
-        nlp.to_disk(model_dir)
-
-+code.
-    def reformat_train_data(tokenizer, examples):
-        """Reformat data to match JSON format"""
-        output = []
-        for i, (text, entity_offsets) in enumerate(examples):
-            doc = tokenizer(text)
-            ner_tags = biluo_tags_from_offsets(tokenizer(text), entity_offsets)
-            words = [w.text for w in doc]
-            tags = ['-'] * len(doc)
-            heads = [0] * len(doc)
-            deps = [''] * len(doc)
-            sentence = (range(len(doc)), words, tags, heads, deps, ner_tags)
-            output.append((text, [(sentence, [])]))
-        return output
-
-p.u-text-right
-    +button(gh("spaCy", "examples/training/train_ner.py"), false, "secondary").u-text-tag View full example
-
-+h(2, "saving-loading") Saving and loading
-
-p
-    |  After training our model, you'll usually want to save its state, and load
-    |  it back later. You can do this with the
-    |  #[+api("language#to_disk") #[code Language.to_disk()]] method:
-
-+code.
-    nlp.to_disk('/home/me/data/en_technology')
-
-p
-    |  To make the model more convenient to deploy, we recommend wrapping it as
-    |  a Python package, so that you can install it via pip and load it as a
-    |  module. spaCy comes with a handy #[+api("cli#package") #[code package]]
-    |  CLI command to create all required files and directories.
-
-+code(false, "bash").
-    spacy package /home/me/data/en_technology /home/me/my_models
-
-p
-    |  To build the package and create a #[code .tar.gz] archive, run
-    |  #[code python setup.py sdist] from within its directory.
-
-+infobox("Saving and loading models")
-    |  For more information and a detailed guide on how to package your model,
-    |  see the documentation on
-    |  #[+a("/docs/usage/saving-loading#models") saving and loading models].
diff --git a/website/docs/usage/tutorials.jade b/website/docs/usage/tutorials.jade
deleted file mode 100644
index 2b8eddbf1..000000000
--- a/website/docs/usage/tutorials.jade
+++ /dev/null
@@ -1,38 +0,0 @@
-//- 💫 DOCS > USAGE > TUTORIALS
-
-include ../../_includes/_mixins
-
-p
-    |  Have you written a tutorial on spaCy, or did you find one that should be
-    |  featured here? #[a(href="mailto:#{EMAIL}") Let us know!]
-
-+h(2, "first-steps") First steps
-
-p
-    |  These tutorials help you get started. They describe how to set up your
-    |  environment and start using spaCy.
-
-+grid
-    each details, title in first_steps
-        +card(title, details)
-
-+h(2, "features") Deep dives
-
-p
-    |  These tutorials take a closer look at particular features of spaCy, or
-    |  particular types of NLP problems. Most come with more explanatory text,
-    |  to help introduce you to new concepts.
-
-+grid
-    each details, title in deep_dives
-        +card(title, details)
-
-+h(2, "code") Programs and scripts
-
-p
-    |  These tutorials give you all the code and nothing but the code — they're
-    |  Python scripts you can modify and run.
-
-+grid
-    each details, title in code
-        +card(title, details)
diff --git a/website/docs/usage/v2.jade b/website/docs/usage/v2.jade
deleted file mode 100644
index 6d98e3f05..000000000
--- a/website/docs/usage/v2.jade
+++ /dev/null
@@ -1,531 +0,0 @@
-//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0
-
-include ../../_includes/_mixins
-
-p
-    |  We're very excited to finally introduce spaCy v2.0! On this page, you'll
-    |  find a summary of the new features, information on the backwards
-    |  incompatibilities, including a handy overview of what's been renamed or
-    |  deprecated. To help you make the most of v2.0, we also
-    |  #[strong re-wrote almost all of the usage guides and API docs], and added
-    |  more real-world examples. If you're new to spaCy, or just want to brush
-    |  up on some NLP basics and the details of the library, check out
-    |  the #[+a("/docs/usage/spacy-101") spaCy 101 guide] that explains the most
-    |  important concepts with examples and illustrations.
-
-+h(2, "summary") Summary
-
-+grid.o-no-block
-    +grid-col("half")
-
-        p This release features
-            |  entirely new #[strong deep learning-powered models] for spaCy's tagger,
-            |  parser and entity recognizer. The new models are #[strong 20x smaller]
-            |  than the linear models that have powered spaCy until now: from 300 MB to
-            |  only 15 MB.
-
-        p
-            |  We've also made several usability improvements that are
-            |  particularly helpful for #[strong production deployments]. spaCy
-            |  v2 now fully supports the Pickle protocol, making it easy to use
-            |  spaCy with #[+a("https://spark.apache.org/") Apache Spark]. The
-            |  string-to-integer mapping is #[strong no longer stateful], making
-            |  it easy to reconcile annotations made in different processes.
-            |  Models are smaller and use less memory, and the APIs for serialization
-            |  are now much more consistent.
-
-    +table-of-contents
-        +item #[+a("#summary") Summary]
-        +item #[+a("#features") New features]
-        +item #[+a("#features-pipelines") Improved processing pipelines]
-        +item #[+a("#features-text-classification") Text classification]
-        +item #[+a("#features-hash-ids") Hash values instead of integer IDs]
-        +item #[+a("#features-serializer") Saving, loading and serialization]
-        +item #[+a("#features-displacy") displaCy visualizer]
-        +item #[+a("#features-language") Language data and lazy loading]
-        +item #[+a("#features-matcher") Revised matcher API]
-        +item #[+a("#features-models") Neural network models]
-        +item #[+a("#incompat") Backwards incompatibilities]
-        +item #[+a("#migrating") Migrating from spaCy v1.x]
-        +item #[+a("#benchmarks") Benchmarks]
-
-p
-    |  The main usability improvements you'll notice in spaCy v2.0 are around
-    |  #[strong defining, training and loading your own models] and components.
-    |  The new neural network models make it much easier to train a model from
-    |  scratch, or update an existing model with a few examples. In v1.x, the
-    |  statistical models depended on the state of the #[code Vocab]. If you
-    |  taught the model a new word, you would have to save and load a lot of
-    |  data — otherwise the model wouldn't correctly recall the features of your
-    |  new example. That's no longer the case.
-
-p
-    |  Due to some clever use of hashing, the statistical models
-    |  #[strong never change size], even as they learn new vocabulary items.
-    |  The whole pipeline is also now fully differentiable. Even if you don't
-    |  have explicitly annotated data, you can update spaCy using all the
-    |  #[strong latest deep learning tricks] like adversarial training, noise
-    |  contrastive estimation or reinforcement learning.
-
-+h(2, "features") New features
-
-p
-    |  This section contains an overview of the most important
-    |  #[strong new features and improvements]. The #[+a("/docs/api") API docs]
-    |  include additional  deprecation notes. New methods and functions that
-    |  were introduced in this version are marked with a #[+tag-new(2)] tag.
-
-+h(3, "features-pipelines") Improved processing pipelines
-
-+aside-code("Example").
-    # Modify an existing pipeline
-    nlp = spacy.load('en')
-    nlp.pipeline.append(my_component)
-
-    # Register a factory to create a component
-    spacy.set_factory('my_factory', my_factory)
-    nlp = Language(pipeline=['my_factory', mycomponent])
-
-p
-    |  It's now much easier to #[strong customise the pipeline] with your own
-    |  components, functions that receive a #[code Doc] object, modify and
-    |  return it. If your component is stateful, you can define and register a
-    |  factory which receives the shared #[code Vocab] object and returns a
-    |  component. spaCy's default components can be added to your pipeline by
-    |  using their string IDs. This way, you won't have to worry about finding
-    |  and implementing them – simply add #[code "tagger"] to the pipeline,
-    |  and spaCy will know what to do.
-
-+image
-    include ../../assets/img/docs/pipeline.svg
-
-+infobox
-    |  #[strong API:] #[+api("language") #[code Language]]
-    |  #[strong Usage:] #[+a("/docs/usage/language-processing-pipeline") Processing text]
-
-+h(3, "features-text-classification") Text classification
-
-+aside-code("Example").
-    from spacy.lang.en import English
-    nlp = English(pipeline=['tensorizer', 'tagger', 'textcat'])
-
-p
-    |  spaCy v2.0 lets you add text categorization models to spaCy pipelines.
-    |  The model supports classification with multiple, non-mutually exclusive
-    |  labels – so multiple labels can apply at once. You can change the model
-    |  architecture rather easily, but by default, the #[code TextCategorizer]
-    |  class uses a convolutional neural network to assign position-sensitive
-    |  vectors to each word in the document.
-
-+infobox
-    |  #[strong API:] #[+api("textcategorizer") #[code TextCategorizer]],
-    |  #[+api("doc#attributes") #[code Doc.cats]],
-    |  #[+api("goldparse#attributes") #[code GoldParse.cats]]#[br]
-    |  #[strong Usage:] #[+a("/docs/usage/text-classification") Text classification]
-
-+h(3, "features-hash-ids") Hash values instead of integer IDs
-
-+aside-code("Example").
-    doc = nlp(u'I love coffee')
-    assert doc.vocab.strings[u'coffee'] == 3197928453018144401
-    assert doc.vocab.strings[3197928453018144401] == u'coffee'
-
-    beer_hash = doc.vocab.strings.add(u'beer')
-    assert doc.vocab.strings[u'beer'] == beer_hash
-    assert doc.vocab.strings[beer_hash] == u'beer'
-
-p
-    |  The #[+api("stringstore") #[code StringStore]] now resolves all strings
-    |  to hash values instead of integer IDs. This means that the string-to-int
-    |  mapping #[strong no longer depends on the vocabulary state], making a lot
-    |  of workflows much simpler, especially during training. Unlike integer IDs
-    |  in spaCy v1.x, hash values will #[strong always match] – even across
-    |  models. Strings can now be added explicitly using the new
-    |  #[+api("stringstore#add") #[code Stringstore.add]] method. A token's hash
-    |  is available via #[code token.orth].
-
-+infobox
-    |  #[strong API:] #[+api("stringstore") #[code StringStore]]
-    |  #[strong Usage:] #[+a("/docs/usage/spacy-101#vocab") Vocab, hashes and lexemes 101]
-
-+h(3, "features-serializer") Saving, loading and serialization
-
-+aside-code("Example").
-    nlp = spacy.load('en') # shortcut link
-    nlp = spacy.load('en_core_web_sm') # package
-    nlp = spacy.load('/path/to/en') # unicode path
-    nlp = spacy.load(Path('/path/to/en')) # pathlib Path
-
-    nlp.to_disk('/path/to/nlp')
-    nlp = English().from_disk('/path/to/nlp')
-
-p
-    |  spay's serialization API has been made consistent across classes and
-    |  objects. All container classes, i.e. #[code Language], #[code Doc],
-    |  #[code Vocab] and #[code StringStore] now have a #[code to_bytes()],
-    |  #[code from_bytes()], #[code to_disk()] and #[code from_disk()] method
-    |  that supports the Pickle protocol.
-
-p
-    |  The improved #[code spacy.load] makes loading models easier and more
-    |  transparent. You can load a model by supplying its
-    |  #[+a("/docs/usage/models#usage") shortcut link], the name of an installed
-    |  #[+a("/docs/usage/saving-loading#generating") model package] or a path.
-    |  The #[code Language] class to initialise will be determined based on the
-    |  model's settings. For a blank language, you can import the class directly,
-    |  e.g. #[code from spacy.lang.en import English].
-
-+infobox
-    |  #[strong API:] #[+api("spacy#load") #[code spacy.load]], #[+api("binder") #[code Binder]]
-    |  #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
-
-+h(3, "features-displacy") displaCy visualizer with Jupyter support
-
-+aside-code("Example").
-    from spacy import displacy
-    doc = nlp(u'This is a sentence about Facebook.')
-    displacy.serve(doc, style='dep') # run the web server
-    html = displacy.render(doc, style='ent') # generate HTML
-
-p
-    |  Our popular dependency and named entity visualizers are now an official
-    |  part of the spaCy library! displaCy can run a simple web server, or
-    |  generate raw HTML markup or SVG files to be exported. You can pass in one
-    |  or more docs, and customise the style. displaCy also auto-detects whether
-    |  you're running #[+a("https://jupyter.org") Jupyter] and will render the
-    |  visualizations in your notebook.
-
-+infobox
-    |  #[strong API:] #[+api("displacy") #[code displacy]]
-    |  #[strong Usage:] #[+a("/docs/usage/visualizers") Visualizing spaCy]
-
-+h(3, "features-language") Improved language data and lazy loading
-
-p
-    |  Language-specfic data now lives in its own submodule, #[code spacy.lang].
-    |  Languages are lazy-loaded, i.e. only loaded when you import a
-    |  #[code Language] class, or load a model that initialises one. This allows
-    |  languages to contain more custom data, e.g. lemmatizer lookup tables, or
-    |  complex regular expressions. The language data has also been tidied up
-    |  and simplified. spaCy now also supports simple lookup-based lemmatization.
-
-+infobox
-    |  #[strong API:] #[+api("language") #[code Language]]
-    |  #[strong Code:] #[+src(gh("spaCy", "spacy/lang")) spacy/lang]
-    |  #[strong Usage:] #[+a("/docs/usage/adding-languages") Adding languages]
-
-+h(3, "features-matcher") Revised matcher API
-
-+aside-code("Example").
-    from spacy.matcher import Matcher
-    matcher = Matcher(nlp.vocab)
-    matcher.add('HEARTS', None, [{'ORTH': '❤️', 'OP': '+'}])
-    assert len(matcher) == 1
-    assert 'HEARTS' in matcher
-
-p
-    |  Patterns can now be added to the matcher by calling
-    |  #[+api("matcher-add") #[code matcher.add()]] with a match ID, an optional
-    |  callback function to be invoked on each match, and one or more patterns.
-    |  This allows you to write powerful, pattern-specific logic using only one
-    |  matcher. For example, you might only want to merge some entity types,
-    |  and set custom flags for other matched patterns.
-
-+infobox
-    |  #[strong API:] #[+api("matcher") #[code Matcher]]
-    |  #[strong Usage:] #[+a("/docs/usage/rule-based-matching") Rule-based matching]
-
-+h(3, "features-models") Neural network models for English, German, French, Spanish and multi-language NER
-
-+aside-code("Example", "bash").
-    spacy download en # default English model
-    spacy download de # default German model
-    spacy download fr # default French model
-    spacy download es # default Spanish model
-    spacy download xx_ent_wiki_sm # multi-language NER
-
-p
-    |  spaCy v2.0 comes with new and improved neural network models for English,
-    |  German, French and Spanish, as well as a multi-language named entity
-    |  recognition model trained on Wikipedia. #[strong GPU usage] is now
-    |  supported via #[+a("http://chainer.org") Chainer]'s CuPy module.
-
-+infobox
-    |  #[strong Details:] #[+a("/docs/api/language-models") Languages],
-    |  #[+src(gh("spacy-models")) spacy-models]
-    |  #[strong Usage:] #[+a("/docs/usage/models") Models],
-    |  #[+a("/docs/usage#gpu") Using spaCy with GPU]
-
-+h(2, "incompat") Backwards incompatibilities
-
-+table(["Old", "New"])
-    +row
-        +cell
-            |  #[code spacy.en]
-            |  #[code spacy.xx]
-        +cell
-            |  #[code spacy.lang.en]
-            |  #[code spacy.lang.xx]
-
-    +row
-        +cell #[code orth]
-        +cell #[code lang.xx.lex_attrs]
-
-    +row
-        +cell #[code syntax.iterators]
-        +cell #[code lang.xx.syntax_iterators]
-
-    +row
-        +cell #[code Language.save_to_directory]
-        +cell #[+api("language#to_disk") #[code Language.to_disk]]
-
-    +row
-        +cell #[code Language.create_make_doc]
-        +cell #[+api("language#attributes") #[code Language.tokenizer]]
-
-    +row
-        +cell
-            |  #[code Vocab.load]
-            |  #[code Vocab.load_lexemes]
-        +cell
-            |  #[+api("vocab#from_disk") #[code Vocab.from_disk]]
-            |  #[+api("vocab#from_bytes") #[code Vocab.from_bytes]]
-
-    +row
-        +cell
-            |  #[code Vocab.dump]
-        +cell
-            |  #[+api("vocab#to_disk") #[code Vocab.to_disk]]#[br]
-            |  #[+api("vocab#to_bytes") #[code Vocab.to_bytes]]
-
-    +row
-        +cell
-            |  #[code Vocab.load_vectors]
-            |  #[code Vocab.load_vectors_from_bin_loc]
-        +cell
-            |  #[+api("vectors#from_disk") #[code Vectors.from_disk]]
-            |  #[+api("vectors#from_bytes") #[code Vectors.from_bytes]]
-
-    +row
-        +cell
-            |  #[code Vocab.dump_vectors]
-        +cell
-            |  #[+api("vectors#to_disk") #[code Vectors.to_disk]]
-            |  #[+api("vectors#to_bytes") #[code Vectors.to_bytes]]
-
-    +row
-        +cell
-            |  #[code StringStore.load]
-        +cell
-            |  #[+api("stringstore#from_disk") #[code StringStore.from_disk]]
-            |  #[+api("stringstore#from_bytes") #[code StringStore.from_bytes]]
-
-    +row
-        +cell
-            |  #[code StringStore.dump]
-        +cell
-            |  #[+api("stringstore#to_disk") #[code StringStore.to_disk]]
-            |  #[+api("stringstore#to_bytes") #[code StringStore.to_bytes]]
-
-    +row
-        +cell #[code Tokenizer.load]
-        +cell
-            |  #[+api("tokenizer#from_disk") #[code Tokenizer.from_disk]]
-            |  #[+api("tokenizer#from_bytes") #[code Tokenizer.from_bytes]]
-
-    +row
-        +cell #[code Tagger.load]
-        +cell
-            |  #[+api("tagger#from_disk") #[code Tagger.from_disk]]
-            |  #[+api("tagger#from_bytes") #[code Tagger.from_bytes]]
-
-    +row
-        +cell #[code DependencyParser.load]
-        +cell
-            |  #[+api("dependencyparser#from_disk") #[code DependencyParser.from_disk]]
-            |  #[+api("dependencyparser#from_bytes") #[code DependencyParser.from_bytes]]
-
-    +row
-        +cell #[code EntityRecognizer.load]
-        +cell
-            |  #[+api("entityrecognizer#from_disk") #[code EntityRecognizer.from_disk]]
-            |  #[+api("entityrecognizer#from_bytes") #[code EntityRecognizer.from_bytes]]
-
-    +row
-        +cell #[code Matcher.load]
-        +cell -
-
-    +row
-        +cell
-            |  #[code Matcher.add_pattern]
-            |  #[code Matcher.add_entity]
-        +cell #[+api("matcher#add") #[code Matcher.add]]
-
-    +row
-        +cell #[code Matcher.get_entity]
-        +cell #[+api("matcher#get") #[code Matcher.get]]
-
-    +row
-        +cell #[code Matcher.has_entity]
-        +cell #[+api("matcher#contains") #[code Matcher.__contains__]]
-
-    +row
-        +cell #[code Doc.read_bytes]
-        +cell #[+api("binder") #[code Binder]]
-
-    +row
-        +cell #[code Token.is_ancestor_of]
-        +cell #[+api("token#is_ancestor") #[code Token.is_ancestor]]
-
-    +row
-        +cell #[code cli.model]
-        +cell -
-
-+h(2, "migrating") Migrating from spaCy 1.x
-
-p
-    |  Because we'e made so many architectural changes to the library, we've
-    |  tried to #[strong keep breaking changes to a minimum]. A lot of projects
-    |  follow the philosophy that if you're going to break anything, you may as
-    |  well break everything. We think migration is easier if there's a logic to
-    |  what has changed.
-
-p
-    |  We've therefore followed a policy of avoiding breaking changes to the
-    |  #[code Doc], #[code Span] and #[code Token] objects. This way, you can
-    |  focus on only migrating the code that does training, loading and
-    |  serialization — in other words, code that works with the #[code nlp]
-    |  object directly. Code that uses the annotations should continue to work.
-
-+infobox("Important note")
-    |  If you've trained your own models, keep in mind that your train and
-    |  runtime inputs must match. This means you'll have to
-    |  #[strong retrain your models] with spaCy v2.0.
-
-+h(3, "migrating-saving-loading") Saving, loading and serialization
-
-p
-    |  Double-check all calls to #[code spacy.load()] and make sure they don't
-    |  use the #[code path] keyword argument. If you're only loading in binary
-    |  data and not a model package that can construct its own #[code Language]
-    |  class and pipeline, you should now use the
-    |  #[+api("language#from_disk") #[code Language.from_disk()]] method.
-
-+code-new.
-    nlp = spacy.load('/model')
-    nlp = English().from_disk('/model/data')
-+code-old nlp = spacy.load('en', path='/model')
-
-p
-    |  Review all other code that writes state to disk or bytes.
-    |  All containers, now share the same, consistent API for saving and
-    |  loading. Replace saving with #[code to_disk()] or #[code to_bytes()], and
-    |  loading with #[code from_disk()] and #[code from_bytes()].
-
-+code-new.
-    nlp.to_disk('/model')
-    nlp.vocab.to_disk('/vocab')
-
-+code-old.
-    nlp.save_to_directory('/model')
-    nlp.vocab.dump('/vocab')
-
-p
-    |  If you've trained models with input from v1.x, you'll need to
-    |  #[strong retrain them] with spaCy v2.0. All previous models will not
-    |  be compatible with the new version.
-
-+h(3, "migrating-strings") Strings and hash values
-
-p
-    |  The change from integer IDs to hash values may not actually affect your
-    |  code very much. However, if you're adding strings to the vocab manually,
-    |  you now need to call #[+api("stringstore#add") #[code StringStore.add()]]
-    |  explicitly. You can also now be sure that the string-to-hash mapping will
-    |  always match across vocabularies.
-
-+code-new.
-    nlp.vocab.strings.add(u'coffee')
-    nlp.vocab.strings[u'coffee']       # 3197928453018144401
-    other_nlp.vocab.strings[u'coffee'] # 3197928453018144401
-
-+code-old.
-    nlp.vocab.strings[u'coffee']       # 3672
-    other_nlp.vocab.strings[u'coffee'] # 40259
-
-+h(3, "migrating-languages") Processing pipelines and language data
-
-p
-    |  If you're importing language data or #[code Language] classes, make sure
-    |  to change your import statements to import from #[code spacy.lang]. If
-    |  you've added your own custom language, it needs to be moved to
-    |  #[code spacy/lang/xx] and adjusted accordingly.
-
-+code-new from spacy.lang.en import English
-+code-old from spacy.en import English
-
-p
-    |  If you've been using custom pipeline components, check out the new
-    |  guide on #[+a("/docs/usage/language-processing-pipelines") processing pipelines].
-    |  Appending functions to the pipeline still works – but you might be able
-    |  to make this more convenient by registering "component factories".
-    |  Components of the processing pipeline can now be disabled by passing a
-    |  list of their names to the #[code disable] keyword argument on loading
-    |  or processing.
-
-+code-new.
-    nlp = spacy.load('en', disable=['tagger', 'ner'])
-    doc = nlp(u"I don't want parsed", disable=['parser'])
-+code-old.
-    nlp = spacy.load('en', tagger=False, entity=False)
-    doc = nlp(u"I don't want parsed", parse=False)
-
-+h(3, "migrating-matcher") Adding patterns and callbacks to the matcher
-
-p
-    |  If you're using the matcher, you can now add patterns in one step. This
-    |  should be easy to update – simply merge the ID, callback and patterns
-    |  into one call to #[+api("matcher#add") #[code Matcher.add()]].
-
-+code-new.
-    matcher.add('GoogleNow', merge_phrases, [{ORTH: 'Google'}, {ORTH: 'Now'}])
-
-+code-old.
-    matcher.add_entity('GoogleNow', on_match=merge_phrases)
-    matcher.add_pattern('GoogleNow', [{ORTH: 'Google'}, {ORTH: 'Now'}])
-
-p
-    |  If you've been using #[strong acceptor functions], you'll need to move
-    |  this logic into the
-    |  #[+a("/docs/usage/rule-based-matching#on_match") #[code on_match] callbacks].
-    |  The callback function is invoked on every match and will give you access to
-    |  the doc, the index of the current match and all total matches. This lets
-    |  you both accept or reject the match, and define the actions to be
-    |  triggered.
-
-+h(2, "benchmarks") Benchmarks
-
-+under-construction
-
-+aside("Data sources")
-    |  #[strong Parser, tagger, NER:] #[+a("https://www.gabormelli.com/RKB/OntoNotes_Corpus") OntoNotes 5]#[br]
-    |  #[strong Word vectors:] #[+a("http://commoncrawl.org") Common Crawl]#[br]
-
-p The evaluation was conducted on raw text with no gold standard information.
-
-+table(["Model", "Version", "Type", "UAS", "LAS", "NER F", "POS", "w/s"])
-    mixin benchmark-row(name, details, values, highlight, style)
-        +row(style)
-            +cell #[code=name]
-            for cell in details
-                +cell=cell
-            for cell, i in values
-                +cell.u-text-right
-                    if highlight && highlight[i]
-                        strong=cell
-                    else
-                        !=cell
-
-    +benchmark-row("en_core_web_sm", ["2.0.0", "neural"], ["91.2", "89.2", "82.6", "96.6", "10,300"], [1, 1, 1, 0, 0])
-    +benchmark-row("en_core_web_sm", ["1.2.0", "linear"], ["86.6", "83.8", "78.5", "96.6", "25,700"], [0, 0, 0, 0, 1], "divider")
-    +benchmark-row("en_core_web_md", ["1.2.1", "linear"], ["90.6", "88.5", "81.4", "96.7", "18,800"], [0, 0, 0, 1, 0])
diff --git a/website/docs/usage/adding-languages.jade b/website/usage/_adding-languages/_language-data.jade
similarity index 62%
rename from website/docs/usage/adding-languages.jade
rename to website/usage/_adding-languages/_language-data.jade
index b341c9f9b..81a6d638e 100644
--- a/website/docs/usage/adding-languages.jade
+++ b/website/usage/_adding-languages/_language-data.jade
@@ -1,58 +1,4 @@
-//- 💫 DOCS > USAGE > ADDING LANGUAGES
-
-include ../../_includes/_mixins
-
-p
-        |  Adding full support for a language touches many different parts of the
-        |  spaCy library. This guide explains how to fit everything together, and
-        |  points you to the specific workflows for each component.
-
-+aside("Working on spaCy's source")
-    |  To add a new language to spaCy, you'll need to
-    |  #[strong modify the library&apos;s code]. The easiest way to do this is to
-    |  clone the #[+src(gh("spaCy")) repository] and #[strong build spaCy from source].
-    |  For more information on this, see the #[+a("/docs/usage") installation guide].
-    |  Unlike spaCy's core, which is mostly written in Cython, all language
-    |  data is stored in regular Python files. This means that you won't have to
-    |  rebuild anything in between – you can simply make edits and reload spaCy
-    |  to test them.
-
-+grid.o-no-block
-    +grid-col("half")
-        p
-            |  Obviously, there are lots of ways you can organise your code when
-            |  you implement your own language data. This guide will focus on
-            |  how it's done within spaCy. For full language support, you'll
-            |  need to create a #[code Language] subclass, define custom
-            |  #[strong language data], like a stop list and tokenizer
-            |  exceptions and test the new tokenizer. Once the language is set
-            |  up, you can #[strong build the vocabulary], including word
-            |  frequencies, Brown clusters and word vectors. Finally, you can
-            |  #[strong train the tagger and parser], and save the model to a
-            |  directory.
-
-        p
-            |  For some languages, you may also want to develop a solution for
-            |  lemmatization and morphological analysis.
-
-    +table-of-contents
-        +item #[+a("#101") Language data 101]
-        +item #[+a("#language-subclass") The Language subclass]
-        +item #[+a("#stop-words") Stop words]
-        +item #[+a("#tokenizer-exceptions") Tokenizer exceptions]
-        +item #[+a("#norm-exceptions") Norm exceptions]
-        +item #[+a("#lex-attrs") Lexical attributes]
-        +item #[+a("#syntax-iterators") Syntax iterators]
-        +item #[+a("#lemmatizer") Lemmatizer]
-        +item #[+a("#tag-map") Tag map]
-        +item #[+a("#morph-rules") Morph rules]
-        +item #[+a("#testing") Testing the tokenizer]
-        +item #[+a("#vocabulary") Building the vocabulary]
-        +item #[+a("#training") Training]
-
-+h(2, "101") Language data 101
-
-include _spacy-101/_language-data
+//- 💫 DOCS > USAGE > ADDING LANGUAGES > LANGUAGE DATA
 
 p
     |  The individual components #[strong expose variables] that can be imported
@@ -137,7 +83,7 @@ p
 
 +aside("Should I ever update the global data?")
     |  Reuseable language data is collected as atomic pieces in the root of the
-    |  #[+src(gh("spaCy", "lang")) spacy.lang] package. Often, when a new
+    |  #[+src(gh("spaCy", "lang")) #[code spacy.lang]] package. Often, when a new
     |  language is added, you'll find a pattern or symbol that's missing. Even
     |  if it isn't common in other languages, it might be best to add it to the
     |  shared language data, unless it has some conflicting interpretation. For
@@ -150,14 +96,14 @@ p
     |  needs to know the language's character set. If the language you're adding
     |  uses non-latin characters, you might need to add the required character
     |  classes to the global
-    |  #[+src(gh("spacy", "spacy/lang/char_classes.py")) char_classes.py].
+    |  #[+src(gh("spacy", "spacy/lang/char_classes.py")) #[code char_classes.py]].
     |  spaCy uses the #[+a("https://pypi.python.org/pypi/regex/") #[code regex] library]
     |  to keep this simple and readable. If the language requires very specific
     |  punctuation rules, you should consider overwriting the default regular
     |  expressions with your own in the language's #[code Defaults].
 
 
-+h(2, "language-subclass") Creating a #[code Language] subclass
++h(3, "language-subclass") Creating a #[code Language] subclass
 
 p
     |  Language-specific code and resources should be organised into a
@@ -250,7 +196,7 @@ p
 +h(3, "tokenizer-exceptions") Tokenizer exceptions
 
 p
-    |  spaCy's #[+a("/docs/usage/customizing-tokenizer#how-tokenizer-works") tokenization algorithm]
+    |  spaCy's #[+a("/usage/linguistic-features#how-tokenizer-works") tokenization algorithm]
     |  lets you deal with whitespace-delimited chunks separately. This makes it
     |  easy to define special-case rules, without worrying about how they
     |  interact with the rest of the tokenizer. Whenever the key string is
@@ -284,7 +230,7 @@ p
     |  efficiently and make your data less verbose. How you do this ultimately
     |  depends on the language. Here's an example of how exceptions for time
     |  formats like "1a.m." and "1am" are generated in the English
-    |  #[+src(gh("spaCy", "spacy/en/lang/tokenizer_exceptions.py")) tokenizer_exceptions.py]:
+    |  #[+src(gh("spaCy", "spacy/en/lang/tokenizer_exceptions.py")) #[code tokenizer_exceptions.py]]:
 
 +code("tokenizer_exceptions.py (excerpt)").
     # use short, internal variable for readability
@@ -376,7 +322,7 @@ p
 p
     |  Norm exceptions can be provided as a simple dictionary. For more examples,
     |  see the English
-    |  #[+src(gh("spaCy", "spacy/lang/en/norm_exceptions.py")) norm_exceptions.py].
+    |  #[+src(gh("spaCy", "spacy/lang/en/norm_exceptions.py")) #[code norm_exceptions.py]].
 
 +code("Example").
     NORM_EXCEPTIONS = {
@@ -428,7 +374,7 @@ p
 
 p
     |  Here's an example from the English
-    |  #[+src(gh("spaCy", "spacy/en/lang/lex_attrs.py")) lex_attrs.py]:
+    |  #[+src(gh("spaCy", "spacy/en/lang/lex_attrs.py")) #[code lex_attrs.py]]:
 
 +code("lex_attrs.py").
     _num_words = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven',
@@ -466,7 +412,7 @@ p
     |  Syntax iterators are functions that compute views of a #[code Doc]
     |  object based on its syntax. At the moment, this data is only used for
     |  extracting
-    |  #[+a("/docs/usage/dependency-parse#noun-chunks") noun chunks], which
+    |  #[+a("/usage/linguistic-features#noun-chunks") noun chunks], which
     |  are available as the #[+api("doc#noun_chunks") #[code Doc.noun_chunks]]
     |  property. Because base noun phrases work differently across languages,
     |  the rules to compute them are part of the individual language's data. If
@@ -479,13 +425,14 @@ p
     assert chunks[0].text == "A phrase"
     assert chunks[1].text == "another phrase"
 
-+table(["Language", "Source"])
-    for lang, lang_id in {en: "English", de: "German", es: "Spanish"}
++table(["Language", "Code", "Source"])
+    for lang in ["en", "de", "fr", "es"]
         +row
-            +cell=lang
+            +cell=LANGUAGES[lang]
+            +cell #[code=lang]
             +cell
-                +src(gh("spaCy", "spacy/lang/" + lang_id + "/syntax_iterators.py"))
-                    |  lang/#{lang_id}/syntax_iterators.py
+                +src(gh("spaCy", "spacy/lang/" + lang + "/syntax_iterators.py"))
+                    code lang/#{lang}/syntax_iterators.py
 
 +h(3, "lemmatizer") Lemmatizer
 
@@ -547,7 +494,7 @@ p
     |  #[+a("http://universaldependencies.org/u/pos/all.html") Universal Dependencies]
     |  tags. Optionally, you can also include morphological features or other
     |  token attributes in the tag map as well. This allows you to do simple
-    |  #[+a("/docs/usage/pos-tagging#rule-based-morphology") rule-based morphological analysis].
+    |  #[+a("/usage/linguistic-features#rule-based-morphology") rule-based morphological analysis].
 
 +code("Example").
     from ..symbols import POS, NOUN, VERB, DET
@@ -560,233 +507,62 @@ p
 
 +h(3, "morph-rules") Morph rules
 
-+under-construction
+p
+    |  The morphology rules let you set token attributes such as lemmas, keyed
+    |  by the extended part-of-speech tag and token text. The morphological
+    |  features and their possible values are language-specific and based on the
+    |  #[+a("http://universaldependencies.org") Universal Dependencies scheme].
 
-+h(2, "testing") Testing the new language tokenizer
+
++code("Example").
+    from ..symbols import LEMMA
+
+    MORPH_RULES = {
+        "VBZ": {
+            "am": {LEMMA: "be", "VerbForm": "Fin", "Person": "One", "Tense": "Pres", "Mood": "Ind"},
+            "are": {LEMMA: "be", "VerbForm": "Fin", "Person": "Two", "Tense": "Pres", "Mood": "Ind"},
+            "is": {LEMMA: "be", "VerbForm": "Fin", "Person": "Three", "Tense": "Pres", "Mood": "Ind"},
+            "'re": {LEMMA: "be", "VerbForm": "Fin", "Person": "Two", "Tense": "Pres", "Mood": "Ind"},
+            "'s": {LEMMA: "be", "VerbForm": "Fin", "Person": "Three", "Tense": "Pres", "Mood": "Ind"}
+        }
+    }
 
 p
-    |  Before using the new language or submitting a
-    |  #[+a(gh("spaCy") + "/pulls") pull request] to spaCy, you should make sure
-    |  it works as expected. This is especially important if you've added custom
-    |  regular expressions for token matching or punctuation – you don't want to
-    |  be causing regressions.
+    |  In the example of #[code "am"], the attributes look like this:
 
-+aside("spaCy's test suite")
-    |  spaCy uses the #[+a("https://docs.pytest.org/en/latest/") pytest framework]
-    |  for testing. For more details on how the tests are structured and best
-    |  practices for writing your own tests, see our
-    |  #[+a(gh("spaCy", "spacy/tests")) tests documentation].
++table(["Attribute", "Description"])
+    +row
+        +cell #[code LEMMA: "be"]
+        +cell Base form, e.g. "to be".
 
-+h(3, "testing-tokenizer") Testing the basic tokenizer
+    +row
+        +cell #[code "VerbForm": "Fin"]
+        +cell
+            |  Finite verb. Finite verbs have a subject and can be the root of
+            |  an independent clause – "I am." is a valid, complete
+            |  sentence.
 
-p
-    |  The easiest way to test your new tokenizer is to run the
-    |  language-independent "tokenizer sanity" tests located in
-    |  #[+src(gh("spaCy", "spacy/tests/tokenizer")) tests/tokenizer]. This will
-    |  test for basic behaviours like punctuation splitting, URL matching and
-    |  correct handling of whitespace. In the
-    |  #[+src(gh("spaCy", "spacy/tests/conftest.py")) conftest.py], add the new
-    |  language ID to the list of #[code _languages]:
+    +row
+        +cell #[code "Person": "One"]
+        +cell First person, i.e. "#[strong I] am".
 
-+code.
-    _languages = ['bn', 'da', 'de', 'en', 'es', 'fi', 'fr', 'he', 'hu', 'it', 'nb',
-                  'nl', 'pl', 'pt', 'sv', 'xx'] # new language here
+    +row
+        +cell #[code "Tense": "Pres"]
+        +cell
+            |  Present tense, i.e. actions that are happening right now or
+            |  actions that usually happen.
 
-+aside-code("Global tokenizer test example").
-    # use fixture by adding it as an argument
-    def test_with_all_languages(tokenizer):
-        # will be performed on ALL language tokenizers
-        tokens = tokenizer(u'Some text here.')
+    +row
+        +cell #[code "Mood": "Ind"]
+        +cell
+            |  Indicative, i.e. something happens, has happened or will happen
+            |  (as opposed to imperative or conditional).
 
-p
-    |  The language will now be included in the #[code tokenizer] test fixture,
-    |  which is used by the basic tokenizer tests. If you want to add your own
-    |  tests that should be run over all languages, you can use this fixture as
-    |  an argument of your test function.
 
-+h(3, "testing-custom") Writing language-specific tests
-
-p
-    |  It's recommended to always add at least some tests with examples specific
-    |  to the language. Language tests should be located in
-    |  #[+src(gh("spaCy", "spacy/tests/lang")) tests/lang] in a directory named
-    |  after the language ID. You'll also need to create a fixture for your
-    |  tokenizer in the #[+src(gh("spaCy", "spacy/tests/conftest.py")) conftest.py].
-    |  Always use the #[code get_lang_class()] helper function within the fixture,
-    |  instead of importing the class at the top of the file. This will load the
-    |  language data only when it's needed. (Otherwise, #[em all data] would be
-    |  loaded every time you run a test.)
-
-+code.
-    @pytest.fixture
-    def en_tokenizer():
-        return util.get_lang_class('en').Defaults.create_tokenizer()
-
-p
-    |  When adding test cases, always
-    |  #[+a(gh("spaCy", "spacy/tests#parameters")) #[code parametrize]] them –
-    |  this will make it easier for others to add more test cases without having
-    |  to modify the test itself. You can also add parameter tuples, for example,
-    |  a test sentence and its expected length, or a list of expected tokens.
-    |  Here's an example of an English tokenizer test for combinations of
-    |  punctuation and abbreviations:
-
-+code("Example test").
-    @pytest.mark.parametrize('text,length', [
-        ("The U.S. Army likes Shock and Awe.", 8),
-        ("U.N. regulations are not a part of their concern.", 10),
-        ("“Isn't it?”", 6)])
-    def test_en_tokenizer_handles_punct_abbrev(en_tokenizer, text, length):
-        tokens = en_tokenizer(text)
-        assert len(tokens) == length
-
-+h(2, "vocabulary") Building the vocabulary
-
-+under-construction
-
-p
-    |  spaCy expects that common words will be cached in a
-    |  #[+api("vocab") #[code Vocab]] instance. The vocabulary caches lexical
-    |  features, and makes it easy to use information from unlabelled text
-    |  samples in your models. Specifically, you'll usually want to collect
-    |  word frequencies, and train two types of distributional similarity model:
-    |  Brown clusters, and word vectors. The Brown clusters are used as features
-    |  by linear models, while the word vectors are useful for lexical
-    |  similarity models and deep learning.
-
-+h(3, "word-frequencies") Word frequencies
-
-p
-    |  To generate the word frequencies from a large, raw corpus, you can use the
-    |  #[+src(gh("spacy-dev-resources", "training/word_freqs.py")) word_freqs.py]
-    |  script from the spaCy developer resources. Note that your corpus should
-    |  not be preprocessed (i.e. you need punctuation for example). The
-    |  #[+api("cli#model") #[code model]] command expects a tab-separated word
-    |  frequencies file with three columns:
-
-+list("numbers")
-    +item The number of times the word occurred in your language sample.
-    +item The number of distinct documents the word occurred in.
-    +item The word itself.
-
-p
-    |  An example word frequencies file could look like this:
-
-+code("es_word_freqs.txt", "text").
-    6361109	111	Aunque
-    23598543	111	aunque
-    10097056	111	claro
-    193454	111	aro
-    7711123	111	viene
-    12812323	111	mal
-    23414636	111	momento
-    2014580	111	felicidad
-    233865	111	repleto
-    15527	111	eto
-    235565	111	deliciosos
-    17259079	111	buena
-    71155	111	Anímate
-    37705	111	anímate
-    33155	111	cuéntanos
-    2389171	111	cuál
-    961576	111	típico
-
-p
-    |  You should make sure you use the spaCy tokenizer for your
-    |  language to segment the text for your word frequencies. This will ensure
-    |  that the frequencies refer to the same segmentation standards you'll be
-    |  using at run-time. For instance, spaCy's English tokenizer segments
-    |  "can't" into two tokens. If we segmented the text by whitespace to
-    |  produce the frequency counts, we'll have incorrect frequency counts for
-    |  the tokens "ca" and "n't".
-
-+h(3, "brown-clusters") Training the Brown clusters
-
-p
-    |  spaCy's tagger, parser and entity recognizer are designed to use
-    |  distributional similarity features provided by the
-    |  #[+a("https://github.com/percyliang/brown-cluster") Brown clustering algorithm].
-    |  You should train a model with between 500 and 1000 clusters. A minimum
-    |  frequency threshold of 10 usually works well.
-
-p
-    |  An example clusters file could look like this:
-
-+code("es_clusters.data", "text").
-    0000	Vestigial	1
-    0000	Vesturland	1
-    0000	Veyreau	1
-    0000	Veynes	1
-    0000	Vexilografía	1
-    0000	Vetrigne	1
-    0000	Vetónica	1
-    0000	Asunden	1
-    0000	Villalambrús	1
-    0000	Vichuquén	1
-    0000	Vichtis	1
-    0000	Vichigasta	1
-    0000	VAAH	1
-    0000	Viciebsk	1
-    0000	Vicovaro	1
-    0000	Villardeveyo	1
-    0000	Vidala	1
-    0000	Videoguard	1
-    0000	Vedás	1
-    0000	Videocomunicado	1
-    0000	VideoCrypt	1
-
-+h(3, "word-vectors") Training the word vectors
-
-+under-construction
-
-p
-    |  #[+a("https://en.wikipedia.org/wiki/Word2vec") Word2vec] and related
-    |  algorithms let you train useful word similarity models from unlabelled
-    |  text. This is a key part of using
-    |  #[+a("/docs/usage/deep-learning") deep learning] for NLP with limited
-    |  labelled data. The vectors are also useful by themselves – they power
-    |  the #[code .similarity()] methods in spaCy. For best results, you should
-    |  pre-process the text with spaCy before training the Word2vec model. This
-    |  ensures your tokenization will match.
-
-p
-    | You can use our
-    |  #[+src(gh("spacy-dev-resources", "training/word_vectors.py")) word vectors training script],
-    |  which pre-processes the text with your language-specific tokenizer and
-    |  trains the model using #[+a("https://radimrehurek.com/gensim/") Gensim].
-    |  The #[code vectors.bin] file should consist of one word and vector per line.
-
-//-+aside-code("your_data_directory", "yaml").
-    ├── vocab/
-    |   ├── lexemes.bin
-    |   ├── strings.json
-    |   └── oov_prob
-    ├── pos/
-    |   ├── model
-    |   └── config.json
-    ├── deps/
-    |   ├── model
-    |   └── config.json
-    └── ner/
-        ├── model
-        └── config.json
-
-+h(2, "train-tagger-parser") Training the tagger and parser
-
-+under-construction
-
-p
-    |  You can now train the model using a corpus for your language annotated
-    |  with #[+a("http://universaldependencies.org/") Universal Dependencies].
-    |  If your corpus uses the
-    |  #[+a("http://universaldependencies.org/docs/format.html") CoNLL-U] format,
-    |  i.e. files with the extension #[code .conllu], you can use the
-    |  #[+api("cli#convert") #[code convert]] command to convert it to spaCy's
-    |  #[+a("/docs/api/annotation#json-input") JSON format] for training.
-
-p
-    |  Once you have your UD corpus transformed into JSON, you can train your
-    |  model use the using spaCy's #[+api("cli#train") #[code train]] command:
-
-+code(false, "bash").
-    spacy train [lang] [output_dir] [train_data] [dev_data] [--n-iter] [--n-sents] [--use-gpu] [--no-tagger] [--no-parser] [--no-entities]
++infobox("Important note", "⚠️")
+    |  The morphological attributes are currently #[strong not all used by spaCy].
+    |  Full integration is still being developed. In the meantime, it can still
+    |  be useful to add them, especially if the language you're adding includes
+    |  important distinctions and special cases. This ensures that as soon as
+    |  full support is introduced, your language will be able to assign all
+    |  possible attributes.
diff --git a/website/usage/_adding-languages/_testing.jade b/website/usage/_adding-languages/_testing.jade
new file mode 100644
index 000000000..825d8db6f
--- /dev/null
+++ b/website/usage/_adding-languages/_testing.jade
@@ -0,0 +1,76 @@
+//- 💫 DOCS > USAGE > ADDING LANGUAGES > TESTING
+
+p
+    |  Before using the new language or submitting a
+    |  #[+a(gh("spaCy") + "/pulls") pull request] to spaCy, you should make sure
+    |  it works as expected. This is especially important if you've added custom
+    |  regular expressions for token matching or punctuation – you don't want to
+    |  be causing regressions.
+
++infobox("spaCy's test suite")
+    |  spaCy uses the #[+a("https://docs.pytest.org/en/latest/") pytest framework]
+    |  for testing. For more details on how the tests are structured and best
+    |  practices for writing your own tests, see our
+    |  #[+a(gh("spaCy", "spacy/tests")) tests documentation].
+
+p
+    |  The easiest way to test your new tokenizer is to run the
+    |  language-independent "tokenizer sanity" tests located in
+    |  #[+src(gh("spaCy", "spacy/tests/tokenizer")) #[code tests/tokenizer]].
+    |  This will test for basic behaviours like punctuation splitting, URL
+    |  matching and correct handling of whitespace. In the
+    |  #[+src(gh("spaCy", "spacy/tests/conftest.py")) #[code conftest.py]], add
+    |  the new language ID to the list of #[code _languages]:
+
++code.
+    _languages = ['bn', 'da', 'de', 'en', 'es', 'fi', 'fr', 'he', 'hu', 'it', 'nb',
+                  'nl', 'pl', 'pt', 'sv', 'xx'] # new language here
+
++aside-code("Global tokenizer test example").
+    # use fixture by adding it as an argument
+    def test_with_all_languages(tokenizer):
+        # will be performed on ALL language tokenizers
+        tokens = tokenizer(u'Some text here.')
+
+p
+    |  The language will now be included in the #[code tokenizer] test fixture,
+    |  which is used by the basic tokenizer tests. If you want to add your own
+    |  tests that should be run over all languages, you can use this fixture as
+    |  an argument of your test function.
+
++h(3, "testing-custom") Writing language-specific tests
+
+p
+    |  It's recommended to always add at least some tests with examples specific
+    |  to the language. Language tests should be located in
+    |  #[+src(gh("spaCy", "spacy/tests/lang")) #[code tests/lang]] in a
+    |  directory named after the language ID. You'll also need to create a
+    |  fixture for your tokenizer in the
+    |  #[+src(gh("spaCy", "spacy/tests/conftest.py")) #[code conftest.py]].
+    |  Always use the #[+api("util#get_lang_class") #[code get_lang_class()]]
+    |  helper function within the fixture, instead of importing the class at the
+    |  top of the file. This will load the language data only when it's needed.
+    |  (Otherwise, #[em all data] would be loaded every time you run a test.)
+
++code.
+    @pytest.fixture
+    def en_tokenizer():
+        return util.get_lang_class('en').Defaults.create_tokenizer()
+
+p
+    |  When adding test cases, always
+    |  #[+a(gh("spaCy", "spacy/tests#parameters")) #[code parametrize]] them –
+    |  this will make it easier for others to add more test cases without having
+    |  to modify the test itself. You can also add parameter tuples, for example,
+    |  a test sentence and its expected length, or a list of expected tokens.
+    |  Here's an example of an English tokenizer test for combinations of
+    |  punctuation and abbreviations:
+
++code("Example test").
+    @pytest.mark.parametrize('text,length', [
+        ("The U.S. Army likes Shock and Awe.", 8),
+        ("U.N. regulations are not a part of their concern.", 10),
+        ("“Isn't it?”", 6)])
+    def test_en_tokenizer_handles_punct_abbrev(en_tokenizer, text, length):
+        tokens = en_tokenizer(text)
+        assert len(tokens) == length
diff --git a/website/usage/_adding-languages/_training.jade b/website/usage/_adding-languages/_training.jade
new file mode 100644
index 000000000..054f2a460
--- /dev/null
+++ b/website/usage/_adding-languages/_training.jade
@@ -0,0 +1,93 @@
+//- 💫 DOCS > USAGE > ADDING LANGUAGES > TRAINING
+
+p
+    |  spaCy expects that common words will be cached in a
+    |  #[+api("vocab") #[code Vocab]] instance. The vocabulary caches lexical
+    |  features, and makes it easy to use information from unlabelled text
+    |  samples in your models. Specifically, you'll usually want to collect
+    |  word frequencies, and train word vectors. To generate the word frequencies
+    |  from a large, raw corpus, you can use the
+    |  #[+src(gh("spacy-dev-resources", "training/word_freqs.py")) #[code word_freqs.py]]
+    |  script from the spaCy developer resources.
+
++github("spacy-dev-resources", "training/word_freqs.py")
+
+p
+    |  Note that your corpus should not be preprocessed (i.e. you need
+    |  punctuation for example). The word frequencies should be generated as a
+    |  tab-separated file with three columns:
+
++list("numbers")
+    +item The number of times the word occurred in your language sample.
+    +item The number of distinct documents the word occurred in.
+    +item The word itself.
+
++code("es_word_freqs.txt", "text").
+    6361109	111	Aunque
+    23598543	111	aunque
+    10097056	111	claro
+    193454	111	aro
+    7711123	111	viene
+    12812323	111	mal
+    23414636	111	momento
+    2014580	111	felicidad
+    233865	111	repleto
+    15527	111	eto
+    235565	111	deliciosos
+    17259079	111	buena
+    71155	111	Anímate
+    37705	111	anímate
+    33155	111	cuéntanos
+    2389171	111	cuál
+    961576	111	típico
+
++aside("Brown Clusters")
+    |  Additionally, you can use distributional similarity features provided by the
+    |  #[+a("https://github.com/percyliang/brown-cluster") Brown clustering algorithm].
+    |  You should train a model with between 500 and 1000 clusters. A minimum
+    |  frequency threshold of 10 usually works well.
+
+p
+    |  You should make sure you use the spaCy tokenizer for your
+    |  language to segment the text for your word frequencies. This will ensure
+    |  that the frequencies refer to the same segmentation standards you'll be
+    |  using at run-time. For instance, spaCy's English tokenizer segments
+    |  "can't" into two tokens. If we segmented the text by whitespace to
+    |  produce the frequency counts, we'll have incorrect frequency counts for
+    |  the tokens "ca" and "n't".
+
++h(4, "word-vectors") Training the word vectors
+
+p
+    |  #[+a("https://en.wikipedia.org/wiki/Word2vec") Word2vec] and related
+    |  algorithms let you train useful word similarity models from unlabelled
+    |  text. This is a key part of using
+    |  #[+a("/usage/deep-learning") deep learning] for NLP with limited
+    |  labelled data. The vectors are also useful by themselves – they power
+    |  the #[code .similarity()] methods in spaCy. For best results, you should
+    |  pre-process the text with spaCy before training the Word2vec model. This
+    |  ensures your tokenization will match. You can use our
+    |  #[+src(gh("spacy-dev-resources", "training/word_vectors.py")) word vectors training script],
+    |  which pre-processes the text with your language-specific tokenizer and
+    |  trains the model using #[+a("https://radimrehurek.com/gensim/") Gensim].
+    |  The #[code vectors.bin] file should consist of one word and vector per line.
+
++github("spacy-dev-resources", "training/word_vectors.py")
+
++h(3, "train-tagger-parser") Training the tagger and parser
+
+p
+    |  You can now train the model using a corpus for your language annotated
+    |  with #[+a("http://universaldependencies.org/") Universal Dependencies].
+    |  If your corpus uses the
+    |  #[+a("http://universaldependencies.org/docs/format.html") CoNLL-U] format,
+    |  i.e. files with the extension #[code .conllu], you can use the
+    |  #[+api("cli#convert") #[code convert]] command to convert it to spaCy's
+    |  #[+a("/api/annotation#json-input") JSON format] for training.
+    |  Once you have your UD corpus transformed into JSON, you can train your
+    |  model use the using spaCy's #[+api("cli#train") #[code train]] command.
+
++infobox
+    |  For more details and examples of how to
+    |  #[strong train the tagger and dependency parser], see the
+    |  #[+a("/usage/training#tagger-parser") usage guide on training].
diff --git a/website/usage/_data.json b/website/usage/_data.json
new file mode 100644
index 000000000..3c37ee4d1
--- /dev/null
+++ b/website/usage/_data.json
@@ -0,0 +1,195 @@
+{
+    "sidebar": {
+        "Get started": {
+            "Installation": "./",
+            "Models & Languages": "models",
+            "Facts & Figures": "facts-figures",
+            "spaCy 101": "spacy-101",
+            "New in v2.0": "v2"
+        },
+        "Guides": {
+            "Linguistic Features": "linguistic-features",
+            "Processing Pipelines": "processing-pipelines",
+            "Vectors & Similarity": "vectors-similarity",
+            "Text Classification": "text-classification",
+            "Deep Learning": "deep-learning",
+            "Training Models": "training",
+            "Adding Languages": "adding-languages",
+            "Visualizers": "visualizers"
+        },
+        "In-depth": {
+            "Code Examples": "examples",
+            "Resources": "resources"
+        }
+    },
+
+    "index": {
+        "title": "Install spaCy",
+        "next": "models",
+        "quickstart": true,
+        "changelog": true,
+        "menu": {
+            "Quickstart": "quickstart",
+            "Instructions": "instructions",
+            "Troubleshooting": "troubleshooting",
+            "Changelog": "changelog"
+        }
+    },
+
+    "models": {
+        "title": "Models & Languages",
+        "next": "facts-figures",
+        "quickstart": true,
+        "menu": {
+            "Quickstart": "quickstart",
+            "Available Models": "available",
+            "Installation & Usage": "install",
+            "Language Support": "languages",
+            "Production Use": "production"
+        }
+    },
+
+    "facts-figures": {
+        "title": "Facts & Figures",
+        "teaser": "The hard numbers for spaCy and how it compares to other libraries and tools.",
+        "next": "spacy-101",
+        "menu": {
+            "Feature comparison": "comparison",
+            "Benchmarks": "benchmarks",
+            "Powered by spaCy": "powered-by",
+            "Other Libraries": "other-libraries"
+        }
+    },
+
+    "spacy-101": {
+        "title": "spaCy 101: Everything you need to know",
+        "teaser": "The most important concepts, explained in simple terms.",
+        "next": "index",
+        "quickstart": true,
+        "preview": "101",
+        "menu": {
+            "Features": "features",
+            "Lightning tour": "lightning-tour",
+            "Architecture": "architecture",
+            "Community & FAQ": "community-faq"
+        }
+    },
+
+    "v2": {
+        "title": "What's New in v2.0",
+        "teaser": "New features, backwards incompatibilities and migration guide.",
+        "menu": {
+            "New features": "features",
+            "Backwards Incompatibilities": "incompat",
+            "Migrating from v1.x": "migrating",
+            "Benchmarks": "benchmarks"
+        }
+    },
+
+    "linguistic-features": {
+        "title": "Linguistic Features",
+        "teaser": "Using spaCy to extract linguistic features like part-of-speech tags, dependency labels and named entities, customising the tokenizer and working with the rule-based matcher.",
+        "next": "processing-pipelines",
+        "menu": {
+            "POS Tagging": "pos-tagging",
+            "Dependency Parse": "dependency-parse",
+            "Named Entities": "named-entities",
+            "Tokenization": "tokenization",
+            "Rule-based Matching": "rule-based-matching"
+        }
+    },
+
+    "processing-pipelines": {
+        "title": "Language Processing Pipelines",
+        "next": "vectors-similarity",
+        "menu": {
+            "How pipelines work": "pipelines",
+            "Examples": "examples",
+            "Multi-threading": "multithreading",
+            "User Hooks": "user-hooks",
+            "Serialization": "serialization"
+        }
+    },
+
+    "vectors-similarity": {
+        "title": "Word Vectors and Semantic Similarity",
+        "next": "text-classification",
+        "menu": {
+            "Basics": "basics",
+            "Similarity in Context": "in-context",
+            "Custom Vectors": "custom",
+            "GPU Usage": "gpu"
+        }
+    },
+
+    "deep-learning": {
+        "title": "Deep Learning",
+        "teaser": "Using spaCy to pre-process text for deep learning, and how to plug in your own machine learning models.",
+        "next": "training",
+        "menu": {
+            "Pre-processing Text": "pre-processing",
+            "spaCy and Thinc": "thinc",
+            "TensorFlow / Keras": "tensorflow-keras",
+            "scikit-learn": "scikit-learn",
+            "PyTorch": "pytorch",
+            "DyNet": "dynet"
+        }
+    },
+
+    "text-classification": {
+        "title": "Text Classification",
+        "next": "training"
+    },
+
+    "training": {
+        "title": "Training spaCy's Statistical Models",
+        "next": "adding-languages",
+        "menu": {
+            "Basics": "basics",
+            "NER": "ner",
+            "Tagger & Parser": "tagger-parser",
+            "Similarity": "similarity",
+            "Text Classification": "textcat",
+            "Saving & Loading": "saving-loading"
+        }
+    },
+
+    "adding-languages": {
+        "title": "Adding Languages",
+        "teaser": "Adding full support for a language touches many different parts of the spaCy library. This guide explains how to fit everything together, and points you to the specific workflows for each component.",
+        "next": "training",
+        "menu": {
+            "Language data": "language-data",
+            "Testing": "testing",
+            "Training": "training"
+        }
+    },
+
+    "visualizers": {
+        "title": "Visualizers",
+        "next": "resources"
+    },
+
+    "resources": {
+        "title": "Resources",
+        "teaser": "Libraries, demos, books, courses and research systems featuring spaCy.",
+        "menu": {
+            "Third-party libraries": "libraries",
+            "Demos & Visualizations": "demos",
+            "Books & Courses": "books",
+            "Jupyter Notebooks": "notebooks",
+            "Research": "research"
+        }
+    },
+
+    "examples": {
+        "title": "Code Examples",
+        "teaser": "Full code examples you can modify and run.",
+        "next": "resources",
+        "menu": {
+            "Matching": "matching",
+            "Training": "training",
+            "Deep Learning": "deep-learning"
+        }
+    }
+}
diff --git a/website/usage/_deep-learning/_dynet.jade b/website/usage/_deep-learning/_dynet.jade
new file mode 100644
index 000000000..81aa4e066
--- /dev/null
+++ b/website/usage/_deep-learning/_dynet.jade
@@ -0,0 +1,11 @@
+//- 💫 DOCS > USAGE > DEEP LEARNING > DYNET
+
++infobox
+    +infobox-logos(["dynet", 80, 34, "http://dynet.readthedocs.io/"])
+    |  #[strong DyNet] is a dynamic neural network library, which can be much
+    |  easier to work with for NLP. Outside of Google, there's a general shift
+    |  among NLP researchers to both DyNet and Pytorch. You can use DyNet to
+    |  create spaCy pipeline components, to add annotations to the #[code Doc]
+    |  object.
+
++under-construction
diff --git a/website/usage/_deep-learning/_pre-processing.jade b/website/usage/_deep-learning/_pre-processing.jade
new file mode 100644
index 000000000..ca87cee7b
--- /dev/null
+++ b/website/usage/_deep-learning/_pre-processing.jade
@@ -0,0 +1,3 @@
+//- 💫 DOCS > USAGE > DEEP LEARNING > PRE-PROCESSING
+
++under-construction
diff --git a/website/usage/_deep-learning/_pytorch.jade b/website/usage/_deep-learning/_pytorch.jade
new file mode 100644
index 000000000..cf0f692f9
--- /dev/null
+++ b/website/usage/_deep-learning/_pytorch.jade
@@ -0,0 +1,91 @@
+//- 💫 DOCS > USAGE > DEEP LEARNING > PYTORCH
+
++infobox
+    +infobox-logos(["pytorch", 100, 48, "http://pytorch.org"])
+    |  #[strong PyTorch] is a dynamic neural network library, which can be much
+    |  easier to work with for NLP. Outside of Google, there's a general shift
+    |  among NLP researchers to both Pytorch and DyNet. spaCy is the front-end
+    |  of choice for PyTorch's #[code torch.text] extension. You can use PyTorch
+    |  to create spaCy pipeline components, to add annotations to the
+    |  #[code Doc] object.
+
++under-construction
+
+p
+    |  Here's how a #[code begin_update] function that wraps an arbitrary
+    |  PyTorch model would look:
+
++code.
+    class PytorchWrapper(thinc.neural.Model):
+        def __init__(self, pytorch_model):
+            self.pytorch_model = pytorch_model
+
+        def begin_update(self, x_data, drop=0.):
+            x_var = Variable(x_data)
+            # Make prediction
+            y_var = pytorch_model.forward(x_var)
+            def backward(dy_data, sgd=None):
+                dy_var = Variable(dy_data)
+                dx_var = torch.autograd.backward(x_var, dy_var)
+                return dx_var
+            return y_var.data, backward
+
+p
+    |  PyTorch requires data to be wrapped in a container, #[code Variable],
+    |  that tracks the operations performed on the data. This "tape" of
+    |  operations is then used by #[code torch.autograd.backward] to compute the
+    |  gradient with respect to the input. For example, the following code
+    |  constructs a PyTorch Linear layer that takes a vector of shape
+    |  #[code (length, 2)], multiples it by a #[code (2, 2)] matrix of weights,
+    |  adds a #[code (2,)] bias, and returns the resulting #[code (length, 2)]
+    |  vector:
+
++code("PyTorch Linear").
+    from torch import autograd
+    from torch import nn
+    import torch
+    import numpy
+
+    pt_model = nn.Linear(2, 2)
+    length = 5
+
+    input_data = numpy.ones((5, 2), dtype='f')
+    input_var = autograd.Variable(torch.Tensor(input_data))
+
+    output_var = pt_model(input_var)
+    output_data = output_var.data.numpy()
+
+p
+    |  Given target values we would like the output data to approximate, we can
+    |  then "learn" values of the parameters within #[code pt_model], to give us
+    |  output that's closer to our target. As a trivial example, let's make the
+    |  linear layer compute the negative inverse of the input:
+
++code.
+    def get_target(input_data):
+        return -(1 / input_data)
+
+p
+    |  To update the PyTorch model, we create an optimizer and give it
+    |  references to the model's parameters. We'll then randomly generate input
+    |  data and get the target result we'd like the function to produce. We then
+    |  compute the #[strong gradient of the error] between the current output
+    |  and the target. Using the most popular definition of "error", this is
+    |  simply the average difference:
+
++code.
+    from torch import optim
+
+    optimizer = optim.SGD(pt_model.parameters(), lr = 0.01)
+    for i in range(10):
+        input_data = numpy.random.uniform(-1., 1., (length, 2))
+        target = -(1 / input_data)
+
+        output_var = pt_model(autograd.Variable(torch.Tensor(input_data)))
+        output_data = output_var.data.numpy()
+
+        d_output_data = (output_data - target) / length
+        d_output_var = autograd.Variable(torch.Tensor(d_output_data))
+
+        d_input_var = torch.autograg.backward(output_var, d_output_var)
+        optimizer.step()
diff --git a/website/usage/_deep-learning/_scikit-learn.jade b/website/usage/_deep-learning/_scikit-learn.jade
new file mode 100644
index 000000000..3d0f30397
--- /dev/null
+++ b/website/usage/_deep-learning/_scikit-learn.jade
@@ -0,0 +1,15 @@
+//- 💫 DOCS > USAGE > DEEP LEARNING > SCIKIT-LEARN
+
++infobox
+    +infobox-logos(["scikitlearn", 70, 34, "http://scikit-learn.org"])
+    |  #[strong scikit-learn] features a number of useful NLP functions,
+    |  especially for solving text classification problems using linear models
+    |  with bag-of-words features. If you know you need exactly that, it might
+    |  be better to use scikit-learn's built-in pipeline directly. However, if
+    |  you want to extract more detailed features, using part-of-speech tags,
+    |  named entity labels, or string transformations, you can use spaCy as a
+    |  pre-process in your classification system. scikit-learn also provides a
+    |  lot of experiment management and evaluation utilities that people use
+    |  alongside spaCy.
+
++under-construction
diff --git a/website/usage/_deep-learning/_tensorflow-keras.jade b/website/usage/_deep-learning/_tensorflow-keras.jade
new file mode 100644
index 000000000..3efb2e2a6
--- /dev/null
+++ b/website/usage/_deep-learning/_tensorflow-keras.jade
@@ -0,0 +1,11 @@
+//- 💫 DOCS > USAGE > DEEP LEARNING > TENSORFLOW / KERAS
+
++infobox
+    +infobox-logos(["tensorflow", 35, 42, "https://www.tensorflow.org"], ["keras", 45, 45, "https://www.keras.io"])
+    |  #[strong Tensorflow / Keras] is the most popular deep learning library.
+    |  spaCy provides efficient and powerful feature extraction functionality,
+    |  that can be used as a pre-process to any deep learning library. You can
+    |  also use Tensorflow and Keras to create spaCy pipeline components, to add
+    |  annotations to the #[code Doc] object.
+
++under-construction
diff --git a/website/usage/_deep-learning/_thinc.jade b/website/usage/_deep-learning/_thinc.jade
new file mode 100644
index 000000000..6c354f708
--- /dev/null
+++ b/website/usage/_deep-learning/_thinc.jade
@@ -0,0 +1,66 @@
+//- 💫 DOCS > USAGE > DEEP LEARNING > THINC
+
+p
+    |  #[+a(gh("thinc")) Thinc] is the machine learning library powering spaCy.
+    |  It's a practical toolkit for implementing models that follow the
+    |  #[+a("https://explosion.ai/blog/deep-learning-formula-nlp", true) "Embed, encode, attend, predict"]
+    |  architecture. It's designed to be easy to install, efficient for CPU
+    |  usage and optimised for NLP and deep learning with text – in particular,
+    |  hierarchically structured input and variable-length sequences.
+
+p
+    |  spaCy's built-in pipeline components can all be powered by any object
+    |  that follows Thinc's #[code Model] API. If a wrapper is not yet available
+    |  for the library you're using, you should create a
+    |  #[code thinc.neural.Model] subclass that implements a #[code begin_update]
+    |  method. You'll also want to implement #[code to_bytes], #[code from_bytes],
+    |  #[code to_disk] and #[code from_disk] methods, to save and load your
+    |  model. Here's the tempate you'll need to fill in:
+
+    +code("Thinc Model API").
+        class ThincModel(thinc.neural.Model):
+            def __init__(self, *args, **kwargs):
+                pass
+
+            def begin_update(self, X, drop=0.):
+                def backprop(dY, sgd=None):
+                    return dX
+                return Y, backprop
+
+            def to_disk(self, path, **exclude):
+                return None
+
+            def from_disk(self, path, **exclude):
+                return self
+
+            def to_bytes(self, **exclude):
+                return bytes
+
+            def from_bytes(self, msgpacked_bytes, **exclude):
+                return self
+
+p
+    |  The #[code begin_update] method should return a callback, that takes the
+    |  gradient with respect to the output, and returns the gradient with
+    |  respect to the input.  It's usually convenient to implement the callback
+    |  as a nested function, so you can refer to any intermediate variables from
+    |  the forward computation in the enclosing scope.
+
++h(3, "how-thinc-works") How Thinc works
+
+p
+    |  Neural networks are all about composing small functions that we know how
+    |  to differentiate into larger functions that we know how to differentiate.
+    |  To differentiate a function efficiently, you usually need to store
+    |  intermediate results, computed during the "forward pass", to reuse them
+    |  during the backward pass. Most libraries require the data passed through
+    |  the network to accumulate these intermediate result. This is the "tape"
+    |  in tape-based differentiation.
+
+p
+    |  In Thinc, a model that computes #[code y = f(x)] is required to also
+    |  return a callback that computes #[code dx = f&apos;(dy)]. The same
+    |  intermediate state needs to be tracked, but this becomes an
+    |  implementation detail for the model to take care of – usually, the
+    |  callback is implemented as a closure, so the intermediate results can be
+    |  read from the enclosing scope.
diff --git a/website/usage/_facts-figures/_benchmarks-choi-2015.jade b/website/usage/_facts-figures/_benchmarks-choi-2015.jade
new file mode 100644
index 000000000..5c3386ce6
--- /dev/null
+++ b/website/usage/_facts-figures/_benchmarks-choi-2015.jade
@@ -0,0 +1,45 @@
+//- 💫 DOCS > USAGE > FACTS & FIGURES > BENCHMARKS > CHOI ET AL. (2015)
+
++table(["System", "Year", "Language", "Accuracy", "Speed (wps)"])
+    +row
+        +cell #[strong spaCy v2.x]
+        +cell 2017
+        +cell Python / Cython
+        +cell.u-text-right #[strong 92.6]
+        +cell.u-text-right #[em n/a]
+            |  #[+help("This table shows speed as benchmarked by Choi et al. We therefore can't provide comparable figures, as we'd be running the benchmark on different hardware.").u-color-dark]
+
+    +row
+        +cell #[strong spaCy v1.x]
+        +cell 2015
+        +cell Python / Cython
+        +cell.u-text-right 91.8
+        +cell.u-text-right 13,963
+
+    +row
+        +cell ClearNLP
+        +cell 2015
+        +cell Java
+        +cell.u-text-right 91.7
+        +cell.u-text-right 10,271
+
+    +row
+        +cell CoreNLP
+        +cell 2015
+        +cell Java
+        +cell.u-text-right 89.6
+        +cell.u-text-right 8,602
+
+    +row
+        +cell MATE
+        +cell 2015
+        +cell Java
+        +cell.u-text-right 92.5
+        +cell.u-text-right 550
+
+    +row
+        +cell Turbo
+        +cell 2015
+        +cell C++
+        +cell.u-text-right 92.4
+        +cell.u-text-right 349
diff --git a/website/usage/_facts-figures/_benchmarks-models.jade b/website/usage/_facts-figures/_benchmarks-models.jade
new file mode 100644
index 000000000..208e7da48
--- /dev/null
+++ b/website/usage/_facts-figures/_benchmarks-models.jade
@@ -0,0 +1,48 @@
+//- 💫 DOCS > USAGE > FACTS & FIGURES > BENCHMARKS > MODEL COMPARISON
+
+p
+    |  In this section, we provide benchmark accuracies for the pre-trained
+    |  model pipelines we distribute with spaCy. Evaluations are conducted
+    |  end-to-end from raw text, with no "gold standard" pre-processing, over
+    |  text from a mix of genres where possible.
+
++under-construction
+
++aside("Methodology")
+    |  The evaluation was conducted on raw text with no gold standard
+    |  information. The parser, tagger and entity recognizer were trained on the
+    |  #[+a("https://www.gabormelli.com/RKB/OntoNotes_Corpus") OntoNotes 5]
+    |  corpus, the word vectors on #[+a("http://commoncrawl.org") Common Crawl].
+
++table(["Model", "spaCy", "Type", "UAS", "NER F", "POS", "WPS", "Size"])
+    +row
+        +cell #[+a("/models/en#en_core_web_sm") #[code en_core_web_sm]] 2.0.0a5
+        each data in ["2.x", "neural"]
+            +cell.u-text-right=data
+        +cell.u-text-right 91.4
+        +cell.u-text-right 85.5
+        +cell.u-text-right 97.0
+        +cell.u-text-right 8.2k
+        +cell.u-text-right #[strong 36 MB]
+
+    +row
+        +cell #[+a("/models/en#en_core_web_lg") #[code en_core_web_lg]] 2.0.0a0
+        each data in ["2.x", "neural"]
+            +cell.u-text-right=data
+        +cell.u-text-right #[strong 91.9]
+        +cell.u-text-right #[strong 86.4]
+        +cell.u-text-right #[strong 97.2]
+        +cell.u-text-right #[em n/a]
+        +cell.u-text-right 667 MB
+
+    +row("divider")
+        +cell #[code en_core_web_sm] 1.2.0
+        each data in ["1.x", "linear", 86.6, 78.5, 96.6]
+            +cell.u-text-right=data
+        +cell.u-text-right #[strong 25.7k]
+        +cell.u-text-right 50 MB
+
+    +row
+        +cell #[code en_core_web_md] 1.2.1
+        each data in ["1.x", "linear", 90.6, 81.4, 96.7, "18.8k", "1 GB"]
+            +cell.u-text-right=data
diff --git a/website/usage/_facts-figures/_benchmarks.jade b/website/usage/_facts-figures/_benchmarks.jade
new file mode 100644
index 000000000..fa0e26763
--- /dev/null
+++ b/website/usage/_facts-figures/_benchmarks.jade
@@ -0,0 +1,206 @@
+//- 💫 DOCS > USAGE > FACTS & FIGURES > BENCHMARKS
+
+p
+    |  Two peer-reviewed papers in 2015 confirm that spaCy offers the
+    |  #[strong fastest syntactic parser in the world] and that
+    |  #[strong its accuracy is within 1% of the best] available. The few
+    |  systems that are more accurate are 20&times; slower or more.
+
++aside("About the evaluation")
+    |  The first of the evaluations was published by #[strong Yahoo! Labs] and
+    |  #[strong Emory University], as part of a survey of current parsing
+    |  technologies #[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") (Choi et al., 2015)].
+    |  Their results and subsequent discussions helped us develop a novel
+    |  psychologically-motivated technique to improve spaCy's accuracy, which
+    |  we published in joint work with Macquarie University
+    |  #[+a("https://aclweb.org/anthology/D/D15/D15-1162.pdf") (Honnibal and Johnson, 2015)].
+
+include _benchmarks-choi-2015
+
++h(3, "algorithm") Algorithm comparison
+
+p
+    |  In this section, we compare spaCy's algorithms to recently published
+    |  systems, using some of the most popular benchmarks. These benchmarks are
+    |  designed to help isolate the contributions of specific algorithmic
+    |  decisions, so they promote slightly "idealised" conditions. Specifically,
+    |  the text comes pre-processed with "gold standard" token and sentence
+    |  boundaries. The data sets also tend to be fairly small, to help
+    |  researchers iterate quickly. These conditions mean the models trained on
+    |  these data sets are not always useful for practical purposes.
+
++h(4, "parse-accuracy-penn") Parse accuracy (Penn Treebank / Wall Street Journal)
+
+p
+    |  This is the "classic" evaluation, so it's the number parsing researchers
+    |  are most easily able to put in context. However, it's quite far removed
+    |  from actual usage: it uses sentences with gold-standard segmentation and
+    |  tokenization, from a pretty specific type of text (articles from a single
+    |  newspaper, 1984-1989).
+
++aside("Methodology")
+    |  #[+a("http://arxiv.org/abs/1603.06042") Andor et al. (2016)] chose
+    |  slightly different experimental conditions from
+    |  #[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") Choi et al. (2015)],
+    |  so the two accuracy tables here do not present directly comparable
+    |  figures.
+
++table(["System", "Year", "Type", "Accuracy"])
+    +row
+        +cell spaCy v2.0.0
+        +cell 2017
+        +cell neural
+        +cell.u-text-right 94.48
+
+    +row
+        +cell spaCy v1.1.0
+        +cell 2016
+        +cell linear
+        +cell.u-text-right 92.80
+
+    +row("divider")
+        +cell
+            +a("https://arxiv.org/pdf/1611.01734.pdf") Dozat and Manning
+            +cell 2017
+            +cell neural
+            +cell.u-text-right #[strong 95.75]
+
+    +row
+        +cell
+            +a("http://arxiv.org/abs/1603.06042") Andor et al.
+        +cell 2016
+        +cell neural
+        +cell.u-text-right 94.44
+
+    +row
+        +cell
+            +a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet Parsey McParseface
+        +cell 2016
+        +cell neural
+        +cell.u-text-right 94.15
+
+    +row
+        +cell
+            +a("http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43800.pdf") Weiss et al.
+        +cell 2015
+        +cell neural
+        +cell.u-text-right 93.91
+
+    +row
+        +cell
+            +a("http://research.google.com/pubs/archive/38148.pdf") Zhang and McDonald
+        +cell 2014
+        +cell linear
+        +cell.u-text-right 93.32
+
+    +row
+        +cell
+            +a("http://www.cs.cmu.edu/~ark/TurboParser/") Martins et al.
+        +cell 2013
+        +cell linear
+        +cell.u-text-right 93.10
+
++h(4, "ner-accuracy-ontonotes5") NER accuracy (OntoNotes 5, no pre-process)
+
+p
+    |  This is the evaluation we use to tune spaCy's parameters are decide which
+    |  algorithms are better than others. It's reasonably close to actual usage,
+    |  because it requires the parses to be produced from raw text, without any
+    |  pre-processing.
+
++table(["System", "Year", "Type", "Accuracy"])
+    +row
+        +cell spaCy #[+a("/models/en#en_core_web_lg") #[code en_core_web_lg]] v2.0.0
+        +cell 2017
+        +cell neural
+        +cell.u-text-right 86.45
+
+    +row("divider")
+        +cell
+            +a("https://arxiv.org/pdf/1702.02098.pdf") Strubell et al.
+        +cell 2017
+        +cell neural
+        +cell.u-text-right #[strong 86.81]
+
+    +row
+        +cell
+            +a("https://www.semanticscholar.org/paper/Named-Entity-Recognition-with-Bidirectional-LSTM-C-Chiu-Nichols/10a4db59e81d26b2e0e896d3186ef81b4458b93f") Chiu and Nichols
+        +cell 2016
+        +cell neural
+        +cell.u-text-right 86.19
+
+    +row
+        +cell
+            +a("https://www.semanticscholar.org/paper/A-Joint-Model-for-Entity-Analysis-Coreference-Typi-Durrett-Klein/28eb033eee5f51c5e5389cbb6b777779203a6778") Durrett and Klein
+        +cell 2014
+        +cell neural
+        +cell.u-text-right 84.04
+
+    +row
+        +cell
+            +a("http://www.aclweb.org/anthology/W09-1119") Ratinov and Roth
+        +cell 2009
+        +cell linear
+        +cell.u-text-right 83.45
+
++h(3, "spacy-models") Model comparison
+
+include _benchmarks-models
+
++h(3, "speed-comparison") Detailed speed comparison
+
+p
+    |  Here we compare the per-document processing time of various spaCy
+    |  functionalities against other NLP libraries. We show both absolute
+    |  timings (in ms) and relative performance (normalized to spaCy). Lower is
+    |  better.
+
++infobox("Important note", "⚠️")
+    |  This evaluation was conducted in 2015. We're working on benchmarks on
+    |  current CPU and GPU hardware.
+
++aside("Methodology")
+    |  #[strong Set up:] 100,000 plain-text documents were streamed from an
+    |  SQLite3 database, and processed with an NLP library, to one of three
+    |  levels of detail — tokenization, tagging, or parsing. The tasks are
+    |  additive: to parse the text you have to tokenize and tag it. The
+    |  pre-processing was not subtracted from the times — we report the time
+    |  required for the pipeline to complete. We report mean times per document,
+    |  in milliseconds.#[br]#[br]
+    |  #[strong Hardware]: Intel i7-3770 (2012)#[br]
+    |  #[strong Implementation]: #[+src(gh("spacy-benchmarks")) #[code spacy-benchmarks]]
+
++table
+    +row.u-text-label.u-text-center
+        +head-cell
+        +head-cell(colspan="3") Absolute (ms per doc)
+        +head-cell(colspan="3") Relative (to spaCy)
+
+    +row
+        each column in ["System", "Tokenize", "Tag", "Parse", "Tokenize", "Tag", "Parse"]
+            +head-cell=column
+
+    +row
+        +cell #[strong spaCy]
+        each data in [ "0.2ms", "1ms", "19ms"]
+            +cell.u-text-right #[strong=data]
+
+        each data in ["1x", "1x", "1x"]
+            +cell.u-text-right=data
+
+    +row
+        +cell CoreNLP
+        each data in ["2ms", "10ms", "49ms", "10x", "10x", "2.6x"]
+            +cell.u-text-right=data
+    +row
+        +cell ZPar
+        each data in ["1ms", "8ms", "850ms", "5x", "8x", "44.7x"]
+            +cell.u-text-right=data
+    +row
+        +cell NLTK
+        each data in ["4ms", "443ms"]
+            +cell.u-text-right=data
+        +cell.u-text-right #[em n/a]
+        each data in ["20x", "443x"]
+            +cell.u-text-right=data
+        +cell.u-text-right #[em n/a]
diff --git a/website/usage/_facts-figures/_feature-comparison.jade b/website/usage/_facts-figures/_feature-comparison.jade
new file mode 100644
index 000000000..92ac69050
--- /dev/null
+++ b/website/usage/_facts-figures/_feature-comparison.jade
@@ -0,0 +1,58 @@
+//- 💫 DOCS > USAGE > FACTS & FIGURES > FEATURE COMPARISON
+
+p
+    |  Here's a quick comparison of the functionalities offered by spaCy,
+    |  #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet],
+    |  #[+a("http://www.nltk.org/py-modindex.html") NLTK] and
+    |  #[+a("http://stanfordnlp.github.io/CoreNLP/") CoreNLP].
+
++table(["", "spaCy", "SyntaxNet", "NLTK", "CoreNLP"])
+    +row
+        +cell Programming language
+        each lang in ["Python", "C++", "Python", "Java"]
+            +cell.u-text-small.u-text-center=lang
+
+    +row
+        +cell Neural network models
+            each icon in ["pro", "pro", "con", "pro"]
+                +cell.u-text-center #[+procon(icon)]
+
+    +row
+        +cell Integrated word vectors
+        each icon in ["pro", "con", "con", "con"]
+            +cell.u-text-center #[+procon(icon)]
+
+    +row
+        +cell Multi-language support
+        each icon in ["pro", "pro", "pro", "pro"]
+            +cell.u-text-center #[+procon(icon)]
+
+    +row
+        +cell Tokenization
+        each icon in ["pro", "pro", "pro", "pro"]
+            +cell.u-text-center #[+procon(icon)]
+
+    +row
+        +cell Part-of-speech tagging
+        each icon in ["pro", "pro", "pro", "pro"]
+            +cell.u-text-center #[+procon(icon)]
+
+    +row
+        +cell Sentence segmentation
+        each icon in ["pro", "pro", "pro", "pro"]
+            +cell.u-text-center #[+procon(icon)]
+
+    +row
+        +cell Dependency parsing
+        each icon in ["pro", "pro", "con", "pro"]
+            +cell.u-text-center #[+procon(icon)]
+
+    +row
+        +cell Entity recognition
+        each icon in ["pro", "con", "pro", "pro"]
+            +cell.u-text-center #[+procon(icon)]
+
+    +row
+        +cell Coreference resolution
+        each icon in ["con", "con", "con", "pro"]
+            +cell.u-text-center #[+procon(icon)]
diff --git a/website/usage/_facts-figures/_other-libraries.jade b/website/usage/_facts-figures/_other-libraries.jade
new file mode 100644
index 000000000..427debb27
--- /dev/null
+++ b/website/usage/_facts-figures/_other-libraries.jade
@@ -0,0 +1,70 @@
+//- 💫 DOCS > USAGE > FACTS & FIGURES > OTHER LIBRARIES
+
+p
+    |  Data scientists, researchers and machine learning engineers have
+    |  converged on Python as the language for AI. This gives developers a rich
+    |  ecosystem of NLP libraries to work with. Here's how we think the pieces
+    |  fit together.
+
++aside("Using spaCy with other libraries")
+    |  For details on how to use spaCy together with popular machine learning
+    |  libraries like TensorFlow, Keras or PyTorch, see the
+    |  #[+a("/usage/deep-learning") usage guide on deep learning].
+
++infobox
+    +infobox-logos(["nltk", 80, 25, "http://nltk.org"])
+    |  #[+label-inline NLTK] offers some of the same functionality as spaCy.
+    |  Although originally developed for teaching and research, its longevity
+    |  and stability has resulted in a large number of industrial users. It's
+    |  the main alternative to spaCy for tokenization and sentence segmentation.
+    |  In comparison to spaCy, NLTK takes a much more "broad church" approach –
+    |  so it has some functions that spaCy doesn't provide, at the expense of a
+    |  bit more clutter to sift through. spaCy is also much more
+    |  performance-focussed than NLTK: where the two libraries provide the same
+    |  functionality, spaCy's implementation will usually be faster and more
+    |  accurate.
+
++infobox
+    +infobox-logos(["gensim", 40, 40, "https://radimrehurek.com/gensim/"])
+    |  #[+label-inline Gensim] provides unsupervised text modelling algorithms.
+    |  Although Gensim isn't a runtime dependency of spaCy, we use it to train
+    |  word vectors. There's almost no overlap between the libraries – the two
+    |  work together.
+
++infobox
+    +infobox-logos(["tensorflow", 35, 42, "https://www.tensorflow.org"], ["keras", 45, 45, "https://www.keras.io"])
+    |  #[+label-inline Tensorflow / Keras] is the most popular deep learning library.
+    |  spaCy provides efficient and powerful feature extraction functionality,
+    |  that can be used as a pre-process to any deep learning library. You can
+    |  also use Tensorflow and Keras to create spaCy pipeline components, to add
+    |  annotations to the #[code Doc] object.
+
++infobox
+    +infobox-logos(["scikitlearn", 90, 44, "http://scikit-learn.org"])
+    |  #[+label-inline scikit-learn] features a number of useful NLP functions,
+    |  especially for solving text classification problems using linear models
+    |  with bag-of-words features. If you know you need exactly that, it might
+    |  be better to use scikit-learn's built-in pipeline directly. However, if
+    |  you want to extract more detailed features, using part-of-speech tags,
+    |  named entity labels, or string transformations, you can use spaCy as a
+    |  pre-process in your classification system. scikit-learn also provides a
+    |  lot of experiment management and evaluation utilities that people use
+    |  alongside spaCy.
+
++infobox
+    +infobox-logos(["pytorch", 100, 48, "http://pytorch.org"], ["dynet", 80, 34, "http://dynet.readthedocs.io/"], ["chainer", 80, 43, "http://chainer.org"])
+    |  #[+label-inline PyTorch, DyNet and Chainer] are dynamic neural network
+    |  libraries, which can be much easier to work with for NLP. Outside of
+    |  Google, there's a general shift among NLP researchers to both DyNet and
+    |  Pytorch. spaCy is the front-end of choice for PyTorch's
+    |  #[code torch.text] extension. You can use any of these libraries to
+    |  create spaCy pipeline components, to add annotations to the #[code Doc]
+    |  object.
+
++infobox
+    +infobox-logos(["allennlp", 124, 22, "http://allennlp.org"])
+    |  #[+label-inline AllenNLP] is a new library designed to accelerate NLP
+    |  research, by providing a framework that supports modern deep learning
+    |  workflows for cutting-edge language understanding problems. AllenNLP uses
+    |  spaCy as a preprocessing component. You can also use AllenNLP to develop
+    |  spaCy pipeline components, to add annotations to the #[code Doc] object.
diff --git a/website/usage/_install/_changelog.jade b/website/usage/_install/_changelog.jade
new file mode 100644
index 000000000..e966b6695
--- /dev/null
+++ b/website/usage/_install/_changelog.jade
@@ -0,0 +1,31 @@
+//- 💫 DOCS > USAGE > INSTALL > CHANGELOG
+
++h(2, "changelog") Changelog
+    +button(gh("spacy") + "/releases", false, "secondary", "small").u-float-right.u-nowrap View releases
+
+div(data-tpl="changelog" data-tpl-key="error")
+    +infobox
+        |  Unable to load changelog from GitHub. Please see the
+        |  #[+a(gh("spacy") + "/releases") releases page] instead.
+
+section(data-tpl="changelog" data-tpl-key="table" style="display: none")
+    +table(["Date", "Version", "Title"])
+        tbody(data-tpl="changelog" data-tpl-key="releases")
+            +row(data-tpl="changelog" data-tpl-key="item")
+                +cell.u-nowrap
+                    +label(data-changelog="date")
+                +cell(data-changelog="tag")
+                +cell.u-text-small(data-changelog="title")
+
+    +h(3) Pre-releases
+
+    +aside("About pre-releases")
+        .o-block-small
+            |  Pre-releases include alpha and beta versions, as well as release
+            |  candidates. They are not intended for production use. You can
+            |  download spaCy pre-releases via the #[code spacy-nightly] package
+            |  on pip.
+        +badge("https://img.shields.io/pypi/v/spacy-nightly.svg?style=flat-square", "https://pypi.python.org/pypi/spacy-nightly")
+
+    +table(["Date", "Version", "Title"])
+        tbody(data-tpl="changelog" data-tpl-key="prereleases")
diff --git a/website/usage/_install/_instructions.jade b/website/usage/_install/_instructions.jade
new file mode 100644
index 000000000..10132a646
--- /dev/null
+++ b/website/usage/_install/_instructions.jade
@@ -0,0 +1,185 @@
+//- 💫 DOCS > USAGE > INSTALL > INSTRUCTIONS
+
++h(3, "pip") pip
+    +badge("https://img.shields.io/pypi/v/spacy.svg?style=flat-square", "https://pypi.python.org/pypi/spacy")
+
+p Using pip, spaCy releases are currently only available as source packages.
+
++code(false, "bash").
+    pip install -U spacy
+
++aside("Download models")
+    |  After installation you need to download a language model. For more info
+    |  and available models, see the #[+a("/usage/models") docs on models].
+
+    +code.o-no-block.
+        spacy download en
+
+        &gt;&gt;&gt; import spacy
+        &gt;&gt;&gt; nlp = spacy.load('en')
+
+p
+    |  When using pip it is generally recommended to install packages in a
+    |  #[code virtualenv] to avoid modifying system state:
+
++code(false, "bash").
+    virtualenv .env
+    source .env/bin/activate
+    pip install spacy
+
++h(3, "conda") conda
+    +badge("https://anaconda.org/conda-forge/spacy/badges/version.svg", "https://anaconda.org/conda-forge/spacy")
+
+p
+    |  Thanks to our great community, we've finally re-added conda support. You
+    |  can now install spaCy via #[code conda-forge]:
+
++code(false, "bash").
+    conda config --add channels conda-forge
+    conda install spacy
+
+p
+    |  For the feedstock including the build recipe and configuration, check out
+    |  #[+a("https://github.com/conda-forge/spacy-feedstock") this repository].
+    |  Improvements and pull requests to the recipe and setup are always
+    |  appreciated.
+
++h(3, "gpu") Run spaCy with GPU
+
+p
+    |  As of v2.0, spaCy's comes with neural network models that are implemented
+    |  in our machine learning library, #[+a(gh("thinc")) Thinc]. For GPU
+    |  support, we've been grateful to use the work of
+    |  #[+a("http://chainer.org") Chainer]'s CuPy module, which provides
+    |  a NumPy-compatible interface for GPU arrays.
+
+p
+    |  First, install follows the normal CUDA installation procedure. Next, set
+    |  your environment variables so that the installation will be able to find
+    |  CUDA. Finally, install spaCy.
+
++code(false, "bash").
+    export CUDA_HOME=/usr/local/cuda-8.0 # Or wherever your CUDA is
+    export PATH=$PATH:$CUDA_HOME/bin
+
+    pip install spacy
+    python -c "import thinc.neural.gpu_ops" # Check the GPU ops were built
+
++h(3, "source") Compile from source
+
+p
+    |  The other way to install spaCy is to clone its
+    |  #[+a(gh("spaCy")) GitHub repository] and build it from source. That is
+    |  the common way if you want to make changes to the code base. You'll need
+    |  to make sure that you have a development environment consisting of a
+    |  Python distribution including header files, a compiler,
+    |  #[+a("https://pip.pypa.io/en/latest/installing/") pip],
+    |  #[+a("https://virtualenv.pypa.io/") virtualenv] and
+    |  #[+a("https://git-scm.com") git] installed. The compiler part is the
+    |  trickiest. How to do that depends on your system. See notes on
+    |  #[a(href="#source-ubuntu") Ubuntu], #[a(href="#source-osx") OS X] and
+    |  #[a(href="#source-windows") Windows] for details.
+
++code(false, "bash").
+    # make sure you are using recent pip/virtualenv versions
+    python -m pip install -U pip virtualenv
+    git clone #{gh("spaCy")}
+    cd spaCy
+
+    virtualenv .env
+    source .env/bin/activate
+    pip install -r requirements.txt
+    pip install -e .
+
+p
+    |  Compared to regular install via pip,
+    |  #[+a(gh("spaCy", "requirements.txt")) requirements.txt]
+    |  additionally installs developer dependencies such as Cython.
+
+p
+    |  Instead of the above verbose commands, you can also use the following
+    |  #[+a("http://www.fabfile.org/") Fabric] commands:
+
++table(["Command", "Description"])
+    +row
+        +cell #[code fab env]
+        +cell Create #[code virtualenv] and delete previous one, if it exists.
+
+    +row
+        +cell #[code fab make]
+        +cell Compile the source.
+
+    +row
+        +cell #[code fab clean]
+        +cell Remove compiled objects, including the generated C++.
+
+    +row
+        +cell #[code fab test]
+        +cell Run basic tests, aborting after first failure.
+
+p
+    |  All commands assume that your #[code virtualenv] is located in a
+    |  directory #[code .env]. If you're using a different directory, you can
+    |  change it via the environment variable #[code VENV_DIR], for example:
+
++code(false, "bash").
+    VENV_DIR=".custom-env" fab clean make
+
++h(4, "source-ubuntu") Ubuntu
+
+p Install system-level dependencies via #[code apt-get]:
+
++code(false, "bash").
+    sudo apt-get install build-essential python-dev git
+
++h(4, "source-osx") macOS / OS X
+
+p
+    |  Install a recent version of
+    |  #[+a("https://developer.apple.com/xcode/") XCode], including the
+    |  so-called "Command Line Tools". macOS and OS X ship with Python and git
+    |  preinstalled. To compile spaCy with multi-threading support on macOS / OS X,
+    |  #[+a("https://github.com/explosion/spaCy/issues/267") see here].
+
++h(4, "source-windows") Windows
+
+p
+    |  Install a version of
+    |  #[+a("https://www.visualstudio.com/vs/visual-studio-express/") Visual Studio Express]
+    |  that matches the version that was used to compile your Python
+    |  interpreter. For official distributions these are:
+
++table([ "Distribution", "Version"])
+    +row
+        +cell Python 2.7
+        +cell Visual Studio 2008
+
+    +row
+        +cell Python 3.4
+        +cell Visual Studio 2010
+
+    +row
+        +cell Python 3.5+
+        +cell Visual Studio 2015
+
++h(3, "tests") Run tests
+
+p
+    |  spaCy comes with an #[+a(gh("spacy", "spacy/tests")) extensive test suite].
+    |  First, find out where spaCy is installed:
+
++code(false, "bash").
+    python -c "import os; import spacy; print(os.path.dirname(spacy.__file__))"
+
+p
+    |  Then run #[code pytest] on that directory. The flags #[code --slow] and
+    |  #[code --model] are optional and enable additional tests.
+
++code(false, "bash").
+    # make sure you are using recent pytest version
+    python -m pip install -U pytest
+
+    python -m pytest &lt;spacy-directory&gt;                 # basic tests
+    python -m pytest &lt;spacy-directory&gt; --slow          # basic and slow tests
+    python -m pytest &lt;spacy-directory&gt; --models --all  # basic and all model tests
+    python -m pytest &lt;spacy-directory&gt; --models --en   # basic and English model tests
diff --git a/website/usage/_install/_quickstart.jade b/website/usage/_install/_quickstart.jade
new file mode 100644
index 000000000..8e581994c
--- /dev/null
+++ b/website/usage/_install/_quickstart.jade
@@ -0,0 +1,26 @@
+//- 💫 DOCS > USAGE > INSTALL > QUICKSTART
+
+- QUICKSTART[QUICKSTART.length - 1].options = Object.keys(MODELS).map(m => ({ id: m, title: LANGUAGES[m] }))
+
++quickstart(QUICKSTART, "Quickstart")
+    +qs({config: 'venv', python: 2}) python -m pip install -U virtualenv
+    +qs({config: 'venv', python: 3}) python -m pip install -U venv
+    +qs({config: 'venv', python: 2}) virtualenv .env
+    +qs({config: 'venv', python: 3}) venv .env
+    +qs({config: 'venv', os: 'mac'}) source .env/bin/activate
+    +qs({config: 'venv', os: 'linux'}) source .env/bin/activate
+    +qs({config: 'venv', os: 'windows'}) .env\Scripts\activate
+
+    +qs({config: 'gpu', os: 'mac'}) export PATH=$PATH:/usr/local/cuda-8.0/bin
+    +qs({config: 'gpu', os: 'linux'}) export PATH=$PATH:/usr/local/cuda-8.0/bin
+
+    +qs({package: 'pip'}) pip install -U spacy
+    +qs({package: 'conda'}) conda install -c conda-forge spacy
+
+    +qs({package: 'source'}) git clone https://github.com/explosion/spaCy
+    +qs({package: 'source'}) cd spaCy
+    +qs({package: 'source'}) pip install -r requirements.txt
+    +qs({package: 'source'}) pip install -e .
+
+    for _, model in MODELS
+        +qs({model: model}) spacy download #{model}
diff --git a/website/usage/_install/_troubleshooting.jade b/website/usage/_install/_troubleshooting.jade
new file mode 100644
index 000000000..9fb92f17b
--- /dev/null
+++ b/website/usage/_install/_troubleshooting.jade
@@ -0,0 +1,147 @@
+//- 💫 DOCS > USAGE > INSTALL > TROUBLESHOOTING
+
+p
+    |  This section collects some of the most common errors you may come
+    |  across when installing, loading and using spaCy, as well as their solutions.
+
++aside("Help us improve this guide")
+    |  Did you come across a problem like the ones listed here and want to
+    |  share the solution? You can find the "Suggest edits" button at the
+    |  bottom of this page that points you to the source. We always
+    |  appreciate #[+a(gh("spaCy") + "/pulls") pull requests]!
+
++h(3, "compatible-model") No compatible model found
+
++code(false, "text").
+    No compatible model found for [lang] (spaCy v#{SPACY_VERSION}).
+
+p
+    |  This usually means that the model you're trying to download does not
+    |  exist, or isn't available for your version of spaCy. Check the
+    |  #[+a(gh("spacy-models", "compatibility.json")) compatibility table]
+    |  to see which models are available for your spaCy version. If you're using
+    |  an old version, consider upgrading to the latest release. Note that while
+    |  spaCy supports tokenization for
+    |  #[+a("/usage/models/#languages") a variety of languages],
+    |  not all of them come with statistical models. To only use the tokenizer,
+    |  import the language's #[code Language] class instead, for example
+    |  #[code from spacy.fr import French].
+
++h(3, "symlink-privilege") Symbolic link privilege not held
+
++code(false, "text").
+    OSError: symbolic link privilege not held
+
+p
+    |  To create #[+a("/usage/models/#usage") shortcut links] that let you
+    |  load models by name, spaCy creates a symbolic link in the
+    |  #[code spacy/data] directory. This means your user needs permission to do
+    |  this. The above error mostly occurs when doing a system-wide installation,
+    |  which will create the symlinks in a system directory. Run the
+    |  #[code download] or #[code link] command as administrator, or use a
+    |  #[code virtualenv] to install spaCy in a user directory, instead
+    |  of doing a system-wide installation.
+
++h(3, "no-cache-dir") No such option: --no-cache-dir
+
++code(false, "text").
+    no such option: --no-cache-dir
+
+p
+    |  The #[code download] command uses pip to install the models and sets the
+    |  #[code --no-cache-dir] flag to prevent it from requiring too much memory.
+    |  #[+a("https://pip.pypa.io/en/stable/reference/pip_install/#caching") This setting]
+    |  requires pip v6.0 or newer. Run #[code pip install -U pip] to upgrade to
+    |  the latest version of pip. To see which version you have installed,
+    |  run #[code pip --version].
+
++h(3, "import-error") Import error
+
++code(false, "text").
+    Import Error: No module named spacy
+
+p
+    |  This error means that the spaCy module can't be located on your system, or in
+    |  your environment. Make sure you have spaCy installed. If you're using a
+    |  #[code virtualenv], make sure it's activated and check that spaCy is
+    |  installed in that environment – otherwise, you're trying to load a system
+    |  installation. You can also run #[code which python] to find out where
+    |  your Python executable is located.
+
++h(3, "import-error-models") Import error: models
+
++code(false, "text").
+    ImportError: No module named 'en_core_web_sm'
+
+p
+    |  As of spaCy v1.7, all models can be installed as Python packages. This means
+    |  that they'll become importable modules of your application. When creating
+    |  #[+a("/usage/models/#usage") shortcut links], spaCy will also try
+    |  to import the model to load its meta data. If this fails, it's usually a
+    |  sign that the package is not installed in the current environment.
+    |  Run #[code pip list] or #[code pip freeze] to check which model packages
+    |  you have installed, and install the
+    |  #[+a("/models") correct models] if necessary. If you're
+    |  importing a model manually at the top of a file, make sure to use the name
+    |  of the package, not the shortcut link you've created.
+
++h(3, "vocab-strings") File not found: vocab/strings.json
+
++code(false, "text").
+    FileNotFoundError: No such file or directory: [...]/vocab/strings.json
+
+p
+    |  This error may occur when using #[code spacy.load()] to load
+    |  a language model – either because you haven't set up a
+    |  #[+a("/usage/models/#usage") shortcut link] for it, or because it
+    |  doesn't actually exist. Set up a
+    |  #[+a("/usage/models/#usage") shortcut link] for the model
+    |  you want to load. This can either be an installed model package, or a
+    |  local directory containing the model data. If you want to use one of the
+    |  #[+a("/usage/models#languages") alpha tokenizers] for
+    |  languages that don't yet have a statistical model, you should import its
+    |  #[code Language] class instead, for example
+    |  #[code from spacy.lang.bn import Bengali].
+
++h(3, "command-not-found") Command not found
+
++code(false, "text").
+    command not found: spacy
+
+p
+    |  This error may occur when running the #[code spacy] command from the
+    |  command line. spaCy does not currently add an entry to our #[code PATH]
+    |  environment variable, as this can lead to unexpected results, especially
+    |  when using #[code virtualenv]. Instead, spaCy adds an auto-alias that
+    |  maps #[code spacy] to #[code python -m spacy]. If this is not working as
+    |  expected, run the command with #[code python -m], yourself –
+    |  for example #[code python -m spacy download en]. For more info on this,
+    |  see #[+api("cli#download") download].
+
++h(3, "module-load") 'module' object has no attribute 'load'
+
++code(false, "text").
+    AttributeError: 'module' object has no attribute 'load'
+
+p
+    |  While this could technically have many causes, including spaCy being
+    |  broken, the most likely one is that your script's file or directory name
+    |  is "shadowing" the module – e.g. your file is called #[code spacy.py],
+    |  or a directory you're importing from is called #[code spacy]. So, when
+    |  using spaCy, never call anything else #[code spacy].
+
++h(3, "pron-lemma") Pronoun lemma is returned as #[code -PRON-]
+
++code.
+    doc = nlp(u'They are')
+    print(doc[0].lemma_)
+    # -PRON-
+
+p
+    |  This is in fact expected behaviour and not a bug.
+    |  Unlike verbs and common nouns, there's no clear base form of a personal
+    |  pronoun. Should the lemma of "me" be "I", or should we normalize person
+    |  as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
+    |  novel symbol, #[code -PRON-], which is used as the lemma for
+    |  all personal pronouns. For more info on this, see the
+    |  #[+api("annotation#lemmatization") annotation specs] on lemmatization.
diff --git a/website/docs/usage/dependency-parse.jade b/website/usage/_linguistic-features/_dependency-parse.jade
similarity index 93%
rename from website/docs/usage/dependency-parse.jade
rename to website/usage/_linguistic-features/_dependency-parse.jade
index beae36578..85d9179df 100644
--- a/website/docs/usage/dependency-parse.jade
+++ b/website/usage/_linguistic-features/_dependency-parse.jade
@@ -1,6 +1,4 @@
-//- 💫 DOCS > USAGE > DEPENDENCY PARSE
-
-include ../../_includes/_mixins
+//- 💫 DOCS > USAGE > LINGUISTIC FEATURES > DEPENDENCY PARSE
 
 p
     |  spaCy features a fast and accurate syntactic dependency parser, and has
@@ -11,8 +9,7 @@ p
     |  boolean value. If this attribute is #[code False], the default sentence
     |  iterator will raise an exception.
 
-+h(2, "noun-chunks") Noun chunks
-    +tag-model("dependency parse")
++h(3, "noun-chunks") Noun chunks
 
 p
     |  Noun chunks are "base noun phrases" – flat phrases that have a noun as
@@ -41,7 +38,7 @@ p
     +annotation-row(["insurance liability", "liability", "dobj", "shift"], style)
     +annotation-row(["manufacturers", "manufacturers", "pobj", "toward"], style)
 
-+h(2, "navigating") Navigating the parse tree
++h(3, "navigating") Navigating the parse tree
 
 p
     |  spaCy uses the terms #[strong head] and #[strong child] to describe the words
@@ -110,7 +107,7 @@ p
     |  attribute, which provides a sequence of #[+api("token") #[code Token]]
     |  objects.
 
-+h(3, "navigating-around") Iterating around the local tree
++h(4, "navigating-around") Iterating around the local tree
 
 p
     |  A few more convenience attributes are provided for iterating around the
@@ -135,7 +132,7 @@ p
     |  method.
 
 +aside("Projective vs. non-projective")
-    |  For the #[+a("/docs/usage/models#available") default English model], the
+    |  For the #[+a("/models/en") default English model], the
     |  parse tree is #[strong projective], which means that there are no crossing
     |  brackets. The tokens returned by #[code .subtree] are therefore guaranteed
     |  to be contiguous. This is not true for the German model, which has many
@@ -181,7 +178,7 @@ p
     +annotation-row(["their", "ADJ", "poss", "requests"], style)
     +annotation-row(["requests", "NOUN", "dobj", "submit"], style)
 
-+h(2, "displacy") Visualizing dependencies
++h(3, "displacy") Visualizing dependencies
 
 p
     |  The best way to understand spaCy's dependency parser is interactively.
@@ -201,14 +198,14 @@ p
 
 +infobox
     |  For more details and examples, see the
-    |  #[+a("/docs/usage/visualizers") usage guide on visualizing spaCy]. You
+    |  #[+a("/usage/visualizers") usage guide on visualizing spaCy]. You
     |  can also test displaCy in our #[+a(DEMOS_URL + "/displacy", true) online demo].
 
-+h(2, "disabling") Disabling the parser
++h(3, "disabling") Disabling the parser
 
 p
-    |  In the #[+a("/docs/usage/models/available") default models], the parser
-    |  is loaded and enabled as part of the
+    |  In the #[+a("/models") default models], the parser is loaded and enabled
+    |  as part of the
     |  #[+a("docs/usage/language-processing-pipelines") standard processing pipeline].
     |  If you don't need any of the syntactic information, you should disable
     |  the parser. Disabling the parser will make spaCy load and run much faster.
@@ -225,7 +222,7 @@ p
         |  Since spaCy v2.0 comes with better support for customising the
         |  processing pipeline components, the #[code parser] keyword argument
         |  has been replaced with #[code disable], which takes a list of
-        |  #[+a("/docs/usage/language-processing-pipeline") pipeline component names].
+        |  #[+a("/usage/processing-pipelines") pipeline component names].
         |  This lets you disable both default and custom components when loading
         |  a model, or initialising a Language class via
         |  #[+api("language-from_disk") #[code from_disk]].
diff --git a/website/docs/usage/entity-recognition.jade b/website/usage/_linguistic-features/_named-entities.jade
similarity index 74%
rename from website/docs/usage/entity-recognition.jade
rename to website/usage/_linguistic-features/_named-entities.jade
index 826de1543..f42df3342 100644
--- a/website/docs/usage/entity-recognition.jade
+++ b/website/usage/_linguistic-features/_named-entities.jade
@@ -1,6 +1,4 @@
-//- 💫 DOCS > USAGE > NAMED ENTITY RECOGNITION
-
-include ../../_includes/_mixins
+//- 💫 DOCS > USAGE > LINGUISTIC FEATURES > NAMED ENTITY RECOGNITION
 
 p
     |  spaCy features an extremely fast statistical entity recognition system,
@@ -9,12 +7,11 @@ p
     |  locations, organizations and products. You can add arbitrary classes to
     |  the entity recognition system, and update the model with new examples.
 
-+h(2, "101") Named Entity Recognition 101
-    +tag-model("named entities")
++h(3, "101") Named Entity Recognition 101
 
-include _spacy-101/_named-entities
+include ../_spacy-101/_named-entities
 
-+h(2, "accessing") Accessing entity annotations
++h(3, "accessing") Accessing entity annotations
 
 p
     |  The standard way to access entity annotations is the
@@ -62,7 +59,7 @@ p
     +annotation-row(["delivery", 2, "O", '""', "outside an entity"], style)
     +annotation-row(["robots", 2, "O", '""', "outside an entity"], style)
 
-+h(2, "setting") Setting entity annotations
++h(3, "setting") Setting entity annotations
 
 p
     |  To ensure that the sequence of token annotations remains consistent, you
@@ -92,7 +89,7 @@ p
     |  but at the document level, the entity will have the start and end
     |  indices #[code (0, 7)].
 
-+h(3, "setting-from-array") Setting entity annotations from array
++h(4, "setting-from-array") Setting entity annotations from array
 
 p
     |  You can also assign entity annotations using the
@@ -114,7 +111,7 @@ p
     doc.from_array(header, attr_array)
     assert list(doc.ents)[0].text == u'London'
 
-+h(3, "setting-cython") Setting entity annotations in Cython
++h(4, "setting-cython") Setting entity annotations in Cython
 
 p
     |  Finally, you can always write to the underlying struct, if you compile
@@ -137,18 +134,16 @@ p
     |  you'll have responsibility for ensuring that the data is left in a
     |  consistent state.
 
-+h(2, "entity-types") Built-in entity types
++h(3, "entity-types") Built-in entity types
 
 +aside("Tip: Understanding entity types")
     |  You can also use #[code spacy.explain()] to get the description for the
     |  string representation of an entity label. For example,
     |  #[code spacy.explain("LANGUAGE")] will return "any named language".
 
-include ../api/_annotation/_named-entities
+include ../../api/_annotation/_named-entities
 
-+h(2, "updating") Training and updating
-
-+under-construction
++h(3, "updating") Training and updating
 
 p
     |  To provide training examples to the entity recogniser, you'll first need
@@ -166,65 +161,24 @@ p
 
 +code.
     doc = Doc(nlp.vocab, [u'rats', u'make', u'good', u'pets'])
-    gold = GoldParse(doc, [u'U-ANIMAL', u'O', u'O', u'O'])
+    gold = GoldParse(doc, entities=[u'U-ANIMAL', u'O', u'O', u'O'])
 
 +infobox
     |  For more details on #[strong training and updating] the named entity
-    |  recognizer, see the usage guides on #[+a("/docs/usage/training") training]
-    |  and #[+a("/docs/usage/training-ner") training the named entity recognizer],
+    |  recognizer, see the usage guides on #[+a("/usage/training") training]
     |  or check out the runnable
     |  #[+src(gh("spaCy", "examples/training/train_ner.py")) training script]
     |  on GitHub.
 
-+h(3, "updating-biluo") The BILUO Scheme
++h(4, "updating-biluo") The BILUO Scheme
 
 p
     |  You can also provide token-level entity annotation, using the
     |  following tagging scheme to describe the entity boundaries:
 
-+table([ "Tag", "Description" ])
-    +row
-        +cell #[code #[span.u-color-theme B] EGIN]
-        +cell The first token of a multi-token entity.
+include ../../api/_annotation/_biluo
 
-    +row
-        +cell #[code #[span.u-color-theme I] N]
-        +cell An inner token of a multi-token entity.
-
-    +row
-        +cell #[code #[span.u-color-theme L] AST]
-        +cell The final token of a multi-token entity.
-
-    +row
-        +cell #[code #[span.u-color-theme U] NIT]
-        +cell A single-token entity.
-
-    +row
-        +cell #[code #[span.u-color-theme O] UT]
-        +cell A non-entity token.
-
-+aside("Why BILUO, not IOB?")
-    |  There are several coding schemes for encoding entity annotations as
-    |  token tags.  These coding schemes are equally expressive, but not
-    |  necessarily equally learnable.
-    |  #[+a("http://www.aclweb.org/anthology/W09-1119") Ratinov and Roth]
-    |  showed that the minimal #[strong Begin], #[strong In], #[strong Out]
-    |  scheme was more difficult to learn than the #[strong BILUO] scheme that
-    |  we use, which explicitly marks boundary tokens.
-
-p
-    |  spaCy translates the character offsets into this scheme, in order to
-    |  decide the cost of each action given the current state of the entity
-    |  recogniser. The costs are then used to calculate the gradient of the
-    |  loss, to train the model. The exact algorithm is a pastiche of
-    |  well-known methods, and is not currently described in any single
-    |  publication. The model is a greedy transition-based parser guided by a
-    |  linear model whose weights are learned using the averaged perceptron
-    |  loss, via the #[+a("http://www.aclweb.org/anthology/C12-1059") dynamic oracle]
-    |  imitation learning strategy. The transition system is equivalent to the
-    |  BILOU tagging scheme.
-
-+h(2, "displacy") Visualizing named entities
++h(3, "displacy") Visualizing named entities
 
 p
     |  The #[+a(DEMOS_URL + "/displacy-ent/") displaCy #[sup ENT] visualizer]
@@ -238,7 +192,7 @@ p
 
 p
     |  For more details and examples, see the
-    |  #[+a("/docs/usage/visualizers") usage guide on visualizing spaCy].
+    |  #[+a("/usage/visualizers") usage guide on visualizing spaCy].
 
 +code("Named Entity example").
     import spacy
diff --git a/website/docs/usage/pos-tagging.jade b/website/usage/_linguistic-features/_pos-tagging.jade
similarity index 76%
rename from website/docs/usage/pos-tagging.jade
rename to website/usage/_linguistic-features/_pos-tagging.jade
index effc185e9..4e845cdaf 100644
--- a/website/docs/usage/pos-tagging.jade
+++ b/website/usage/_linguistic-features/_pos-tagging.jade
@@ -1,20 +1,10 @@
-//- 💫 DOCS > USAGE > PART-OF-SPEECH TAGGING
+//- 💫 DOCS > USAGE > LINGUISTIC FEATURES > PART-OF-SPEECH TAGGING
 
-include ../../_includes/_mixins
+include ../_spacy-101/_pos-deps
 
-p
-    |  Part-of-speech tags are labels like noun, verb, adjective etc that are
-    |  assigned to each token in the document. They're useful in rule-based
-    |  processes. They can also be useful features in some statistical models.
+//-+aside("Help – spaCy's output is wrong!")
 
-+h(2, "101") Part-of-speech tagging 101
-    +tag-model("tagger", "dependency parse")
-
-include _spacy-101/_pos-deps
-
-+aside("Help – spaCy's output is wrong!")
-
-+h(2, "rule-based-morphology") Rule-based morphology
++h(3, "rule-based-morphology") Rule-based morphology
 
 p
     |  Inflectional morphology is the process by which a root form of a word is
@@ -54,7 +44,7 @@ p
 +list("numbers")
     +item
         |  The tokenizer consults a
-        |  #[+a("/docs/usage/adding-languages#tokenizer-exceptions") mapping table]
+        |  #[+a("/usage/adding-languages#tokenizer-exceptions") mapping table]
         |  #[code TOKENIZER_EXCEPTIONS], which allows sequences of characters
         |  to be mapped to multiple tokens. Each token may be assigned a part
         |  of speech and one or more morphological features.
@@ -68,7 +58,7 @@ p
 
     +item
         |  For words whose POS is not set by a prior process, a
-        |  #[+a("/docs/usage/adding-languages#tag-map") mapping table]
+        |  #[+a("/usage/adding-languages#tag-map") mapping table]
         |  #[code TAG_MAP] maps the tags to a part-of-speech and a set of
         |  morphological features.
 
@@ -80,6 +70,4 @@ p
         |  list-based exception files, acquired from
         |  #[+a("https://wordnet.princeton.edu/") WordNet].
 
-+h(2, "pos-schemes") Part-of-speech tag schemes
-
-include ../api/_annotation/_pos-tags
+include ../../api/_annotation/_pos-tags
diff --git a/website/docs/usage/rule-based-matching.jade b/website/usage/_linguistic-features/_rule-based-matching.jade
similarity index 95%
rename from website/docs/usage/rule-based-matching.jade
rename to website/usage/_linguistic-features/_rule-based-matching.jade
index 71400ea55..88a713ffc 100644
--- a/website/docs/usage/rule-based-matching.jade
+++ b/website/usage/_linguistic-features/_rule-based-matching.jade
@@ -1,19 +1,18 @@
 //- 💫 DOCS > USAGE > RULE-BASED MATCHING
 
-include ../../_includes/_mixins
-
 p
-    |  spaCy features a rule-matching engine that operates over tokens, similar
+    |  spaCy features a rule-matching engine, the #[+api("matcher") #[code Matcher]],
+    |  that operates over tokens, similar
     |  to regular expressions. The rules can refer to token annotations (e.g.
     |  the token #[code text] or #[code tag_], and flags (e.g. #[code IS_PUNCT]).
     |  The rule matcher also lets you pass in a custom callback
     |  to act on matches – for example, to merge entities and apply custom labels.
     |  You can also associate patterns with entity IDs, to allow some basic
-    |  entity linking or disambiguation.
+    |  entity linking or disambiguation. To match large terminology lists,
+    |  you can use the #[+api("phrasematcher") #[code PhraseMatcher]], which
+    |  accepts #[code Doc] objects as match patterns.
 
-//-+aside("What about \"real\" regular expressions?")
-
-+h(2, "adding-patterns") Adding patterns
++h(3, "adding-patterns") Adding patterns
 
 p
     |  Let's say we want to enable spaCy to find a combination of three tokens:
@@ -76,7 +75,7 @@ p
     |  other pattern types. You shouldn't have to create different matchers for
     |  each of those processes.
 
-+h(2, "on_match") Adding #[code on_match] rules
++h(3, "on_match") Adding #[code on_match] rules
 
 p
     |  To move on to a more realistic example, let's say you're working with a
@@ -142,7 +141,7 @@ p
                                options={'ents': ['EVENT']})
 
     |  For more info and examples, see the usage guide on
-    |  #[+a("/docs/usage/visualizers") visualizing spaCy].
+    |  #[+a("/usage/visualizers") visualizing spaCy].
 
 p
     |  We can now call the matcher on our documents. The patterns will be
@@ -184,7 +183,7 @@ p
             |  A list of #[code (match_id, start, end)] tuples, describing the
             |  matches. A match tuple describes a span #[code doc[start:end]].
 
-+h(2, "quantifiers") Using operators and quantifiers
++h(3, "quantifiers") Using operators and quantifiers
 
 p
     |  The matcher also lets you use quantifiers, specified as the #[code 'OP']
@@ -221,7 +220,7 @@ p
         +cell match 0 or 1 times
         +cell optional, max one
 
-+h(2, "example1") Example: Using linguistic annotations
++h(3, "example1") Example: Using linguistic annotations
 
 p
     |  Let's say you're analysing user comments and you want to find out what
@@ -246,13 +245,13 @@ p
 p
     |  To get a quick overview of the results, you could collect all sentences
     |  containing a match and render them with the
-    |  #[+a("/docs/usage/visualizers") displaCy visualizer].
+    |  #[+a("/usage/visualizers") displaCy visualizer].
     |  In the callback function, you'll have access to the #[code start] and
     |  #[code end] of each match, as well as the parent #[code Doc]. This lets
     |  you determine the sentence containing the match,
     |  #[code doc[start : end].sent], and calculate the start and end of the
     |  matched span within the sentence. Using displaCy in
-    |  #[+a("/docs/usage/visualizers#manual-usage") "manual" mode] lets you
+    |  #[+a("/usage/visualizers#manual-usage") "manual" mode] lets you
     |  pass in a list of dictionaries containing the text and entities to render.
 
 +code.
@@ -283,7 +282,7 @@ p
     # set manual=True to make displaCy render straight from a dictionary
     displacy.serve(matched_sents, style='ent', manual=True)
 
-+h(2, "example2") Example: Phone numbers
++h(3, "example2") Example: Phone numbers
 
 p
     |  Phone numbers can have many different formats and matching them is often
@@ -321,7 +320,7 @@ p
     |  extend, and doesn't require any training data – only a set of
     |  test cases.
 
-+h(2, "example3") Example: Hashtags and emoji on social media
++h(3, "example3") Example: Hashtags and emoji on social media
 
 p
     |  Social media posts, especially tweets, can be difficult to work with.
diff --git a/website/docs/usage/customizing-tokenizer.jade b/website/usage/_linguistic-features/_tokenization.jade
similarity index 76%
rename from website/docs/usage/customizing-tokenizer.jade
rename to website/usage/_linguistic-features/_tokenization.jade
index 0bc81771d..182bc31e9 100644
--- a/website/docs/usage/customizing-tokenizer.jade
+++ b/website/usage/_linguistic-features/_tokenization.jade
@@ -1,6 +1,4 @@
-//- 💫 DOCS > USAGE > TOKENIZER
-
-include ../../_includes/_mixins
+//- 💫 DOCS > USAGE > LINGUISTIC FEATURES > TOKENIZATION
 
 p
     |  Tokenization is the task of splitting a text into meaningful segments,
@@ -11,15 +9,14 @@ p
     |  #[code spaces] booleans, which allow you to maintain alignment of the
     |  tokens into the original string.
 
-+h(2, "101") Tokenizer 101
+include ../_spacy-101/_tokenization
 
-include _spacy-101/_tokenization
-
-+h(3, "101-data") Tokenizer data
++h(4, "101-data") Tokenizer data
 
 p
     |  #[strong Global] and #[strong language-specific] tokenizer data is
-    |  supplied via the language data in #[+src(gh("spaCy", "spacy/lang")) spacy/lang].
+    |  supplied via the language data in
+    |  #[+src(gh("spaCy", "spacy/lang")) #[code spacy/lang]].
     |  The tokenizer exceptions define special cases like "don't" in English,
     |  which needs to be split into two tokens: #[code {ORTH: "do"}] and
     |  #[code {ORTH: "n't", LEMMA: "not"}]. The prefixes, suffixes and infixes
@@ -27,16 +24,14 @@ p
     |  (at the end of a sentence), and when to leave token containing periods
     |  intact (abbreviations like "U.S.").
 
-+image
-    include ../../assets/img/docs/language_data.svg
-    .u-text-right
-        +button("/assets/img/docs/language_data.svg", false, "secondary").u-text-tag View large graphic
++graphic("/assets/img/language_data.svg")
+    include ../../assets/img/language_data.svg
 
 +infobox
     |  For more details on the language-specific data, see the
-    |  usage guide on #[+a("/docs/usage/adding-languages") adding languages].
+    |  usage guide on #[+a("/usage/adding-languages") adding languages].
 
-+h(2, "special-cases") Adding special case tokenization rules
++h(3, "special-cases") Adding special case tokenization rules
 
 p
     |  Most domains have at least some idiosyncrasies that require custom
@@ -46,7 +41,7 @@ p
 +aside("Language data vs. custom tokenization")
     |  Tokenization rules that are specific to one language, but can be
     |  #[strong generalised across that language] should ideally live in the
-    |  language data in #[+src(gh("spaCy", "spacy/lang")) spacy/lang] – we
+    |  language data in #[+src(gh("spaCy", "spacy/lang")) #[code spacy/lang]] – we
     |  always appreciate pull requests! Anything that's specific to a domain or
     |  text type – like financial trading abbreviations, or Bavarian youth slang
     |  – should be added as a special case rule to your tokenizer instance. If
@@ -69,9 +64,12 @@ p
     special_case = [{ORTH: u'gim', LEMMA: u'give', POS: u'VERB'}, {ORTH: u'me'}]
     nlp.tokenizer.add_special_case(u'gimme', special_case)
     assert [w.text for w in nlp(u'gimme that')] == [u'gim', u'me', u'that']
-    assert [w.lemma_ for w in nlp(u'gimme that')] == [u'give', u'me', u'that']
+    # Pronoun lemma is returned as -PRON-!
+    assert [w.lemma_ for w in nlp(u'gimme that')] == [u'give', u'-PRON-', u'that']
 
 p
+    |  For details on spaCy's custom pronoun lemma #[code -PRON-],
+    |  #[+a("/usage/#pron-lemma") see here].
     |  The special case doesn't have to match an entire whitespace-delimited
     |  substring. The tokenizer will incrementally split off punctuation, and
     |  keep looking up the remaining substring:
@@ -97,7 +95,7 @@ p
     |  #[+api("language") #[code Language]] class itself.
 
 
-+h(2, "how-tokenizer-works") How spaCy's tokenizer works
++h(3, "how-tokenizer-works") How spaCy's tokenizer works
 
 p
     |  spaCy introduces a novel tokenization algorithm, that gives a better
@@ -113,8 +111,8 @@ p
     |  algorithm in Python, optimized for readability rather than performance:
 
 +code.
-    def tokenizer_pseudo_code(text, find_prefix, find_suffix,
-                              find_infixes, special_cases):
+    def tokenizer_pseudo_code(text, special_cases,
+                              find_prefix, find_suffix, find_infixes):
         tokens = []
         for substring in text.split(' '):
             suffixes = []
@@ -162,11 +160,11 @@ p
         |  like hyphens etc.
     +item Once we can't consume any more of the string, handle it as a single token.
 
-+h(2, "native-tokenizers") Customizing spaCy's Tokenizer class
++h(3, "native-tokenizers") Customizing spaCy's Tokenizer class
 
 p
     |  Let's imagine you wanted to create a tokenizer for a new language or
-    |  specific domain. There are four things you would need to define:
+    |  specific domain. There are five things you would need to define:
 
 +list("numbers")
     +item
@@ -188,6 +186,11 @@ p
         |  A function #[code infixes_finditer], to handle non-whitespace
         |  separators, such as hyphens etc.
 
+    +item
+        |  An optional boolean function #[code token_match] matching strings
+        |  that should never be split, overriding the previous rules.
+        |  Useful for things like URLs or numbers.
+
 p
     |  You shouldn't usually need to create a #[code Tokenizer] subclass.
     |  Standard usage is to use #[code re.compile()] to build a regular
@@ -200,10 +203,14 @@ p
 
     prefix_re = re.compile(r'''[\[\(&quot;&apos;]''')
     suffix_re = re.compile(r'''[\]\)&quot;&apos;]''')
+    infix_re = re.compile(r'''[-~]''')
+    simple_url_re = re.compile(r'''^https?://''')
 
     def custom_tokenizer(nlp):
         return Tokenizer(nlp.vocab, prefix_search=prefix_re.search,
-                                    suffix_search=suffix_re.search)
+                                    suffix_search=suffix_re.search,
+                                    infix_finditer=infix_re.finditer,
+                                    token_match=simple_url_re.match)
 
     nlp = spacy.load('en')
     nlp.tokenizer = custom_tokenizer(nlp)
@@ -213,7 +220,7 @@ p
     |  specialize are #[code find_prefix], #[code find_suffix] and
     |  #[code find_infix].
 
-+h(2, "custom-tokenizer") Hooking an arbitrary tokenizer into the pipeline
++h(3, "custom-tokenizer") Hooking an arbitrary tokenizer into the pipeline
 
 p
     |  The tokenizer is the first component of the processing pipeline and the
@@ -222,11 +229,8 @@ p
     |  it takes a text and returns a #[code Doc], whereas all other components
     |  expect to already receive a tokenized #[code Doc].
 
-+image
-    include ../../assets/img/docs/pipeline.svg
-    .u-text-right
-        +button("/assets/img/docs/pipeline.svg", false, "secondary").u-text-tag View large graphic
-
++graphic("/assets/img/pipeline.svg")
+    include ../../assets/img/pipeline.svg
 
 p
     |  To overwrite the existing tokenizer, you need to replace
@@ -243,7 +247,7 @@ p
         +cell unicode
         +cell The raw text to tokenize.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Doc]
         +cell The tokenized document.
@@ -295,3 +299,36 @@ p
 +code.
     nlp = spacy.load('en')
     nlp.tokenizer = WhitespaceTokenizer(nlp.vocab)
+
++h(3, "own-annotations") Bringing your own annotations
+
+p
+    |  spaCy generally assumes by default that your data is raw text. However,
+    |  sometimes your data is partially annotated, e.g. with pre-existing
+    |  tokenization, part-of-speech tags, etc. The most common situation is
+    |  that you have pre-defined tokenization. If you have a list of strings,
+    |  you can create a #[code Doc] object directly. Optionally, you can also
+    |  specify a list of boolean values, indicating whether each word has a
+    |  subsequent space.
+
++code.
+    doc = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'], spaces=[False, True, False, False])
+
+p
+    |  If provided, the spaces list must be the same length as the words list.
+    |  The spaces list affects the #[code doc.text], #[code span.text],
+    |  #[code token.idx], #[code span.start_char] and #[code span.end_char]
+    |  attributes. If you don't provide a #[code spaces] sequence, spaCy will
+    |  assume that all words are whitespace delimited.
+
++code.
+    good_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'], spaces=[False, True, False, False])
+    bad_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'])
+    assert bad_spaces.text == u'Hello , world !'
+    assert good_spaces.text == u'Hello, world!'
+
+p
+    |  Once you have a #[+api("doc") #[code Doc]] object, you can write to its
+    |  attributes to set the part-of-speech tags, syntactic dependencies, named
+    |  entities and other attributes. For details, see the respective usage
+    |  pages.
diff --git a/website/usage/_models/_available-models.jade b/website/usage/_models/_available-models.jade
new file mode 100644
index 000000000..b4fa1fc90
--- /dev/null
+++ b/website/usage/_models/_available-models.jade
@@ -0,0 +1,22 @@
+//- 💫 DOCS > USAGE > MODELS > AVAILABE MODELS
+
+p
+    |  Model differences are mostly statistical. In general, we do expect larger
+    |  models to be "better" and more accurate overall. Ultimately, it depends on
+    |  your use case and requirements, and we recommend starting with the default
+    |  models (marked with a star below). For a more detailed overview, see the
+    |  #[+a("/models") models directory].
+
++table(["Name", "Language", "Type"])
+    for models, lang in MODELS
+        for model, i in models
+            - var comps = getModelComponents(model)
+            +row
+                +cell #[+a("/models/" + lang + "#" + model) #[code=model]]
+                    if i == 0
+                        +icon("star", 16).o-icon--inline.u-color-theme
+                +cell #{LANGUAGES[comps.lang]}
+                +cell #{MODEL_META[comps.type]}
+
+.u-text-right
+    +button("/models", true, "primary", "small") View models directory
diff --git a/website/usage/_models/_install-basics.jade b/website/usage/_models/_install-basics.jade
new file mode 100644
index 000000000..a8029cc10
--- /dev/null
+++ b/website/usage/_models/_install-basics.jade
@@ -0,0 +1,33 @@
+//- 💫 DOCS > USAGE > MODELS > INSTALLATION BASICS
+
+p
+    |  The easiest way to download a model is via spaCy's
+    |  #[+api("cli#download") #[code download]] command. It takes care of
+    |  finding the best-matching model compatible with your spaCy installation.
+
+- var models = Object.keys(MODELS).map(function(lang) { return "spacy download " + lang })
++code(false, "bash").
+    # out-of-the-box: download best-matching default model
+    #{Object.keys(MODELS).map(function(l) {return "spacy download " + l}).join('\n')}
+
+    # download best-matching version of specific model for your spaCy installation
+    spacy download en_core_web_sm
+
+    # download exact model version (doesn't create shortcut link)
+    spacy download en_core_web_sm-2.0.0 --direct
+
+p
+    |  The download command will #[+a("/usage/models#download-pip") install the model] via
+    |  pip, place the package in your #[code site-packages] directory and create
+    |  a #[+a("/usage/models#usage") shortcut link] that lets you load the model by a custom
+    |  name. The shortcut link will be the same as the model name used in
+    |  #[code spacy download].
+
++code(false, "bash").
+    pip install spacy
+    spacy download en
+
++code.
+    import spacy
+    nlp = spacy.load('en')
+    doc = nlp(u'This is a sentence.')
diff --git a/website/docs/usage/models.jade b/website/usage/_models/_install.jade
similarity index 67%
rename from website/docs/usage/models.jade
rename to website/usage/_models/_install.jade
index 7421e8aad..1d15199a2 100644
--- a/website/docs/usage/models.jade
+++ b/website/usage/_models/_install.jade
@@ -1,38 +1,4 @@
-//- 💫 DOCS > USAGE > MODELS
-
-include ../../_includes/_mixins
-
-p
-    |  As of v1.7.0, models for spaCy can be installed as #[strong Python packages].
-    |  This means that they're a component of your application, just like any
-    |  other module. They're versioned and can be defined as a dependency in your
-    |  #[code requirements.txt]. Models can be installed from a download URL or
-    |  a local directory, manually or via #[+a("https://pypi.python.org/pypi/pip") pip].
-    |  Their data can be located anywhere on your file system.
-
-+aside("Important note")
-    |  If you're upgrading to spaCy v1.7.x or v2.x, you need to
-    |  #[strong download the new models]. If you've trained statistical models
-    |  that use spaCy's annotations, you should #[strong retrain your models]
-    |  after updating spaCy. If you don't retrain, you may suffer train/test
-    |  skew, which might decrease your accuracy.
-
-+quickstart(QUICKSTART_MODELS, "Quickstart", "Install a default model, get the code to load it from within spaCy and an example to test it. For more options, see the section on available models below.")
-    for models, lang in MODELS
-        - var package = (models.length == 1) ? models[0] : models.find(function(m) { return m.def })
-        +qs({lang: lang}) spacy download #{lang}
-        +qs({lang: lang}, "divider")
-        +qs({lang: lang, load: "module"}, "python") import #{package.id}
-        +qs({lang: lang, load: "module"}, "python") nlp = #{package.id}.load()
-        +qs({lang: lang, load: "spacy"}, "python") nlp = spacy.load('#{lang}')
-        +qs({lang: lang, config: "example"}, "python") doc = nlp(u"#{EXAMPLE_SENTENCES[lang]}")
-        +qs({lang: lang, config: "example"}, "python") print([(w.text, w.pos_) for w in doc])
-
-+h(2, "available") Available models
-
-include _models-list
-
-+h(2, "download") Downloading models
+//- 💫 DOCS > USAGE > MODELS > INSTALLATION
 
 +aside("Downloading models in spaCy < v1.7")
     |  In older versions of spaCy, you can still use the old download commands.
@@ -47,37 +13,8 @@ include _models-list
     |  The old models are also #[+a(gh("spacy") + "/tree/v1.6.0") attached to the v1.6.0 release].
     |  To download and install them manually, unpack the archive, drop the
     |  contained directory into #[code spacy/data].
-p
-    |  The easiest way to download a model is via spaCy's
-    |  #[+api("cli#download") #[code download]] command. It takes care of
-    |  finding the best-matching model compatible with your spaCy installation.
 
-- var models = Object.keys(MODELS).map(function(lang) { return "spacy download " + lang })
-+code(false, "bash").
-    # out-of-the-box: download best-matching default model
-    #{Object.keys(MODELS).map(function(l) {return "spacy download " + l}).join('\n')}
-
-    # download best-matching version of specific model for your spaCy installation
-    spacy download en_core_web_md
-
-    # download exact model version (doesn't create shortcut link)
-    spacy download en_core_web_md-1.2.0 --direct
-
-p
-    |  The download command will #[+a("#download-pip") install the model] via
-    |  pip, place the package in your #[code site-packages] directory and create
-    |  a #[+a("#usage") shortcut link] that lets you load the model by a custom
-    |  name. The shortcut link will be the same as the model name used in
-    |  #[code spacy.download].
-
-+code(false, "bash").
-    pip install spacy
-    spacy download en
-
-+code.
-    import spacy
-    nlp = spacy.load('en')
-    doc = nlp(u'This is a sentence.')
+include _install-basics
 
 +h(3, "download-pip") Installation via pip
 
@@ -107,8 +44,8 @@ p
 +infobox
     |  You can also add the direct download link to your application's
     |  #[code requirements.txt]. For more details,
-    |  see the usage guide on
-    |  #[+a("/docs/usage/production-use#models") working with models in production].
+    |  see the section on
+    |  #[+a("/models/#production") working with models in production].
 
 
 +h(3, "download-manual") Manual download and installation
@@ -135,7 +72,7 @@ p
     |  local file system. To use it with spaCy, simply assign it a name by
     |  creating a #[+a("#usage") shortcut link] for the data directory.
 
-+h(2, "usage") Using models with spaCy
++h(3, "usage") Using models with spaCy
 
 p
     |  To load a model, use #[+api("spacy#load") #[code spacy.load()]] with the
@@ -201,7 +138,7 @@ p
     |  privileges, the #[code spacy link] command may fail. The easiest solution
     |  is to re-run the command as admin, or use a #[code virtualenv]. For more
     |  info on this, see the
-    |  #[+a("/docs/usage/#symlink-privilege") troubleshooting guide].
+    |  #[+a("/usage/#symlink-privilege") troubleshooting guide].
 
 +h(3, "usage-import") Importing models as modules
 
@@ -227,15 +164,15 @@ p
     |  #[code spacy.load()].
 
 +infobox
-    |  For more details, see the usage guide on
-    |  #[+a("/docs/usage/production-use#models") working with models in production].
+    |  For more details, see the section on
+    |  #[+a("/models/#production") working with models in production].
 
-+h(2, "own-models") Using your own models
++h(3, "own-models") Using your own models
 
 p
     |  If you've trained your own model, for example for
-    |  #[+a("/docs/usage/adding-languages") additional languages] or
-    |  #[+a("/docs/usage/train-ner") custom named entities], you can save its
+    |  #[+a("/usage/adding-languages") additional languages] or
+    |  #[+a("/usage/training#ner") custom named entities], you can save its
     |  state using the #[+api("language#to_disk") #[code Language.to_disk()]]
     |  method. To make the model more convenient to deploy, we recommend
     |  wrapping it as a Python package.
@@ -243,4 +180,4 @@ p
 +infobox("Saving and loading models")
     |  For more information and a detailed guide on how to package your model,
     |  see the documentation on
-    |  #[+a("/docs/usage/saving-loading#models") saving and loading models].
+    |  #[+a("/usage/training#saving-loading") saving and loading models].
diff --git a/website/usage/_models/_production.jade b/website/usage/_models/_production.jade
new file mode 100644
index 000000000..43f4b1ba9
--- /dev/null
+++ b/website/usage/_models/_production.jade
@@ -0,0 +1,81 @@
+//- 💫 DOCS > USAGE > MODELS > PRODUCTION USE
+
+p
+    |  If your application depends on one or more models,
+    |  you'll usually want to integrate them into your continuous integration
+    |  workflow and build process. While spaCy provides a range of useful helpers
+    |  for downloading, linking and loading models, the underlying functionality
+    |  is entirely based on native Python packages. This allows your application
+    |  to handle a model like any other package dependency.
+
++infobox("Training models for production")
+    |  For an example of an automated model training and build process, see
+    |  #[+a("/usage/training#example-training-spacy") this example] of how
+    |  we're training and packaging our models for spaCy.
+
++h(3, "models-download") Downloading and requiring model dependencies
+
+p
+    |  spaCy's built-in #[+api("cli#download") #[code download]] command
+    |  is mostly intended as a convenient, interactive wrapper. It performs
+    |  compatibility checks and prints detailed error messages and warnings.
+    |  However, if you're downloading models as part of an automated build
+    |  process, this only adds an unnecessary layer of complexity. If you know
+    |  which models your application needs, you should be specifying them directly.
+
+p
+    |  Because all models are valid Python packages, you can add them to your
+    |  application's #[code requirements.txt]. If you're running your own
+    |  internal PyPi installation, you can simply upload the models there. pip's
+    |  #[+a("https://pip.pypa.io/en/latest/reference/pip_install/#requirements-file-format") requirements file format]
+    |  supports both package names to download via a PyPi server, as well as direct
+    |  URLs.
+
++code("requirements.txt", "text").
+    spacy&gt;=2.0.0,&lt;3.0.0
+    -e #{gh("spacy-models")}/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz#en_core_web_sm
+
+p
+    |  Specifying #[code #egg=] with the package name tells pip
+    |  which package to expect from the download URL. This way, the
+    |  package won't be re-downloaded and overwritten if it's already
+    |  installed - just like when you're downloading a package from PyPi.
+
+p
+    |  All models are versioned and specify their spaCy dependency. This ensures
+    |  cross-compatibility and lets you specify exact version requirements for
+    |  each model. If you've trained your own model, you can use the
+    |  #[+api("cli#package") #[code package]] command to generate the required
+    |  meta data and turn it into a loadable package.
+
+
++h(3, "models-loading") Loading and testing models
+
+p
+    |  Downloading models directly via pip won't call spaCy's link
+    |  #[+api("cli#link") #[code link]] command, which creates
+    |  symlinks for model shortcuts. This means that you'll have to run this
+    |  command separately, or use the native #[code import] syntax to load the
+    |  models:
+
++code.
+    import en_core_web_sm
+    nlp = en_core_web_sm.load()
+
+p
+    |  In general, this approach is recommended for larger code bases, as it's
+    |  more "native", and doesn't depend on symlinks or rely on spaCy's loader
+    |  to resolve string names to model packages. If a model can't be
+    |  imported, Python will raise an #[code ImportError] immediately. And if a
+    |  model is imported but not used, any linter will catch that.
+
+p
+    |  Similarly, it'll give you more flexibility when writing tests that
+    |  require loading models. For example, instead of writing your own
+    |  #[code try] and #[code except] logic around spaCy's loader, you can use
+    |  #[+a("http://pytest.readthedocs.io/en/latest/") pytest]'s
+    |  #[+a("https://docs.pytest.org/en/latest/builtin.html#_pytest.outcomes.importorskip") #[code importorskip()]]
+    |  method to only run a test if a specific model or model version is
+    |  installed. Each model package exposes a #[code __version__] attribute
+    |  which you can also use to perform your own version compatibility checks
+    |  before loading a model.
diff --git a/website/usage/_models/_quickstart.jade b/website/usage/_models/_quickstart.jade
new file mode 100644
index 000000000..c8f702cb4
--- /dev/null
+++ b/website/usage/_models/_quickstart.jade
@@ -0,0 +1,17 @@
+//- 💫 DOCS > USAGE > MODELS > QUICKSTART
+
+- QUICKSTART_MODELS[0].options = Object.keys(MODELS).map(m => ({ id: m, title: LANGUAGES[m], checked: m == 'en'}))
++quickstart(QUICKSTART_MODELS, "Quickstart", "Install a default model, get the code to load it from within spaCy and an example to test it. For more options, see the section on available models below.")
+    for models, lang in MODELS
+        - var package = models[0]
+        +qs({lang: lang}) spacy download #{lang}
+        +qs({lang: lang}, "divider")
+        +qs({lang: lang, load: "module"}, "python") import #{package}
+        +qs({lang: lang, load: "module"}, "python") nlp = #{package}.load()
+        +qs({lang: lang, load: "spacy"}, "python") nlp = spacy.load('#{lang}')
+        +qs({lang: lang, config: "example"}, "python") doc = nlp(u"#{EXAMPLE_SENTENCES[lang]}")
+
+        if lang != "xx"
+            +qs({lang: lang, config: "example"}, "python") print([(w.text, w.pos_) for w in doc])
+        else
+            +qs({lang: lang, config: "example"}, "python") print([(ent.text, ent.label) for ent in doc.ents])
diff --git a/website/usage/_processing-pipelines/_examples.jade b/website/usage/_processing-pipelines/_examples.jade
new file mode 100644
index 000000000..616bed32c
--- /dev/null
+++ b/website/usage/_processing-pipelines/_examples.jade
@@ -0,0 +1,126 @@
+//- 💫 DOCS > USAGE > PROCESSING PIPELINES > EXAMPLES
+
+p
+    |  To see real-world examples of pipeline factories and components in action,
+    |  you can have a look at the source of spaCy's built-in components, e.g.
+    |  the #[+api("tagger") #[code Tagger]], #[+api("parser") #[code Parser]] or
+    |  #[+api("entityrecognizer") #[code EntityRecongnizer]].
+
++h(3, "example1") Example: Custom sentence segmentation logic
+
+p
+    |  Let's say you want to implement custom logic to improve spaCy's sentence
+    |  boundary detection. Currently, sentence segmentation is based on the
+    |  dependency parse, which doesn't always produce ideal results. The custom
+    |  logic should therefore be applied #[strong after] tokenization, but
+    |  #[strong before] the dependency parsing – this way, the parser can also
+    |  take advantage of the sentence boundaries.
+
++code.
+    def sbd_component(doc):
+        for i, token in enumerate(doc[:-2]):
+            # define sentence start if period + titlecase token
+            if token.text == '.' and doc[i+1].is_title:
+                doc[i+1].sent_start = True
+        return doc
+
+p
+    |  In this case, we simply want to add the component to the existing
+    |  pipeline of the English model. We can do this by inserting it at index 0
+    |  of #[code nlp.pipeline]:
+
++code.
+    nlp = spacy.load('en')
+    nlp.pipeline.insert(0, sbd_component)
+
+p
+    |  When you call #[code nlp] on some text, spaCy will tokenize it to create
+    |  a #[code Doc] object, and first call #[code sbd_component] on it, followed
+    |  by the model's default pipeline.
+
++h(3, "example2") Example: Sentiment model
+
+p
+    |  Let's say you have trained your own document sentiment model on English
+    |  text. After tokenization, you want spaCy to first execute the
+    |  #[strong default tensorizer], followed by a custom
+    |  #[strong sentiment component] that adds a #[code .sentiment]
+    |  property to the #[code Doc], containing your model's sentiment precition.
+
+p
+    |  Your component class will have a #[code from_disk()] method that spaCy
+    |  calls to load the model data. When called, the component will compute
+    |  the sentiment score, add it to the #[code Doc] and return the modified
+    |  document. Optionally, the component can include an #[code update()] method
+    |  to allow training the model.
+
++code.
+    import pickle
+    from pathlib import Path
+
+    class SentimentComponent(object):
+        def __init__(self, vocab):
+            self.weights = None
+
+        def __call__(self, doc):
+            doc.sentiment = sum(self.weights*doc.vector) # set sentiment property
+            return doc
+
+        def from_disk(self, path): # path = model path + factory ID ('sentiment')
+            self.weights = pickle.load(Path(path) / 'weights.bin') # load weights
+            return self
+
+        def update(self, doc, gold): # update weights – allows training!
+            prediction = sum(self.weights*doc.vector)
+            self.weights -= 0.001*doc.vector*(prediction-gold.sentiment)
+
+p
+    |  The factory will initialise the component with the #[code Vocab] object.
+    |  To be able to add it to your model's pipeline as #[code 'sentiment'],
+    |  it also needs to be registered via
+    |  #[+api("spacy#set_factory") #[code set_factory()]].
+
++code.
+    def sentiment_factory(vocab):
+        component = SentimentComponent(vocab) # initialise component
+        return component
+
+    spacy.set_factory('sentiment', sentiment_factory)
+
+p
+    |  The above code should be #[strong shipped with your model]. You can use
+    |  the #[+api("cli#package") #[code package]] command to create all required
+    |  files and directories. The model package will include an
+    |  #[+src(gh("spacy-dev-resources", "templates/model/en_model_name/__init__.py")) #[code __init__.py]]
+    |  with a #[code load()] method, that will initialise the language class with
+    |  the model's pipeline and call the #[code from_disk()] method to load
+    |  the model data.
+
+p
+    |  In the model package's meta.json, specify the language class and pipeline
+    |  IDs:
+
++code("meta.json (excerpt)", "json").
+    {
+        "name": "sentiment_model",
+        "lang": "en",
+        "version": "1.0.0",
+        "spacy_version": "&gt;=2.0.0,&lt;3.0.0",
+        "pipeline": ["tensorizer", "sentiment"]
+    }
+
+p
+    |  When you load your new model, spaCy will call the model's #[code load()]
+    |  method. This will return a #[code Language] object with a pipeline
+    |  containing the default tensorizer, and the sentiment component returned
+    |  by your custom #[code "sentiment"] factory.
+
++code.
+    nlp = spacy.load('en_sentiment_model')
+    doc = nlp(u'I love pizza')
+    assert doc.sentiment
+
++infobox("Saving and loading models")
+    |  For more information and a detailed guide on how to package your model,
+    |  see the documentation on
+    |  #[+a("/usage/training#saving-loading") saving and loading models].
diff --git a/website/usage/_processing-pipelines/_multithreading.jade b/website/usage/_processing-pipelines/_multithreading.jade
new file mode 100644
index 000000000..1e08508b8
--- /dev/null
+++ b/website/usage/_processing-pipelines/_multithreading.jade
@@ -0,0 +1,40 @@
+//- 💫 DOCS > USAGE > PROCESSING PIPELINES > MULTI-THREADING
+
+p
+    |  If you have a sequence of documents to process, you should use the
+    |  #[+api("language#pipe") #[code Language.pipe()]] method. The method takes
+    |  an iterator of texts, and accumulates an internal buffer,
+    |  which it works on in parallel. It then yields the documents in order,
+    |  one-by-one. After a long and bitter struggle, the global interpreter
+    |  lock was freed around spaCy's main parsing loop in v0.100.3. This means
+    |  that #[code .pipe()] will be significantly faster in most
+    |  practical situations, because it allows shared memory parallelism.
+
++code.
+    for doc in nlp.pipe(texts, batch_size=10000, n_threads=3):
+       pass
+
+p
+    |  To make full use of the #[code .pipe()] function, you might want to
+    |  brush up on #[strong Python generators]. Here are a few quick hints:
+
++list
+    +item
+        |  Generator comprehensions can be written as
+        |  #[code (item for item in sequence)].
+
+    +item
+        |  The
+        |  #[+a("https://docs.python.org/2/library/itertools.html") #[code itertools] built-in library]
+        |  and the
+        |  #[+a("https://github.com/pytoolz/cytoolz") #[code cytoolz] package]
+        |  provide a lot of handy #[strong generator tools].
+
+    +item
+        |  Often you'll have an input stream that pairs text with some
+        |  important meta data, e.g. a JSON document. To
+        |  #[strong pair up the meta data] with the processed #[code Doc]
+        |  object, you should use the #[code itertools.tee] function to split
+        |  the generator in two, and then #[code izip] the extra stream to the
+        |  document stream. Here's
+        |  #[+a(gh("spacy") + "/issues/172#issuecomment-183963403") an example].
diff --git a/website/docs/usage/language-processing-pipeline.jade b/website/usage/_processing-pipelines/_pipelines.jade
similarity index 56%
rename from website/docs/usage/language-processing-pipeline.jade
rename to website/usage/_processing-pipelines/_pipelines.jade
index 03f6c28f5..d09ed4ead 100644
--- a/website/docs/usage/language-processing-pipeline.jade
+++ b/website/usage/_processing-pipelines/_pipelines.jade
@@ -1,12 +1,4 @@
-//- 💫 DOCS > USAGE > PIPELINE
-
-include ../../_includes/_mixins
-
-+h(2, "101") Pipelines 101
-
-include _spacy-101/_pipelines
-
-+h(2, "pipelines") How pipelines work
+//- 💫 DOCS > USAGE > PROCESSING PIPELINES > PIPELINES
 
 p
     |  spaCy makes it very easy to create your own pipelines consisting of
@@ -15,11 +7,11 @@ p
     |  functions. A pipeline component can be added to an already existing
     |  #[code nlp] object, specified when initialising a #[code Language] class,
     |  or defined within a
-    |  #[+a("/docs/usage/saving-loading#models-generating") model package].
+    |  #[+a("/usage/saving-loading#models-generating") model package].
 
 p
     |  When you load a model, spaCy first consults the model's
-    |  #[+a("/docs/usage/saving-loading#models-generating") meta.json]. The
+    |  #[+a("/usage/saving-loading#models-generating") meta.json]. The
     |  meta typically includes the model details, the ID of a language class,
     |  and an optional list of pipeline components. spaCy then does the
     |  following:
@@ -29,7 +21,7 @@ p
         "name": "example_model",
         "lang": "en"
         "description": "Example model for spaCy",
-        "pipeline": ["token_vectors", "tagger"]
+        "pipeline": ["tensorizer", "tagger"]
     }
 
 +list("numbers")
@@ -56,24 +48,50 @@ p
 
 p
     | ... the model tells spaCy to use the pipeline
-    |  #[code ["tensorizer", "tagger", "parser", "ner"]]. spaCy will then look
-    |  up each string in its internal factories registry and initialise the
-    |  individual components. It'll then load #[code spacy.lang.en.English],
-    |  pass it the path to the model's data directory, and return it for you
-    |  to use as the #[code nlp] object.
+    |  #[code.u-break ["tensorizer", "tagger", "parser", "ner"]]. spaCy will
+    |  then look up each string in its internal factories registry and
+    |  initialise the individual components. It'll then load
+    |  #[code spacy.lang.en.English], pass it the path to the model's data
+    |  directory, and return it for you to use as the #[code nlp] object.
+
+p
+    |  Fundamentally, a #[+a("/models") spaCy model] consists of three
+    |  components: #[strong the weights], i.e. binary data loaded in from a
+    |  directory, a #[strong pipeline] of functions called in order,
+    |  and #[strong language data] like the tokenization rules and annotation
+    |  scheme. All of this is specific to each model, and defined in the
+    |  model's #[code meta.json] – for example, a Spanish NER model requires
+    |  different weights, language data and pipeline components than an English
+    |  parsing and tagging model. This is also why the pipeline state is always
+    |  held by the #[code Language] class.
+    |  #[+api("spacy#load") #[code spacy.load]] puts this all together and
+    |  returns an instance of #[code Language]  with a pipeline set and access
+    |  to the binary data:
+
++code("spacy.load under the hood").
+    lang = 'en'
+    pipeline = ['tensorizer', 'tagger', 'parser', 'ner']
+    data_path = 'path/to/en_core_web_sm/en_core_web_sm-2.0.0'
+
+    cls = spacy.util.get_lang_class(lang)  # 1. get Language instance, e.g. English()
+    nlp = cls(pipeline=pipeline)           # 2. initialise it with the pipeline
+    nlp.from_disk(model_data_path)         # 3. load in the binary data
 
 p
     |  When you call #[code nlp] on a text, spaCy will #[strong tokenize] it and
     |  then #[strong call each component] on the #[code Doc], in order.
-    |  Components all return the modified document, which is then processed by
-    |  the component next in the pipeline.
+    |  Since the model data is loaded, the components can access it to assign
+    |  annotations to the #[code Doc] object, and subsequently to the
+    |  #[code Token] and #[code Span] which are only views of the #[code Doc],
+    |  and don't own any data themselves. All components return the modified
+    |  document, which is then processed by the component next in the pipeline.
 
 +code("The pipeline under the hood").
     doc = nlp.make_doc(u'This is a sentence')
     for proc in nlp.pipeline:
         doc = proc(doc)
 
-+h(2, "creating") Creating pipeline components and factories
++h(3, "creating") Creating pipeline components and factories
 
 p
     |  spaCy lets you customise the pipeline with your own components. Components
@@ -82,7 +100,7 @@ p
     |  pipeline. You can do that by defining and registering a factory which
     |  receives the shared #[code Vocab] object and returns a component.
 
-+h(3, "creating-component") Creating a  component
++h(4, "creating-component") Creating a  component
 
 p
     |  A component receives a #[code Doc] object and
@@ -103,7 +121,7 @@ p
         +cell #[code Doc]
         +cell The #[code Doc] object processed by the previous component.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Doc]
         +cell The #[code Doc] object processed by this pipeline component.
@@ -123,7 +141,7 @@ p
     nlp = spacy.load('en')
     nlp.pipeline.append(my_component)
 
-+h(3, "creating-factory") Creating a factory
++h(4, "creating-factory") Creating a factory
 
 p
     |  A factory is a #[strong function that returns a pipeline component].
@@ -149,7 +167,7 @@ p
             |  Shared data between components, including strings, morphology,
             |  vectors etc.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell callable
         +cell The pipeline component.
@@ -171,148 +189,22 @@ p
     |  by looking it up in the available factories. The factory will then be
     |  initialised with the #[code Vocab]. Providing factory names instead of
     |  callables also makes it easy to specify them in the model's
-    |  #[+a("/docs/usage/saving-loading#models-generating") meta.json]. If you're
+    |  #[+a("/usage/saving-loading#models-generating") meta.json]. If you're
     |  training your own model and want to use one of spaCy's default components,
     |  you won't have to worry about finding and implementing it either – to use
     |  the default tagger, simply add #[code "tagger"] to the pipeline, and
     |  #[strong spaCy will know what to do].
 
-
 +infobox("Important note")
     |  Because factories are #[strong resolved on initialisation] of the
     |  #[code Language] class, it's #[strong not possible] to add them to the
     |  pipeline afterwards, e.g. by modifying #[code nlp.pipeline]. This only
     |  works with individual component functions. To use factories, you need to
     |  create a new #[code Language] object, or generate a
-    |  #[+a("/docs/usage/saving-loading#models-generating") model package] with
+    |  #[+a("/usage/training#models-generating") model package] with
     |  a custom pipeline.
 
-+aside("Real-world examples")
-    |  To see real-world examples of pipeline factories and components in action,
-    |  you can have a look at the source of spaCy's built-in components, e.g.
-    |  the #[+api("tagger") #[code Tagger]], #[+api("parser") #[code Parser]] or
-    |  #[+api("entityrecognizer") #[code EntityRecongnizer]].
-
-+h(2, "example1") Example: Custom sentence segmentation logic
-
-p
-    |  Let's say you want to implement custom logic to improve spaCy's sentence
-    |  boundary detection. Currently, sentence segmentation is based on the
-    |  dependency parse, which doesn't always produce ideal results. The custom
-    |  logic should therefore be applied #[strong after] tokenization, but
-    |  #[strong before] the dependency parsing – this way, the parser can also
-    |  take advantage of the sentence boundaries.
-
-+code.
-    def sbd_component(doc):
-        for i, token in enumerate(doc[:-2]):
-            # define sentence start if period + titlecase token
-            if token.text == '.' and doc[i+1].is_title:
-                doc[i+1].sent_start = True
-        return doc
-
-p
-    |  In this case, we simply want to add the component to the existing
-    |  pipeline of the English model. We can do this by inserting it at index 0
-    |  of #[code nlp.pipeline]:
-
-+code.
-    nlp = spacy.load('en')
-    nlp.pipeline.insert(0, sbd_component)
-
-p
-    |  When you call #[code nlp] on some text, spaCy will tokenize it to create
-    |  a #[code Doc] object, and first call #[code sbd_component] on it, followed
-    |  by the model's default pipeline.
-
-+h(2, "example2") Example: Sentiment model
-
-p
-    |  Let's say you have trained your own document sentiment model on English
-    |  text. After tokenization, you want spaCy to first execute the
-    |  #[strong default tensorizer], followed by a custom
-    |  #[strong sentiment component] that adds a #[code .sentiment]
-    |  property to the #[code Doc], containing your model's sentiment precition.
-
-p
-    |  Your component class will have a #[code from_disk()] method that spaCy
-    |  calls to load the model data. When called, the component will compute
-    |  the sentiment score, add it to the #[code Doc] and return the modified
-    |  document. Optionally, the component can include an #[code update()] method
-    |  to allow training the model.
-
-+code.
-    import pickle
-    from pathlib import Path
-
-    class SentimentComponent(object):
-        def __init__(self, vocab):
-            self.weights = None
-
-        def __call__(self, doc):
-            doc.sentiment = sum(self.weights*doc.vector) # set sentiment property
-            return doc
-
-        def from_disk(self, path): # path = model path + factory ID ('sentiment')
-            self.weights = pickle.load(Path(path) / 'weights.bin') # load weights
-            return self
-
-        def update(self, doc, gold): # update weights – allows training!
-            prediction = sum(self.weights*doc.vector)
-            self.weights -= 0.001*doc.vector*(prediction-gold.sentiment)
-
-p
-    |  The factory will initialise the component with the #[code Vocab] object.
-    |  To be able to add it to your model's pipeline as #[code 'sentiment'],
-    |  it also needs to be registered via
-    |  #[+api("spacy#set_factory") #[code set_factory()]].
-
-+code.
-    def sentiment_factory(vocab):
-        component = SentimentComponent(vocab) # initialise component
-        return component
-
-    spacy.set_factory('sentiment', sentiment_factory)
-
-p
-    |  The above code should be #[strong shipped with your model]. You can use
-    |  the #[+api("cli#package") #[code package]] command to create all required
-    |  files and directories. The model package will include an
-    |  #[+src(gh("spacy-dev-resources", "templates/model/en_model_name/__init__.py")) __init__.py]
-    |  with a #[code load()] method, that will initialise the language class with
-    |  the model's pipeline and call the #[code from_disk()] method to load
-    |  the model data.
-
-p
-    |  In the model package's meta.json, specify the language class and pipeline
-    |  IDs:
-
-+code("meta.json (excerpt)", "json").
-    {
-        "name": "sentiment_model",
-        "lang": "en",
-        "version": "1.0.0",
-        "spacy_version": "&gt;=2.0.0,&lt;3.0.0",
-        "pipeline": ["tensorizer", "sentiment"]
-    }
-
-p
-    |  When you load your new model, spaCy will call the model's #[code load()]
-    |  method. This will return a #[code Language] object with a pipeline
-    |  containing the default tensorizer, and the sentiment component returned
-    |  by your custom #[code "sentiment"] factory.
-
-+code.
-    nlp = spacy.load('en_sentiment_model')
-    doc = nlp(u'I love pizza')
-    assert doc.sentiment
-
-+infobox("Saving and loading models")
-    |  For more information and a detailed guide on how to package your model,
-    |  see the documentation on
-    |  #[+a("/docs/usage/saving-loading#models") saving and loading models].
-
-+h(2, "disabling") Disabling pipeline components
++h(3, "disabling") Disabling pipeline components
 
 p
     |  If you don't need a particular component of the pipeline – for
diff --git a/website/usage/_processing-pipelines/_serialization.jade b/website/usage/_processing-pipelines/_serialization.jade
new file mode 100644
index 000000000..e29cbc558
--- /dev/null
+++ b/website/usage/_processing-pipelines/_serialization.jade
@@ -0,0 +1,38 @@
+//- 💫 DOCS > USAGE > PROCESSING PIPELINES > SERIALIZATION
+
+include ../_spacy-101/_serialization
+
++infobox("Important note")
+    |  In spaCy v2.0, the API for saving and loading has changed to only use the
+    |  four methods listed above consistently across objects and classes. For an
+    |  overview of the changes, see #[+a("/usage/v2#incompat") this table]
+    |  and the notes on #[+a("/usage/v2#migrating-saving-loading") migrating].
+
++h(3, "example-doc") Example: Saving and loading a document
+
+p
+    |  For simplicity, let's assume you've
+    |  #[+a("/usage/entity-recognition#setting") added custom entities] to
+    |  a #[code Doc], either manually, or by using a
+    |  #[+a("/usage/rule-based-matching#on_match") match pattern]. You can
+    |  save it locally by calling #[+api("doc#to_disk") #[code Doc.to_disk()]],
+    |  and load it again via #[+api("doc#from_disk") #[code Doc.from_disk()]].
+    |  This will overwrite the existing object and return it.
+
++code.
+    import spacy
+    from spacy.tokens import Span
+
+    text = u'Netflix is hiring a new VP of global policy'
+
+    nlp = spacy.load('en')
+    doc = nlp(text)
+    assert len(doc.ents) == 0 # Doc has no entities
+    doc.ents += ((Span(doc, 0, 1, label=doc.vocab.strings[u'ORG'])) # add entity
+    doc.to_disk('/path/to/doc') # save Doc to disk
+
+    new_doc = nlp(text)
+    assert len(new_doc.ents) == 0 # new Doc has no entities
+    new_doc = new_doc.from_disk('path/to/doc') # load from disk and overwrite
+    assert len(new_doc.ents) == 1 # entity is now recognised!
+    assert [(ent.text, ent.label_) for ent in new_doc.ents] == [(u'Netflix', u'ORG')]
diff --git a/website/usage/_processing-pipelines/_user-hooks.jade b/website/usage/_processing-pipelines/_user-hooks.jade
new file mode 100644
index 000000000..e7dce53fe
--- /dev/null
+++ b/website/usage/_processing-pipelines/_user-hooks.jade
@@ -0,0 +1,61 @@
+//- 💫 DOCS > USAGE > PROCESSING PIPELINES > ATTRIBUTE HOOKS
+
+p
+    |  Hooks let you customize some of the behaviours of the #[code Doc],
+    |  #[code Span] or #[code Token] objects by adding a component to the
+    |  pipeline. For instance, to customize the
+    |  #[+api("doc#similarity") #[code Doc.similarity]] method, you can add a
+    |  component that sets a custom function to
+    |  #[code doc.user_hooks['similarity']]. The built-in #[code Doc.similarity]
+    |  method will check the #[code user_hooks] dict, and delegate to your
+    |  function if you've set one. Similar results can be achieved by setting
+    |  functions to #[code Doc.user_span_hooks] and #[code Doc.user_token_hooks].
+
++code("Polymorphic similarity example").
+    span.similarity(doc)
+    token.similarity(span)
+    doc1.similarity(doc2)
+
+p
+    |  By default, this just averages the vectors for each document, and
+    |  computes their cosine. Obviously, spaCy should make it easy for you to
+    |  install your own similarity model. This introduces a tricky design
+    |  challenge. The current solution is to add three more dicts to the
+    |  #[code Doc] object:
+
++aside("Implementation note")
+    |  The hooks live on the #[code Doc] object because the #[code Span] and
+    |  #[code Token] objects are created lazily, and don't own any data. They
+    |  just proxy to their parent #[code Doc]. This turns out to be convenient
+    |  here — we only have to worry about installing hooks in one place.
+
++table(["Name", "Description"])
+    +row
+        +cell #[code user_hooks]
+        +cell Customise behaviour of #[code doc.vector], #[code doc.has_vector], #[code doc.vector_norm] or #[code doc.sents]
+
+    +row
+        +cell #[code user_token_hooks]
+        +cell Customise behaviour of #[code token.similarity], #[code token.vector], #[code token.has_vector], #[code token.vector_norm] or #[code token.conjuncts]
+
+    +row
+        +cell #[code user_span_hooks]
+        +cell Customise behaviour of #[code span.similarity], #[code span.vector], #[code span.has_vector], #[code span.vector_norm] or #[code span.root]
+
+p
+    |  To sum up, here's an example of hooking in custom #[code .similarity()]
+    |  methods:
+
++code("Add custom similarity hooks").
+    class SimilarityModel(object):
+        def __init__(self, model):
+            self._model = model
+
+        def __call__(self, doc):
+            doc.user_hooks['similarity'] = self.similarity
+            doc.user_span_hooks['similarity'] = self.similarity
+            doc.user_token_hooks['similarity'] = self.similarity
+
+        def similarity(self, obj1, obj2):
+            y = self._model([obj1.vector, obj2.vector])
+            return float(y[0])
diff --git a/website/docs/usage/_spacy-101/_architecture.jade b/website/usage/_spacy-101/_architecture.jade
similarity index 83%
rename from website/docs/usage/_spacy-101/_architecture.jade
rename to website/usage/_spacy-101/_architecture.jade
index c5a85f0b0..c9b299036 100644
--- a/website/docs/usage/_spacy-101/_architecture.jade
+++ b/website/usage/_spacy-101/_architecture.jade
@@ -20,18 +20,12 @@ p
     |  returning an #[strong annotated document]. It also orchestrates training
     |  and serialization.
 
-+image
-    include ../../../assets/img/docs/architecture.svg
-    .u-text-right
-        +button("/assets/img/docs/architecture.svg", false, "secondary").u-text-tag View large graphic
++graphic("/assets/img/architecture.svg")
+    include ../../assets/img/architecture.svg
+
++h(3, "architecture-containers") Container objects
 
 +table(["Name", "Description"])
-    +row
-        +cell #[+api("language") #[code Language]]
-        +cell
-            |  A text-processing pipeline. Usually you'll load this once per
-            |  process as #[code nlp] and pass the instance around your application.
-
     +row
         +cell #[+api("doc") #[code Doc]]
         +cell A container for accessing linguistic annotations.
@@ -53,43 +47,25 @@ p
             |  opposed to a word token. It therefore has no part-of-speech tag,
             |  dependency parse etc.
 
-    +row
-        +cell #[+api("vocab") #[code Vocab]]
-        +cell
-            |  A lookup table for the vocabulary that allows you to access
-            |  #[code Lexeme] objects.
-
-    +row
-        +cell #[code Morphology]
-        +cell
-            |  Assign linguistic features like lemmas, noun case, verb tense etc.
-            |  based on the word and its part-of-speech tag.
-
-    +row
-        +cell #[+api("stringstore") #[code StringStore]]
-        +cell Map strings to and from hash values.
-
-    +row
-        +cell #[+api("tokenizer") #[code Tokenizer]]
-        +cell
-            |  Segment text, and create #[code Doc] objects with the discovered
-            |  segment boundaries.
-
-    +row
-        +cell #[code Lemmatizer]
-        +cell
-            |  Determine the base forms of words.
-
-    +row
-        +cell #[+api("matcher") #[code Matcher]]
-        +cell
-            |  Match sequences of tokens, based on pattern rules, similar to
-            |  regular expressions.
-
-
-+h(3, "architecture-pipeline") Pipeline components
++h(3, "architecture-pipeline") Processing pipeline
 
 +table(["Name", "Description"])
+    +row
+        +cell #[+api("language") #[code Language]]
+        +cell
+            |  A text-processing pipeline. Usually you'll load this once per
+            |  process as #[code nlp] and pass the instance around your application.
+
+    +row
+        +cell #[+api("pipe") #[code Pipe]]
+        +cell Base class for processing pipeline components.
+
+    +row
+        +cell #[+api("tensorizer") #[code Tensorizer]]
+        +cell
+            |  Add tensors with position-sensitive meaning representations to
+            |  #[code Doc] objects.
+
     +row
         +cell #[+api("tagger") #[code Tagger]]
         +cell Annotate part-of-speech tags on #[code Doc] objects.
@@ -104,16 +80,54 @@ p
             |  Annotate named entities, e.g. persons or products, on #[code Doc]
             |  objects.
 
+    +row
+        +cell #[+api("textcategorizer") #[code TextCategorizer]]
+        +cell  Assigning categories or labels to #[code Doc] objects.
+
+    +row
+        +cell #[+api("tokenizer") #[code Tokenizer]]
+        +cell
+            |  Segment text, and create #[code Doc] objects with the discovered
+            |  segment boundaries.
+
+    +row
+        +cell #[+api("lemmatizer") #[code Lemmatizer]]
+        +cell
+            |  Determine the base forms of words.
+
+    +row
+        +cell #[code Morphology]
+        +cell
+            |  Assign linguistic features like lemmas, noun case, verb tense etc.
+            |  based on the word and its part-of-speech tag.
+
+    +row
+        +cell #[+api("matcher") #[code Matcher]]
+        +cell
+            |  Match sequences of tokens, based on pattern rules, similar to
+            |  regular expressions.
+
+    +row
+        +cell #[+api("phrasematcher") #[code PhraseMatcher]]
+        +cell Match sequences of tokens based on phrases.
+
+
 +h(3, "architecture-other") Other classes
 
 +table(["Name", "Description"])
     +row
-        +cell #[+api("vectors") #[code Vectors]]
-        +cell Container class for vector data keyed by string.
+        +cell #[+api("vocab") #[code Vocab]]
+        +cell
+            |  A lookup table for the vocabulary that allows you to access
+            |  #[code Lexeme] objects.
 
     +row
-        +cell #[+api("binder") #[code Binder]]
-        +cell Container class for serializing collections of #[code Doc] objects.
+        +cell #[+api("stringstore") #[code StringStore]]
+        +cell Map strings to and from hash values.
+
+    +row
+        +cell #[+api("vectors") #[code Vectors]]
+        +cell Container class for vector data keyed by string.
 
     +row
         +cell #[+api("goldparse") #[code GoldParse]]
@@ -124,3 +138,7 @@ p
         +cell
             |  An annotated corpus, using the JSON file format. Manages
             |  annotations for tagging, dependency parsing and NER.
+
+    +row
+        +cell #[+api("binder") #[code Binder]]
+        +cell Container class for serializing collections of #[code Doc] objects.
diff --git a/website/usage/_spacy-101/_community-faq.jade b/website/usage/_spacy-101/_community-faq.jade
new file mode 100644
index 000000000..f91248bfd
--- /dev/null
+++ b/website/usage/_spacy-101/_community-faq.jade
@@ -0,0 +1,141 @@
+//- 💫 DOCS > USAGE > SPACY 101 > COMMUNITY & FAQ
+
+p
+    |  We're very happy to see the spaCy community grow and include a mix of
+    |  people from all kinds of different backgrounds – computational
+    |  linguistics, data science, deep learning, research and more. If you'd
+    |  like to get involved, below are some answers to the most important
+    |  questions and resources for further reading.
+
++h(3, "faq-help-code") Help, my code isn't working!
+
+p
+    |  Bugs suck, and we're doing our best to continuously improve the tests
+    |  and fix bugs as soon as possible. Before you submit an issue, do a
+    |  quick search and check if the problem has already been reported. If
+    |  you're having installation or loading problems, make sure to also check
+    |  out the #[+a("/usage/#troubleshooting") troubleshooting guide]. Help
+    |  with spaCy is available via the following platforms:
+
++aside("How do I know if something is a bug?")
+    |  Of course, it's always hard to know for sure, so don't worry – we're not
+    |  going to be mad if a bug report turns out to be a typo in your
+    |  code. As a simple rule, any C-level error without a Python traceback,
+    |  like a #[strong segmentation fault] or #[strong memory error],
+    |  is #[strong always] a spaCy bug.#[br]#[br]
+    |  Because models are statistical, their performance will never be
+    |  #[em perfect]. However, if you come across
+    |  #[strong patterns that might indicate an underlying issue], please do
+    |  file a report. Similarly, we also care about behaviours that
+    |  #[strong contradict our docs].
+
++table(["Platform", "Purpose"])
+    +row
+        +cell #[+a("https://stackoverflow.com/questions/tagged/spacy") StackOverflow]
+        +cell
+            |  #[strong Usage questions] and everything related to problems with
+            |  your specific code. The StackOverflow community is much larger
+            |  than ours, so if your problem can be solved by others, you'll
+            |  receive help much quicker.
+
+    +row
+        +cell #[+a("https://gitter.im/" + SOCIAL.gitter) Gitter chat]
+        +cell
+            |  #[strong General discussion] about spaCy, meeting other community
+            |  members and exchanging #[strong tips, tricks and best practices].
+            |  If we're working on experimental models and features, we usually
+            |  share them on Gitter first.
+
+    +row
+        +cell #[+a(gh("spaCy") + "/issues") GitHub issue tracker]
+        +cell
+            |  #[strong Bug reports] and #[strong improvement suggestions], i.e.
+            |  everything that's likely spaCy's fault. This also includes
+            |  problems with the models beyond statistical imprecisions, like
+            |  patterns that point to a bug.
+
++infobox
+    |  Please understand that we won't be able to provide individual support via
+    |  email. We also believe that help is much more valuable if it's shared
+    |  publicly, so that #[strong more people can benefit from it]. If you come
+    |  across an issue and you think you might be able to help, consider posting
+    |  a quick update with your solution. No matter how simple, it can easily
+    |  save someone a lot of time and headache – and the next time you need help,
+    |  they might repay the favour.
+
++h(3, "faq-contributing") How can I contribute to spaCy?
+
+p
+    |  You don't have to be an NLP expert or Python pro to contribute, and we're
+    |  happy to help you get started. If you're new to spaCy, a good place to
+    |  start is the
+    |  #[+a(gh("spaCy") + '/issues?q=is%3Aissue+is%3Aopen+label%3A"help+wanted+%28easy%29"') #[code help wanted (easy)] label]
+    |  on GitHub, which we use to tag bugs and feature requests that are easy
+    |  and self-contained. We also appreciate contributions to the docs – whether
+    |  it's fixing a typo, improving an example or adding additional explanations.
+    |  You'll find a "Suggest edits" link at the bottom of each page that points
+    |  you to the source.
+
+p
+    |  Another way of getting involved is to help us improve the
+    |  #[+a("/usage/adding-languages#language-data") language data] –
+    |  especially if you happen to speak one of the languages currently in
+    |  #[+a("/usage/models#languages") alpha support]. Even
+    |  adding simple tokenizer exceptions, stop words or lemmatizer data
+    |  can make a big difference. It will also make it easier for us to provide
+    |  a statistical model for the language in the future. Submitting a test
+    |  that documents a bug or performance issue, or covers functionality that's
+    |  especially important for your application is also very helpful. This way,
+    |  you'll also make sure we never accidentally introduce regressions to the
+    |  parts of the library that you care about the most.
+
+p
+    strong
+        |  For more details on the types of contributions we're looking for, the
+        |  code conventions and other useful tips, make sure to check out the
+        |  #[+a(gh("spaCy", "CONTRIBUTING.md")) contributing guidelines].
+
++infobox("Code of Conduct")
+    |  spaCy adheres to the
+    |  #[+a("http://contributor-covenant.org/version/1/4/") Contributor Covenant Code of Conduct].
+    |  By participating, you are expected to uphold this code.
+
++h(3, "faq-project-with-spacy")
+    |  I've built something cool with spaCy – how can I get the word out?
+
+p
+    |  First, congrats – we'd love to check it out! When you share your
+    |  project on Twitter, don't forget to tag
+    |  #[+a("https://twitter.com/" + SOCIAL.twitter) @#{SOCIAL.twitter}] so we
+    |  don't miss it. If you think your project would be a good fit for the
+    |  #[+a("/usage/resources") resources], #[strong feel free to submit it!]
+    |  Tutorials are also incredibly valuable to other users and a great way to
+    |  get exposure. So we strongly encourage #[strong writing up your experiences],
+    |  or sharing your code and some tips and tricks on your blog. Since our
+    |  website is open-source, you can add your project or tutorial by making a
+    |  pull request on GitHub.
+
++aside("Contributing to spacy.io")
+    |  All showcase and tutorial links are stored in a
+    |  #[+a(gh("spaCy", "website/usage/_data.json")) JSON file], so you
+    |  won't even have to edit any markup. For more info on how to submit
+    |  your project, see the
+    |  #[+a(gh("spaCy", "CONTRIBUTING.md#submitting-a-project-to-the-showcase")) contributing guidelines]
+    |  and our #[+a(gh("spaCy", "website")) website docs].
+
+p
+    |  If you would like to use the spaCy logo on your site, please get in touch
+    |  and ask us first. However, if you want to show support and tell others
+    |  that your project is using spaCy, you can grab one of our
+    |  #[strong spaCy badges] here:
+
+- SPACY_BADGES =  ["built%20with-spaCy-09a3d5.svg", "made%20with%20❤%20and-spaCy-09a3d5.svg", "spaCy-v2-09a3d5.svg"]
++quickstart([{id: "badge", input_style: "check", options: SPACY_BADGES.map(function(badge, i) { return {id: i, title: "<img class='o-icon' src='https://img.shields.io/badge/" + badge + "' height='20'/>", checked: (i == 0) ? true : false}}) }], false, false, true)
+    .c-code-block(data-qs-results)
+        for badge, i in SPACY_BADGES
+            - var url = "https://img.shields.io/badge/" + badge
+            +code(false, "text", false, false, "star").o-no-block(data-qs-badge=i)=url
+            +code(false, "text", false, false, "code").o-no-block(data-qs-badge=i).
+                &lt;a href="#{SITE_URL}"&gt;&lt;img src="#{url}" height="20"&gt;&lt;/a&gt;
+            +code(false, "text", false, false, "markdown").o-no-block(data-qs-badge=i).
+                [![spaCy](#{url})](#{SITE_URL})
diff --git a/website/docs/usage/_spacy-101/_language-data.jade b/website/usage/_spacy-101/_language-data.jade
similarity index 86%
rename from website/docs/usage/_spacy-101/_language-data.jade
rename to website/usage/_spacy-101/_language-data.jade
index 1f75b47e8..628152524 100644
--- a/website/docs/usage/_spacy-101/_language-data.jade
+++ b/website/usage/_spacy-101/_language-data.jade
@@ -5,7 +5,7 @@ p
     |  #[strong exceptions and special cases], especially amongst the most
     |  common words. Some of these exceptions are shared across languages, while
     |  others are #[strong entirely specific] – usually so specific that they need
-    |  to be hard-coded. The #[+src(gh("spaCy", "spacy/lang")) lang] module
+    |  to be hard-coded. The #[+src(gh("spaCy", "spacy/lang")) #[code lang]] module
     |  contains all language-specific data, organised in simple Python files.
     |  This makes the data easy to update and extend.
 
@@ -27,15 +27,13 @@ p
     nlp_en = English() # includes English data
     nlp_de = German() # includes German data
 
-+image
-    include ../../../assets/img/docs/language_data.svg
-    .u-text-right
-        +button("/assets/img/docs/language_data.svg", false, "secondary").u-text-tag View large graphic
++graphic("/assets/img/language_data.svg")
+    include ../../assets/img/language_data.svg
 
 +table(["Name", "Description"])
     +row
         +cell #[strong Stop words]#[br]
-            |  #[+src(gh("spacy-dev-resources", "templates/new_language/stop_words.py")) stop_words.py]
+            |  #[+src(gh("spacy-dev-resources", "templates/new_language/stop_words.py")) #[code stop_words.py]]
         +cell
             |  List of most common words of a language that are often useful to
             |  filter out, for example "and" or "I". Matching tokens will
@@ -43,21 +41,21 @@ p
 
     +row
         +cell #[strong Tokenizer exceptions]#[br]
-            |  #[+src(gh("spacy-dev-resources", "templates/new_language/tokenizer_exceptions.py")) tokenizer_exceptions.py]
+            |  #[+src(gh("spacy-dev-resources", "templates/new_language/tokenizer_exceptions.py")) #[code tokenizer_exceptions.py]]
         +cell
             |  Special-case rules for the tokenizer, for example, contractions
             |  like "can't" and abbreviations with punctuation, like "U.K.".
 
     +row
         +cell #[strong Norm exceptions]
-            |  #[+src(gh("spaCy", "spacy/lang/norm_exceptions.py")) norm_exceptions.py]
+            |  #[+src(gh("spaCy", "spacy/lang/norm_exceptions.py")) #[code norm_exceptions.py]]
         +cell
             |  Special-case rules for normalising tokens to improve the model's
             |  predictions, for example on American vs. British spelling.
 
     +row
         +cell #[strong Punctuation rules]
-            |  #[+src(gh("spaCy", "spacy/lang/punctuation.py")) punctuation.py]
+            |  #[+src(gh("spaCy", "spacy/lang/punctuation.py")) #[code punctuation.py]]
         +cell
             |  Regular expressions for splitting tokens, e.g. on punctuation or
             |  special characters like emoji. Includes rules for prefixes,
@@ -65,14 +63,14 @@ p
 
     +row
         +cell #[strong Character classes]
-            |  #[+src(gh("spaCy", "spacy/lang/char_classes.py")) char_classes.py]
+            |  #[+src(gh("spaCy", "spacy/lang/char_classes.py")) #[code char_classes.py]]
         +cell
             |  Character classes to be used in regular expressions, for example,
             |  latin characters, quotes, hyphens or icons.
 
     +row
         +cell #[strong Lexical attributes]
-            |  #[+src(gh("spacy-dev-resources", "templates/new_language/lex_attrs.py")) lex_attrs.py]
+            |  #[+src(gh("spacy-dev-resources", "templates/new_language/lex_attrs.py")) #[code lex_attrs.py]]
         +cell
             |  Custom functions for setting lexical attributes on tokens, e.g.
             |  #[code like_num], which includes language-specific words like "ten"
@@ -80,22 +78,22 @@ p
 
     +row
         +cell #[strong Syntax iterators]
-            |  #[+src(gh("spaCy", "spacy/lang/en/syntax_iterators.py")) syntax_iterators.py]
+            |  #[+src(gh("spaCy", "spacy/lang/en/syntax_iterators.py")) #[code syntax_iterators.py]]
         +cell
             |  Functions that compute views of a #[code Doc] object based on its
             |  syntax. At the moment, only used for
-            |  #[+a("/docs/usage/dependency-parse#noun-chunks") noun chunks].
+            |  #[+a("/usage/linguistic-features#noun-chunks") noun chunks].
 
     +row
         +cell #[strong Lemmatizer]
-            |  #[+src(gh("spacy-dev-resources", "templates/new_language/lemmatizer.py")) lemmatizer.py]
+            |  #[+src(gh("spacy-dev-resources", "templates/new_language/lemmatizer.py")) #[code lemmatizer.py]]
         +cell
             |  Lemmatization rules or a lookup-based lemmatization table to
             |  assign base forms, for example "be" for "was".
 
     +row
         +cell #[strong Tag map]#[br]
-            |  #[+src(gh("spacy-dev-resources", "templates/new_language/tag_map.py")) tag_map.py]
+            |  #[+src(gh("spacy-dev-resources", "templates/new_language/tag_map.py")) #[code tag_map.py]]
         +cell
             |  Dictionary mapping strings in your tag set to
             |  #[+a("http://universaldependencies.org/u/pos/all.html") Universal Dependencies]
@@ -103,7 +101,7 @@ p
 
     +row
         +cell #[strong Morph rules]
-            |  #[+src(gh("spaCy", "spacy/lang/en/morph_rules.py")) morph_rules.py]
+            |  #[+src(gh("spaCy", "spacy/lang/en/morph_rules.py")) #[code morph_rules.py]]
         +cell
             |  Exception rules for morphological analysis of irregular words like
             |  personal pronouns.
diff --git a/website/docs/usage/lightning-tour.jade b/website/usage/_spacy-101/_lightning-tour.jade
similarity index 82%
rename from website/docs/usage/lightning-tour.jade
rename to website/usage/_spacy-101/_lightning-tour.jade
index 2b0cf0880..061ec7758 100644
--- a/website/docs/usage/lightning-tour.jade
+++ b/website/usage/_spacy-101/_lightning-tour.jade
@@ -1,13 +1,11 @@
-//- 💫 DOCS > USAGE > LIGHTNING TOUR
-
-include ../../_includes/_mixins
+//- 💫 DOCS > USAGE > SPACY 101 > LIGHTNING TOUR
 
 p
     |  The following examples and code snippets give you an overview of spaCy's
     |  functionality and its usage. If you're new to spaCy, make sure to check
-    |  out the #[+a("/docs/usage/spacy-101") spaCy 101 guide].
+    |  out the #[+a("/usage/spacy-101") spaCy 101 guide].
 
-+h(2, "models") Install models and process text
++h(3, "lightning-tour-models") Install models and process text
 
 +code(false, "bash").
     spacy download en
@@ -23,10 +21,10 @@ p
 
 +infobox
     |  #[strong API:] #[+api("spacy#load") #[code spacy.load()]]
-    |  #[strong Usage:] #[+a("/docs/usage/models") Models],
-    |  #[+a("/docs/usage/spacy-101") spaCy 101]
+    |  #[strong Usage:] #[+a("/usage/models") Models],
+    |  #[+a("/usage/spacy-101") spaCy 101]
 
-+h(2, "examples-tokens-sentences") Get tokens, noun chunks & sentences
++h(3, "lightning-tour-tokens-sentences") Get tokens, noun chunks & sentences
     +tag-model("dependency parse")
 
 +code.
@@ -45,9 +43,9 @@ p
 
 +infobox
     |  #[strong API:] #[+api("doc") #[code Doc]], #[+api("token") #[code Token]]
-    |  #[strong Usage:] #[+a("/docs/usage/spacy-101") spaCy 101]
+    |  #[strong Usage:] #[+a("/usage/spacy-101") spaCy 101]
 
-+h(2, "examples-pos-tags") Get part-of-speech tags and flags
++h(3, "lightning-tour-pos-tags") Get part-of-speech tags and flags
     +tag-model("tagger")
 
 +code.
@@ -66,9 +64,9 @@ p
 
 +infobox
     |  #[strong API:] #[+api("token") #[code Token]]
-    |  #[strong Usage:] #[+a("/docs/usage/pos-tagging") Part-of-speech tagging]
+    |  #[strong Usage:] #[+a("/usage/linguistic-features#pos-tagging") Part-of-speech tagging]
 
-+h(2, "examples-hashes") Use hash values for any string
++h(3, "lightning-tour-hashes") Use hash values for any string
 
 +code.
     doc = nlp(u'I love coffee')
@@ -86,9 +84,9 @@ p
 
 +infobox
     |  #[strong API:] #[+api("stringstore") #[code stringstore]]
-    |  #[strong Usage:] #[+a("/docs/usage/spacy-101#vocab") Vocab, hashes and lexemes 101]
+    |  #[strong Usage:] #[+a("/usage/spacy-101#vocab") Vocab, hashes and lexemes 101]
 
-+h(2, "examples-entities") Recongnise and update named entities
++h(3, "lightning-tour-entities") Recongnise and update named entities
     +tag-model("NER")
 
 +code.
@@ -103,9 +101,9 @@ p
     assert ents == [(0, 7, u'ORG')]
 
 +infobox
-    |  #[strong Usage:] #[+a("/docs/usage/entity-recognition") Named entity recognition]
+    |  #[strong Usage:] #[+a("/usage/linguistic-features#named-entities") Named entity recognition]
 
-+h(2, "displacy") Visualize a dependency parse and named entities in your browser
++h(3, "lightning-tour-displacy") Visualize a dependency parse and named entities in your browser
     +tag-model("dependency parse", "NER")
 
 +aside
@@ -156,9 +154,9 @@ p
 
 +infobox
     |  #[strong API:] #[+api("displacy") #[code displacy]]
-    |  #[strong Usage:] #[+a("/docs/usage/visualizers") Visualizers]
+    |  #[strong Usage:] #[+a("/usage/visualizers") Visualizers]
 
-+h(2, "examples-word-vectors") Get word vectors and similarity
++h(3, "lightning-tour-word-vectors") Get word vectors and similarity
     +tag-model("word vectors")
 
 +code.
@@ -171,9 +169,9 @@ p
     assert apple.has_vector, banana.has_vector, pasta.has_vector, hippo.has_vector
 
 +infobox
-    |  #[strong Usage:] #[+a("/docs/usage/word-vectors-similarities") Word vectors and similarity]
+    |  #[strong Usage:] #[+a("/usage/vectors-similarity") Word vectors and similarity]
 
-+h(2, "examples-serialization") Simple and efficient serialization
++h(3, "lightning-tour-serialization") Simple and efficient serialization
 
 +code.
     import spacy
@@ -190,9 +188,9 @@ p
 +infobox
     |  #[strong API:] #[+api("language") #[code Language]],
     |  #[+api("doc") #[code Doc]]
-    |  #[strong Usage:] #[+a("/docs/usage/saving-loading") Saving and loading]
+    |  #[strong Usage:] #[+a("/usage/models#saving-loading") Saving and loading models]
 
-+h(2, "rule-matcher") Match text with token rules
++h(3, "lightning-tour-rule-matcher") Match text with token rules
 
 +code.
     import spacy
@@ -212,9 +210,9 @@ p
 
 +infobox
     |  #[strong API:] #[+api("matcher") #[code Matcher]]
-    |  #[strong Usage:] #[+a("/docs/usage/rule-based-matching") Rule-based matching]
+    |  #[strong Usage:] #[+a("/usage/linguistic-features#rule-based-matching") Rule-based matching]
 
-+h(2, "multi-threaded") Multi-threaded generator
++h(3, "lightning-tour-multi-threaded") Multi-threaded generator
 
 +code.
     texts = [u'One document.', u'...', u'Lots of documents']
@@ -227,9 +225,9 @@ p
 
 +infobox
     |  #[strong API:] #[+api("doc") #[code Doc]]
-    |  #[strong Usage:] #[+a("/docs/usage/production-usage") Production usage]
+    |  #[strong Usage:] #[+a("/usage/processing-pipelines#multithreading") Processing pipelines]
 
-+h(2, "examples-dependencies") Get syntactic dependencies
++h(3, "lightning-tour-dependencies") Get syntactic dependencies
     +tag-model("dependency parse")
 
 +code.
@@ -243,9 +241,9 @@ p
 
 +infobox
     |  #[strong API:] #[+api("token") #[code Token]]
-    |  #[strong Usage:] #[+a("/docs/usage/dependency-parse") Using the dependency parse]
+    |  #[strong Usage:] #[+a("/usage/linguistic-features#dependency-parse") Using the dependency parse]
 
-+h(2, "examples-numpy-arrays") Export to numpy arrays
++h(3, "lightning-tour-numpy-arrays") Export to numpy arrays
 
 +code.
     from spacy.attrs import ORTH, LIKE_URL, IS_OOV
@@ -258,7 +256,7 @@ p
     assert doc[0].like_url == doc_array[0, 1]
     assert list(doc_array[:, 1]) == [t.like_url for t in doc]
 
-+h(2, "examples-inline") Calculate inline markup on original string
++h(3, "lightning-tour-inline") Calculate inline markup on original string
 
 +code.
     def put_spans_around_tokens(doc, get_classes):
diff --git a/website/docs/usage/_spacy-101/_named-entities.jade b/website/usage/_spacy-101/_named-entities.jade
similarity index 90%
rename from website/docs/usage/_spacy-101/_named-entities.jade
rename to website/usage/_spacy-101/_named-entities.jade
index a3c539564..d9c595e6a 100644
--- a/website/docs/usage/_spacy-101/_named-entities.jade
+++ b/website/usage/_spacy-101/_named-entities.jade
@@ -3,7 +3,7 @@
 p
     |  A named entity is a "real-world object" that's assigned a name – for
     |  example, a person, a country, a product or a book title. spaCy can
-    |  #[strong recognise] #[+a("/docs/api/annotation#named-entities") various types]
+    |  #[strong recognise] #[+a("/api/annotation#named-entities") various types]
     |  of named entities in a document, by asking the model for a
     |  #[strong prediction]. Because models are statistical and strongly depend
     |  on the examples they were trained on, this doesn't always work
@@ -32,7 +32,7 @@ p
     +annotation-row(["$1 billion", 44, 54, "MONEY", "Monetary values, including unit."], style)
 
 p
-    |  Using spaCy's built-in #[+a("/docs/usage/visualizers") displaCy visualizer],
+    |  Using spaCy's built-in #[+a("/usage/visualizers") displaCy visualizer],
     |  here's what our example sentence and its named entities look like:
 
 +codepen("2f2ad1408ff79fc6a326ea3aedbb353b", 160)
diff --git a/website/docs/usage/_spacy-101/_pipelines.jade b/website/usage/_spacy-101/_pipelines.jade
similarity index 89%
rename from website/docs/usage/_spacy-101/_pipelines.jade
rename to website/usage/_spacy-101/_pipelines.jade
index c21c9f97c..4e9cd8aeb 100644
--- a/website/docs/usage/_spacy-101/_pipelines.jade
+++ b/website/usage/_spacy-101/_pipelines.jade
@@ -5,15 +5,13 @@ p
     |  produce a #[code Doc] object. The #[code Doc] is then processed in several
     |  different steps – this is also referred to as the
     |  #[strong processing pipeline]. The pipeline used by the
-    |  #[+a("/docs/usage/models") default models] consists of a
+    |  #[+a("/models") default models] consists of a
     |  tensorizer, a tagger, a parser and an entity recognizer. Each pipeline
     |  component returns the processed #[code Doc], which is then passed on to
     |  the next component.
 
-+image
-    include ../../../assets/img/docs/pipeline.svg
-    .u-text-right
-        +button("/assets/img/docs/pipeline.svg", false, "secondary").u-text-tag View large graphic
++graphic("/assets/img/pipeline.svg")
+    include ../../assets/img/pipeline.svg
 
 +aside
     |  #[strong Name:] ID of the pipeline component.#[br]
@@ -30,7 +28,7 @@ p
 
     +row("divider")
         +cell tensorizer
-        +cell #[code TokenVectorEncoder]
+        +cell #[+api("tensorizer") Tensorizer]
         +cell #[code Doc.tensor]
         +cell Create feature representation tensor for #[code Doc].
 
@@ -54,6 +52,12 @@ p
         +cell #[code Doc.ents], #[code Doc[i].ent_iob], #[code Doc[i].ent_type]
         +cell Detect and label named entities.
 
+    +row
+        +cell textcat
+        +cell #[+api("textcategorizer") #[code TextCategorizer]]
+        +cell #[code Doc.cats]
+        +cell Assign document labels.
+
 p
     |  The processing pipeline always #[strong depends on the statistical model]
     |  and its capabilities. For example, a pipeline can only include an entity
diff --git a/website/docs/usage/_spacy-101/_pos-deps.jade b/website/usage/_spacy-101/_pos-deps.jade
similarity index 95%
rename from website/docs/usage/_spacy-101/_pos-deps.jade
rename to website/usage/_spacy-101/_pos-deps.jade
index 52a7fdd3c..a8f7f04b5 100644
--- a/website/docs/usage/_spacy-101/_pos-deps.jade
+++ b/website/usage/_spacy-101/_pos-deps.jade
@@ -1,7 +1,7 @@
 //- 💫 DOCS > USAGE > SPACY 101 > POS TAGGING AND DEPENDENCY PARSING
 
 p
-    |  After tokenization, spaCy can also #[strong parse] and #[strong tag] a
+    |  After tokenization, spaCy can #[strong parse] and #[strong tag] a
     |  given #[code Doc]. This is where the statistical model comes in, which
     |  enables spaCy to #[strong make a prediction] of which tag or label most
     |  likely applies in this context. A model consists of binary data and is
@@ -56,7 +56,7 @@ p
     |  singular present".
 
 p
-    |  Using spaCy's built-in #[+a("/docs/usage/visualizers") displaCy visualizer],
+    |  Using spaCy's built-in #[+a("/usage/visualizers") displaCy visualizer],
     |  here's what our example sentence and its dependencies look like:
 
 +codepen("030d1e4dfa6256cad8fdd59e6aefecbe", 460)
diff --git a/website/docs/usage/_spacy-101/_serialization.jade b/website/usage/_spacy-101/_serialization.jade
similarity index 100%
rename from website/docs/usage/_spacy-101/_serialization.jade
rename to website/usage/_spacy-101/_serialization.jade
diff --git a/website/docs/usage/_spacy-101/_similarity.jade b/website/usage/_spacy-101/_similarity.jade
similarity index 100%
rename from website/docs/usage/_spacy-101/_similarity.jade
rename to website/usage/_spacy-101/_similarity.jade
diff --git a/website/docs/usage/_spacy-101/_tokenization.jade b/website/usage/_spacy-101/_tokenization.jade
similarity index 90%
rename from website/docs/usage/_spacy-101/_tokenization.jade
rename to website/usage/_spacy-101/_tokenization.jade
index d6911387c..602209ec8 100644
--- a/website/docs/usage/_spacy-101/_tokenization.jade
+++ b/website/usage/_spacy-101/_tokenization.jade
@@ -49,14 +49,12 @@ p
     |  #[strong Infix:] Character(s) in between, e.g.
     |  #[code -], #[code --], #[code /], #[code …].#[br]
 
-+image
-    include ../../../assets/img/docs/tokenization.svg
-    .u-text-right
-        +button("/assets/img/docs/tokenization.svg", false, "secondary").u-text-tag View large graphic
++graphic("/assets/img/tokenization.svg")
+    include ../../assets/img/tokenization.svg
 
 p
     |  While punctuation rules are usually pretty general, tokenizer exceptions
     |  strongly depend on the specifics of the individual language. This is
-    |  why each #[+a("/docs/api/language-models") available language] has its
+    |  why each #[+a("/models/#languages") available language] has its
     |  own subclass like #[code English] or #[code German], that loads in lists
     |  of hard-coded data and exception rules.
diff --git a/website/docs/usage/_spacy-101/_training.jade b/website/usage/_spacy-101/_training.jade
similarity index 94%
rename from website/docs/usage/_spacy-101/_training.jade
rename to website/usage/_spacy-101/_training.jade
index 9b283c0eb..5d97a86df 100644
--- a/website/docs/usage/_spacy-101/_training.jade
+++ b/website/usage/_spacy-101/_training.jade
@@ -24,10 +24,8 @@ p
     |  #[strong Gradient:] Gradient of the loss function calculating the
     |  difference between input and expected output.
 
-+image
-    include ../../../assets/img/docs/training.svg
-    .u-text-right
-        +button("/assets/img/docs/training.svg", false, "secondary").u-text-tag View large graphic
++graphic("/assets/img/training.svg")
+    include ../../assets/img/training.svg
 
 p
     |  When training a model, we don't just want it to memorise our examples –
diff --git a/website/docs/usage/_spacy-101/_vocab.jade b/website/usage/_spacy-101/_vocab.jade
similarity index 96%
rename from website/docs/usage/_spacy-101/_vocab.jade
rename to website/usage/_spacy-101/_vocab.jade
index 3063262d5..185e634fe 100644
--- a/website/docs/usage/_spacy-101/_vocab.jade
+++ b/website/usage/_spacy-101/_vocab.jade
@@ -19,10 +19,8 @@ p
     |  #[strong StringStore]: The dictionary mapping hash values to strings, for
     |  example #[code 3197928453018144401] &rarr; "coffee".
 
-+image
-    include ../../../assets/img/docs/vocab_stringstore.svg
-    .u-text-right
-        +button("/assets/img/docs/vocab_stringstore.svg", false, "secondary").u-text-tag View large graphic
++graphic("/assets/img/vocab_stringstore.svg")
+    include ../../assets/img/vocab_stringstore.svg
 
 p
     |  If you process lots of documents containing the word "coffee" in all
diff --git a/website/docs/usage/_spacy-101/_word-vectors.jade b/website/usage/_spacy-101/_word-vectors.jade
similarity index 98%
rename from website/docs/usage/_spacy-101/_word-vectors.jade
rename to website/usage/_spacy-101/_word-vectors.jade
index cbb9d06f2..bb9add8a6 100644
--- a/website/docs/usage/_spacy-101/_word-vectors.jade
+++ b/website/usage/_spacy-101/_word-vectors.jade
@@ -5,7 +5,7 @@ p
     |  embeddings", multi-dimensional meaning representations of a word. Word
     |  vectors can be generated using an algorithm like
     |  #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]. Most of spaCy's
-    |  #[+a("/docs/usage/models") default models] come with
+    |  #[+a("/models") default models] come with
     |  #[strong 300-dimensional vectors] that look like this:
 
 +code("banana.vector", false, false, 250).
@@ -148,5 +148,5 @@ p
 p
     |  If your application will benefit from a large vocabulary with more
     |  vectors, you should consider using one of the
-    |  #[+a("/docs/usage/models#available") larger models] instead of the default,
+    |  #[+a("/models") larger models] instead of the default,
     |  smaller ones, which usually come with a clipped vocabulary.
diff --git a/website/docs/usage/training.jade b/website/usage/_training/_basics.jade
similarity index 89%
rename from website/docs/usage/training.jade
rename to website/usage/_training/_basics.jade
index c1a7c1835..05e67c2c1 100644
--- a/website/docs/usage/training.jade
+++ b/website/usage/_training/_basics.jade
@@ -1,14 +1,6 @@
-include ../../_includes/_mixins
+//- 💫 DOCS > USAGE > TRAINING > BASICS
 
-p
-    |  This guide describes how to train new statistical models for spaCy's
-    |  part-of-speech tagger, named entity recognizer and dependency parser.
-    |  Once the model is trained, you can then
-    |  #[+a("/docs/usage/saving-loading") save and load] it.
-
-+h(2, "101") Training 101
-
-include _spacy-101/_training
+include ../_spacy-101/_training
 
 +h(3, "training-data") How do I get training data?
 
@@ -50,7 +42,7 @@ p
 
 p
     |  Alternatively, the
-    |  #[+a("/docs/usage/rule-based-matching#example3") rule-based matcher]
+    |  #[+a("/usage/linguistic-features#rule-based-matching") rule-based matcher]
     |  can be a useful tool to extract tokens or combinations of tokens, as
     |  well as their start and end index in a document. In this case, we'll
     |  extract mentions of Google and assume they're an #[code ORG].
@@ -73,7 +65,7 @@ p
     |  #[strong what you want the model to learn]. While there are some entity
     |  annotations that are more or less universally correct – like Canada being
     |  a geopolitical entity – your application may have its very own definition
-    |  of the #[+a("/docs/api/annotation#named-entities") NER annotation scheme].
+    |  of the #[+a("/api/annotation#named-entities") NER annotation scheme].
 
 +code.
     train_data = [
@@ -84,7 +76,7 @@ p
         ("Google rebrands its business apps", [(0, 6, "ORG")]),
         ("look what i found on google! 😂", [(21, 27, "PRODUCT")])]
 
-+h(2) Training with annotations
++h(3, "annotations") Training with annotations
 
 p
     |  The #[+api("goldparse") #[code GoldParse]] object collects the annotated
@@ -103,7 +95,7 @@ p
 p
     |  Using the #[code Doc] and its gold-standard annotations, the model can be
     |  updated to learn a sentence of three words with their assigned
-    |  part-of-speech tags. The #[+a("/docs/usage/adding-languages#tag-map") tag map]
+    |  part-of-speech tags. The #[+a("/usage/adding-languages#tag-map") tag map]
     |  is part of the vocabulary and defines the annotation scheme. If you're
     |  training a new language model, this will let you map the tags present in
     |  the treebank you train on to spaCy's tag scheme.
@@ -115,7 +107,7 @@ p
 p
     |  The same goes for named entities. The letters added before the labels
     |  refer to the tags of the
-    |  #[+a("/docs/usage/entity-recognition#updating-biluo") BILUO scheme] –
+    |  #[+a("/usage/linguistic-features#updating-biluo") BILUO scheme] –
     |  #[code O] is a token outside an entity, #[code U] an single entity unit,
     |  #[code B] the beginning of an entity, #[code I] a token inside an entity
     |  and #[code L] the last token of an entity.
@@ -130,10 +122,8 @@ p
     |  #[strong Update]: Update the model's weights.#[br]
     |  #[strong ]
 
-+image
-    include ../../assets/img/docs/training-loop.svg
-    .u-text-right
-        +button("/assets/img/docs/training-loop.svg", false, "secondary").u-text-tag View large graphic
++graphic("/assets/img/training-loop.svg")
+    include ../../assets/img/training-loop.svg
 
 p
     |  Of course, it's not enough to only show a model a single example once.
@@ -192,11 +182,7 @@ p
 
 +infobox
     |  For the #[strong full example and more details], see the usage guide on
-    |  #[+a("/docs/usage/training-ner") training the named entity recognizer],
+    |  #[+a("/usage/training#ner") training the named entity recognizer],
     |  or the runnable
     |  #[+src(gh("spaCy", "examples/training/train_ner.py")) training script]
     |  on GitHub.
-
-+h(2) Examples
-
-+under-construction
diff --git a/website/usage/_training/_ner.jade b/website/usage/_training/_ner.jade
new file mode 100644
index 000000000..ff3101c8f
--- /dev/null
+++ b/website/usage/_training/_ner.jade
@@ -0,0 +1,61 @@
+//- 💫 DOCS > USAGE > TRAINING > NER
+
+p
+    |  All #[+a("/models") spaCy models] support online learning, so
+    |  you can update a pre-trained model with new examples. To update the
+    |  model, you first need to create an instance of
+    |  #[+api("goldparse") #[code GoldParse]], with the entity labels
+    |  you want to learn. You'll usually need to provide many examples to
+    |  meaningfully improve the system — a few hundred is a good start, although
+    |  more is better.
+
+p
+    |  You should avoid iterating over the same few examples multiple times, or
+    |  the model is likely to "forget" how to annotate other examples. If you
+    |  iterate over the same few examples, you're effectively changing the loss
+    |  function. The optimizer will find a way to minimize the loss on your
+    |  examples, without regard for the consequences on the examples it's no
+    |  longer paying attention to. One way to avoid this
+    |  #[+a("https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting", true) "catastrophic forgetting" problem]
+    |  is to "remind"
+    |  the model of other examples by augmenting your annotations with sentences
+    |  annotated with entities automatically recognised by the original model.
+    |  Ultimately, this is an empirical process: you'll need to
+    |  #[strong experiment on your own data] to find a solution that works best
+    |  for you.
+
++h(3, "example-new-entity-type") Example: Training an additional entity type
+
+p
+    |  This script shows how to add a new entity type to an existing pre-trained
+    |  NER model. To keep the example short and simple, only a few sentences are
+    |  provided as examples. In practice, you'll need many more — a few hundred
+    |  would be a good start. You will also likely need to mix in examples of
+    |  other entity types, which might be obtained by running the entity
+    |  recognizer over unlabelled sentences, and adding their annotations to the
+    |  training set.
+
+p
+    |  The actual training is performed by looping over the examples, and
+    |  calling #[+api("language#update") #[code nlp.update()]]. The
+    |  #[code update] method steps through the words of the input. At each word,
+    |  it makes a prediction. It then consults the annotations provided on the
+    |  #[+api("goldparse") #[code GoldParse]] instance, to see whether it was
+    |  right. If it was wrong, it adjusts its weights so that the correct
+    |  action will score higher next time.
+
++github("spacy", "examples/training/train_new_entity_type.py")
+
++h(3, "example-ner-from-scratch") Example: Training an NER system from scratch
+
+p
+    |  This example is written to be self-contained and reasonably transparent.
+    |  To achieve that, it duplicates some of spaCy's internal functionality.
+    |  Specifically, in this example, we don't use spaCy's built-in
+    |  #[+api("language") #[code Language]] class to wire together the
+    |  #[+api("vocab") #[code Vocab]], #[+api("tokenizer") #[code Tokenizer]]
+    |  and #[+api("entityrecognizer") #[code EntityRecognizer]]. Instead, we
+    |  write our own simle #[code Pipeline] class, so that it's easier to see
+    |  how the pieces interact.
+
++github("spacy", "examples/training/train_ner_standalone.py")
diff --git a/website/docs/usage/saving-loading.jade b/website/usage/_training/_saving-loading.jade
similarity index 70%
rename from website/docs/usage/saving-loading.jade
rename to website/usage/_training/_saving-loading.jade
index de7e4ed33..e6e54385c 100644
--- a/website/docs/usage/saving-loading.jade
+++ b/website/usage/_training/_saving-loading.jade
@@ -1,45 +1,4 @@
-include ../../_includes/_mixins
-
-+h(2, "101") Serialization 101
-
-include _spacy-101/_serialization
-
-+infobox("Important note")
-    |  In spaCy v2.0, the API for saving and loading has changed to only use the
-    |  four methods listed above consistently across objects and classes. For an
-    |  overview of the changes, see #[+a("/docs/usage/v2#incompat") this table]
-    |  and the notes on #[+a("/docs/usage/v2#migrating-saving-loading") migrating].
-
-+h(3, "example-doc") Example: Saving and loading a document
-
-p
-    |  For simplicity, let's assume you've
-    |  #[+a("/docs/usage/entity-recognition#setting") added custom entities] to
-    |  a #[code Doc], either manually, or by using a
-    |  #[+a("/docs/usage/rule-based-matching#on_match") match pattern]. You can
-    |  save it locally by calling #[+api("doc#to_disk") #[code Doc.to_disk()]],
-    |  and load it again via #[+api("doc#from_disk") #[code Doc.from_disk()]].
-    |  This will overwrite the existing object and return it.
-
-+code.
-    import spacy
-    from spacy.tokens import Span
-
-    text = u'Netflix is hiring a new VP of global policy'
-
-    nlp = spacy.load('en')
-    doc = nlp(text)
-    assert len(doc.ents) == 0 # Doc has no entities
-    doc.ents += ((Span(doc, 0, 1, label=doc.vocab.strings[u'ORG'])) # add entity
-    doc.to_disk('/path/to/doc') # save Doc to disk
-
-    new_doc = nlp(text)
-    assert len(new_doc.ents) == 0 # new Doc has no entities
-    new_doc = new_doc.from_disk('path/to/doc') # load from disk and overwrite
-    assert len(new_doc.ents) == 1 # entity is now recognised!
-    assert [(ent.text, ent.label_) for ent in new_doc.ents] == [(u'Netflix', u'ORG')]
-
-+h(2, "models") Saving models
+//- 💫 DOCS > USAGE > TRAINING > SAVING & LOADING
 
 p
     |  After training your model, you'll usually want to save its state, and load
@@ -55,6 +14,7 @@ p
     |  will be written out. To make the model more convenient to deploy, we
     |  recommend wrapping it as a Python package.
 
+
 +h(3, "models-generating") Generating a model package
 
 +infobox("Important note")
@@ -105,13 +65,14 @@ p
     |  need to be named according to the naming conventions of
     |  #[code lang_name] and #[code lang_name-version].
 
+
 +h(3, "models-custom") Customising the model setup
 
 p
     |  The meta.json includes the model details, like name, requirements and
     |  license, and lets you customise how the model should be initialised and
     |  loaded. You can define the language data to be loaded and the
-    |  #[+a("/docs/usage/language-processing-pipeline") processing pipeline] to
+    |  #[+a("/usage/processing-pipelines") processing pipeline] to
     |  execute.
 
 +table(["Setting", "Type", "Description"])
@@ -126,7 +87,7 @@ p
         +cell
             |  A list of strings mapping to the IDs of pipeline factories to
             |  apply in that order. If not set, spaCy's
-            |  #[+a("/docs/usage/language-processing/pipelines") default pipeline]
+            |  #[+a("/usage/processing-pipelines") default pipeline]
             |  will be used.
 
 p
@@ -135,7 +96,7 @@ p
     |  #[code Language] object with the loaded pipeline and data. If your model
     |  requires custom pipeline components, you should
     |  #[strong ship then with your model] and register their
-    |  #[+a("/docs/usage/language-processing-pipeline#creating-factory") factories]
+    |  #[+a("/usage/processing-pipelines#creating-factory") factories]
     |  via  #[+api("spacy#set_factory") #[code set_factory()]].
 
 +aside-code("Factory example").
@@ -152,7 +113,7 @@ p
 +infobox("Custom models with pipeline components")
     |  For more details and an example of how to package a sentiment model
     |  with a custom pipeline component, see the usage guide on
-    |  #[+a("/docs/usage/language-processing-pipeline#example2") language processing pipelines].
+    |  #[+a("/usage/processing-pipelines#example2") language processing pipelines].
 
 +h(3, "models-building") Building the model package
 
@@ -176,7 +137,7 @@ p
     |  You can then load the model via its name, #[code en_example_model], or
     |  import it directly as a module and then call its #[code load()] method.
 
-+h(2, "loading") Loading a custom model package
++h(3, "loading") Loading a custom model package
 
 p
     |  To load a model from a data directory, you can use
@@ -209,3 +170,38 @@ p
 
     +code-new nlp = English().from_disk('/path/to/data')
     +code-old nlp = spacy.load('en', path='/path/to/data')
+
++h(3, "example-training-spacy") Example: How we're training and packaging models for spaCy
+
+p
+    |  Publishing a new version of spaCy often means re-training all available
+    |  models – currently, that's #{MODEL_COUNT} models for #{MODEL_LANG_COUNT}
+    |  languages. To make this run smoothly, we're using an automated build
+    |  process and a #[+api("cli#train") #[code spacy train]] template that
+    |  looks like this:
+
++code(false, "bash", "$", false, false, true).
+    spacy train {lang} {models_dir}/{name} {train_data} {dev_data} -m meta/{name}.json -V {version} -g {gpu_id} -n {n_epoch} -ns {n_sents}
+
++aside-code("meta.json template", "json").
+    {
+        "lang": "en",
+        "name": "core_web_sm",
+        "license":"CC BY-SA 3.0",
+        "author":"Explosion AI",
+        "url":"https://explosion.ai",
+        "email":"contact@explosion.ai",
+        "sources": ["OntoNotes 5", "Common Crawl"],
+        "description":"English multi-task CNN trained on OntoNotes, with GloVe vectors trained on common crawl. Assigns word vectors, context-specific token vectors, POS tags, dependency parse and named entities."
+    }
+
+p In a directory #[code meta], we keep #[code meta.json] templates for the individual models, containing all relevant information that doesn't change across versions, like the name, description, author info and training data sources. When we train the model, we pass in the file to the meta template as the #[code --meta] argument, and specify the current model version as the #[code --version] argument.
+
+p On each epoch, the model is saved out with a #[code meta.json] using our template and added properties, like the #[code pipeline], #[code accuracy] scores and the #[code spacy_version] used to train the model. After training completion, the best model is selected automatically and packaged using the #[+api("cli#package") #[code package]] command. Since a full meta file is already present on the trained model, no further setup is required to build a valid model package.
+
++code(false, "bash").
+    spacy package -f {best_model} dist/
+    cd dist/{model_name}
+    python setup.py sdist
+
+p This process allows us to quickly trigger the model training and build process for all available models and languages, and generate the correct meta data automatically.
diff --git a/website/usage/_training/_similarity.jade b/website/usage/_training/_similarity.jade
new file mode 100644
index 000000000..eb7991c37
--- /dev/null
+++ b/website/usage/_training/_similarity.jade
@@ -0,0 +1,3 @@
+//- 💫 DOCS > USAGE > TRAINING > SIMILARITY
+
++under-construction
diff --git a/website/usage/_training/_tagger-parser.jade b/website/usage/_training/_tagger-parser.jade
new file mode 100644
index 000000000..4011464c7
--- /dev/null
+++ b/website/usage/_training/_tagger-parser.jade
@@ -0,0 +1,3 @@
+//- 💫 DOCS > USAGE > TRAINING > TAGGER & PARSER
+
++under-construction
diff --git a/website/usage/_training/_textcat.jade b/website/usage/_training/_textcat.jade
new file mode 100644
index 000000000..5c90519db
--- /dev/null
+++ b/website/usage/_training/_textcat.jade
@@ -0,0 +1,13 @@
+//- 💫 DOCS > USAGE > TRAINING > TEXT CLASSIFICATION
+
++under-construction
+
++h(3, "example-textcat") Example: Training spaCy's text classifier
+    +tag-new(2)
+
+p
+    |  This example shows how to use and train spaCy's new
+    |  #[+api("textcategorizer") #[code TextCategorizer]] pipeline component
+    |  on IMDB movie reviews.
+
++github("spacy", "examples/training/train_textcat.py")
diff --git a/website/usage/_vectors-similarity/_basics.jade b/website/usage/_vectors-similarity/_basics.jade
new file mode 100644
index 000000000..b8f8d834c
--- /dev/null
+++ b/website/usage/_vectors-similarity/_basics.jade
@@ -0,0 +1,15 @@
+//- 💫 DOCS > USAGE > VECTORS & SIMILARITY > BASICS
+
++aside("Training word vectors")
+    |  Dense, real valued vectors representing distributional similarity
+    |  information are now a cornerstone of practical NLP. The most common way
+    |  to train these vectors is the #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]
+    |  family of algorithms. The default
+    |  #[+a("/models/en") English model] installs
+    |  300-dimensional vectors trained on the
+    |  #[+a("http://commoncrawl.org") Common Crawl] corpus.
+    |  If you need to train a word2vec model, we recommend the implementation in
+    |  the Python library #[+a("https://radimrehurek.com/gensim/") Gensim].
+
+include ../_spacy-101/_similarity
+include ../_spacy-101/_word-vectors
diff --git a/website/usage/_vectors-similarity/_custom.jade b/website/usage/_vectors-similarity/_custom.jade
new file mode 100644
index 000000000..da4be39fd
--- /dev/null
+++ b/website/usage/_vectors-similarity/_custom.jade
@@ -0,0 +1,91 @@
+//- 💫 DOCS > USAGE > VECTORS & SIMILARITY > CUSTOM VECTORS
+
+p
+    |  By default, #[+api("token#vector") #[code Token.vector]] returns the
+    |  vector for its underlying #[+api("lexeme") #[code Lexeme]], while
+    |  #[+api("doc#vector") #[code Doc.vector]] and
+    |  #[+api("span#vector") #[code Span.vector]] return an average of the
+    |  vectors of their tokens. You can customize these
+    |  behaviours by modifying the #[code doc.user_hooks],
+    |  #[code doc.user_span_hooks] and #[code doc.user_token_hooks]
+    |  dictionaries.
+
++infobox
+    |  For more details on #[strong adding hooks] and #[strong overwriting] the
+    |  built-in #[code Doc], #[code Span] and #[code Token] methods, see the
+    |  usage guide on #[+a("/usage/processing-pipelines#user-hooks") user hooks].
+
++h(3, "custom-vectors-add") Adding vectors
+    +tag-new(2)
+
+p
+    |  The new #[+api("vectors") #[code Vectors]] class makes it easy to add
+    |  your own vectors to spaCy. Just like the #[+api("vocab") #[code Vocab]],
+    |  it is initialised with a #[+api("stringstore") #[code StringStore]] or
+    |  a list of strings.
+
++code("Adding vectors one-by-one").
+    from spacy.strings import StringStore
+    from spacy.vectors import Vectors
+
+    vector_data = {'dog': numpy.random.uniform(-1, 1, (300,)),
+                   'cat': numpy.random.uniform(-1, 1, (300,)),
+                   'orange': numpy.random.uniform(-1, 1, (300,))}
+
+    vectors = Vectors(StringStore(), 300)
+    for word, vector in vector_data.items():
+        vectors.add(word, vector)
+
+p
+    |  You can also add the vector values directly on initialisation:
+
++code("Adding vectors on initialisation").
+    from spacy.vectors import Vectors
+
+    vector_table = numpy.zeros((3, 300), dtype='f')
+    vectors = Vectors([u'dog', u'cat', u'orange'], vector_table)
+
++h(3, "custom-loading-glove") Loading GloVe vectors
+    +tag-new(2)
+
+p
+    |  spaCy comes with built-in support for loading
+    |  #[+a("https://nlp.stanford.edu/projects/glove/") GloVe] vectors from
+    |  a directory. The #[+api("vectors#from_glove") #[code Vectors.from_glove]]
+    |  method assumes a binary format, the vocab provided in a
+    |  #[code vocab.txt], and the naming scheme of
+    |  #[code vectors.{size}.[fd].bin]. For example:
+
++aside-code("Directory structure", "yaml").
+    └── vectors
+        ├── vectors.128.f.bin  # vectors file
+        └── vocab.txt          # vocabulary
+
++table(["File name", "Dimensions", "Data type"])
+    +row
+        +cell #[code vectors.128.f.bin]
+        +cell 128
+        +cell float32
+
+    +row
+        +cell #[code vectors.300.d.bin]
+        +cell 300
+        +cell float64 (double)
+
++code.
+    from spacy.vectors import Vectors
+
+    vectors = Vectors([], 128)
+    vectors.from_glove('/path/to/vectors')
+
++h(3, "custom-loading-other") Loading other vectors
+    +tag-new(2)
+
+p
+    |  You can also choose to load in vectors from other sources, like the
+    |  #[+a("https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md") fastText vectors]
+    |  for 294 languages, trained on Wikipedia. After reading in the file,
+    |  the vectors are added to the #[code Vocab] using the
+    |  #[+api("vocab#set_vector") #[code set_vector]] method.
+
++github("spacy", "examples/vectors_fast_text.py")
diff --git a/website/usage/_vectors-similarity/_gpu.jade b/website/usage/_vectors-similarity/_gpu.jade
new file mode 100644
index 000000000..9f1201da9
--- /dev/null
+++ b/website/usage/_vectors-similarity/_gpu.jade
@@ -0,0 +1,30 @@
+//- 💫 DOCS > USAGE > VECTORS & SIMILARITY > GPU
+
+p
+    |  If you're using a GPU, it's much more efficient to keep the word vectors
+    |  on the device. You can do that by setting the
+    |  #[+api("vectors#attributes") #[code Vectors.data]] attribute to a
+    |  #[code cupy.ndarray] object if you're using spaCy
+    |  or #[+a("https://chainer.org") Chainer], or a
+    |  #[code torch.Tensor] object if you're using
+    |  #[+a("http://pytorch.org") PyTorch]. The #[code data] object just needs
+    |  to support #[code __iter__] and #[code __getitem__], so if you're using
+    |  another library such as #[+a("https://www.tensorflow.org") TensorFlow],
+    |  you could also create a wrapper for your vectors data.
+
++code("spaCy, Thinc or Chainer").
+    import cupy.cuda
+    from spacy.vectors import Vectors
+
+    vector_table = numpy.zeros((3, 300), dtype='f')
+    vectors = Vectors([u'dog', u'cat', u'orange'], vector_table)
+    with cupy.cuda.Device(0):
+        vectors.data = cupy.asarray(vectors.data)
+
++code("PyTorch").
+    import torch
+    from spacy.vectors import Vectors
+
+    vector_table = numpy.zeros((3, 300), dtype='f')
+    vectors = Vectors([u'dog', u'cat', u'orange'], vector_table)
+    vectors.data = torch.Tensor(vectors.data).cuda(0)
diff --git a/website/docs/usage/word-vectors-similarities.jade b/website/usage/_vectors-similarity/_in-context.jade
similarity index 72%
rename from website/docs/usage/word-vectors-similarities.jade
rename to website/usage/_vectors-similarity/_in-context.jade
index 937fbfbd0..d8e864d9d 100644
--- a/website/docs/usage/word-vectors-similarities.jade
+++ b/website/usage/_vectors-similarity/_in-context.jade
@@ -1,34 +1,11 @@
-//- 💫 DOCS > USAGE > WORD VECTORS & SIMILARITIES
-
-include ../../_includes/_mixins
-
-p
-    |  Dense, real valued vectors representing distributional similarity
-    |  information are now a cornerstone of practical NLP. The most common way
-    |  to train these vectors is the #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]
-    |  family of algorithms. The default
-    |  #[+a("/docs/usage/models#available") English model] installs
-    |  300-dimensional vectors trained on the
-    |  #[+a("http://commoncrawl.org") Common Crawl] corpus.
-
-+aside("Tip: Training a word2vec model")
-    |  If you need to train a word2vec model, we recommend the implementation in
-    |  the Python library #[+a("https://radimrehurek.com/gensim/") Gensim].
-
-+h(2, "101") Similarity and word vectors 101
-    +tag-model("vectors")
-
-include _spacy-101/_similarity
-include _spacy-101/_word-vectors
-
-+h(2, "similarity-context") Similarities in context
+//- 💫 DOCS > USAGE > VECTORS & SIMILARITY > IN CONTEXT
 
 p
     |  Aside from spaCy's built-in word vectors, which were trained on a lot of
     |  text with a wide vocabulary, the parsing, tagging and NER models also
     |  rely on vector representations of the #[strong meanings of words in context].
     |  As the first component of the
-    |  #[+a("/docs/usage/language-processing-pipeline") processing pipeline], the
+    |  #[+a("/usage/processing-pipelines") processing pipeline], the
     |  tensorizer encodes a document's internal meaning representations as an
     |  array of floats, also called a tensor. This allows spaCy to make a
     |  reasonable guess at a word's meaning, based on its surrounding words.
@@ -117,8 +94,8 @@ p
             nlp(u"man dog bites"), nlp(u"dog man bites")]
 
     for doc in docs:
-    for other_doc in docs:
-        print(doc.similarity(other_doc))
+        for other_doc in docs:
+            print(doc.similarity(other_doc))
 
 p
     |  Interestingly, "man bites dog" and "man dog bites" are seen as slightly
@@ -143,17 +120,3 @@ p
                 +cell.u-text-center #[code=cell.toFixed(2)]
                     |  #[+procon(cell < 0.7 ? "con" : cell != 1 ? "pro" : "neutral")]
         - counter++
-
-+h(2, "custom") Customising word vectors
-
-+under-construction
-
-p
-    |  By default, #[+api("token#vector") #[code Token.vector]] returns the
-    |  vector for its underlying #[+api("lexeme") #[code Lexeme]], while
-    |  #[+api("doc#vector") #[code Doc.vector]] and
-    |  #[+api("span#vector") #[code Span.vector]] return an average of the
-    |  vectors of their tokens. You can customize these
-    |  behaviours by modifying the #[code doc.user_hooks],
-    |  #[code doc.user_span_hooks] and #[code doc.user_token_hooks]
-    |  dictionaries.
diff --git a/website/usage/adding-languages.jade b/website/usage/adding-languages.jade
new file mode 100644
index 000000000..0690c8738
--- /dev/null
+++ b/website/usage/adding-languages.jade
@@ -0,0 +1,59 @@
+//- 💫 DOCS > USAGE > ADDING LANGUAGES
+
+include ../_includes/_mixins
+
++aside("Working on spaCy's source")
+    |  To add a new language to spaCy, you'll need to
+    |  #[strong modify the library&apos;s code]. The easiest way to do this is to
+    |  clone the #[+src(gh("spaCy")) repository] and #[strong build spaCy from source].
+    |  For more information on this, see the #[+a("/usage") installation guide].
+    |  Unlike spaCy's core, which is mostly written in Cython, all language
+    |  data is stored in regular Python files. This means that you won't have to
+    |  rebuild anything in between – you can simply make edits and reload spaCy
+    |  to test them.
+
++grid.o-no-block
+    +grid-col("half")
+        p
+            |  Obviously, there are lots of ways you can organise your code when
+            |  you implement your own language data. This guide will focus on
+            |  how it's done within spaCy. For full language support, you'll
+            |  need to create a #[code Language] subclass, define custom
+            |  #[strong language data], like a stop list and tokenizer
+            |  exceptions and test the new tokenizer. Once the language is set
+            |  up, you can #[strong build the vocabulary], including word
+            |  frequencies, Brown clusters and word vectors. Finally, you can
+            |  #[strong train the tagger and parser], and save the model to a
+            |  directory.
+
+        p
+            |  For some languages, you may also want to develop a solution for
+            |  lemmatization and morphological analysis.
+
+    +table-of-contents
+        +item #[+a("#101") Language data 101]
+        +item #[+a("#language-subclass") The Language subclass]
+        +item #[+a("#stop-words") Stop words]
+        +item #[+a("#tokenizer-exceptions") Tokenizer exceptions]
+        +item #[+a("#norm-exceptions") Norm exceptions]
+        +item #[+a("#lex-attrs") Lexical attributes]
+        +item #[+a("#syntax-iterators") Syntax iterators]
+        +item #[+a("#lemmatizer") Lemmatizer]
+        +item #[+a("#tag-map") Tag map]
+        +item #[+a("#morph-rules") Morph rules]
+        +item #[+a("#testing") Testing the language]
+        +item #[+a("#vocabulary") Building the vocabulary]
+        +item #[+a("#training") Training]
+
++section("language-data")
+    +h(2, "language-data") Language data
+    include _spacy-101/_language-data
+    include _adding-languages/_language-data
+
++section("testing")
+    +h(2, "testing") Testing the new language
+    include _adding-languages/_testing
+
++section("training")
+    +h(2, "training") Training a language model
+    include _adding-languages/_training
diff --git a/website/usage/deep-learning.jade b/website/usage/deep-learning.jade
new file mode 100644
index 000000000..4c33c0572
--- /dev/null
+++ b/website/usage/deep-learning.jade
@@ -0,0 +1,29 @@
+//- 💫 DOCS > USAGE > DEEP LEARNING
+
+include ../_includes/_mixins
++section
+    +under-construction
+
++section("pre-processing")
+    +h(2, "pre-processing") Pre-processing text for deep learning
+    include _deep-learning/_pre-processing
+
++section("thinc")
+    +h(2, "thinc") spaCy and Thinc
+    include _deep-learning/_thinc
+
++section("tensorflow-keras")
+    +h(2, "tensorflow-keras") Using spaCy with TensorFlow / Keras
+    include _deep-learning/_tensorflow-keras
+
++section("scikit-learn")
+    +h(2, "scikit-learn") Using spaCy with scikit-learn
+    include _deep-learning/_scikit-learn
+
++section("pytorch")
+    +h(2, "pytorch") Using spaCy with PyTorch
+    include _deep-learning/_pytorch
+
++section("dynet")
+    +h(2, "dynet") Using spaCy with DyNet
+    include _deep-learning/_dynet
diff --git a/website/usage/examples.jade b/website/usage/examples.jade
new file mode 100644
index 000000000..75d05e339
--- /dev/null
+++ b/website/usage/examples.jade
@@ -0,0 +1,73 @@
+//- 💫 DOCS > USAGE > EXAMPLES
+
+include ../_includes/_mixins
+
++section("matching")
+    +h(3, "matcher") Using spaCy's rule-based matcher
+
+    p
+        |  This example shows how to use spaCy's rule-based
+        |  #[+api("matcher") #[code Matcher]] to find and label entities across
+        |  documents.
+
+    +github("spacy", "examples/matcher_example.py")
+
+    +h(3, "phrase-matcher") Using spaCy's phrase matcher
+        +tag-new(2)
+
+    p
+        |  This example shows how to use the new
+        |  #[+api("phrasematcher") #[code PhraseMatcher]] to efficiently find
+        |  entities from a large terminology list.
+
+    +github("spacy", "examples/phrase_matcher.py")
+
++section("training")
+    +h(3, "new-entity-type") Training an additional entity type
+
+    p
+        |  This script shows how to add a new entity type to an existing
+        |  pre-trained NER model. To keep the example short and simple, only
+        |  four sentences are provided as examples. In practice, you'll need
+        |  many more — a few hundred would be a good start.
+
+    +github("spacy", "examples/training/train_new_entity_type.py")
+
+    +h(3, "ner-standalone") Training an NER system from scratch
+
+    p
+        |  This example is written to be self-contained and reasonably
+        |  transparent. To achieve that, it duplicates some of spaCy's internal
+        |  functionality.
+
+    +github("spacy", "examples/training/train_ner_standalone.py")
+
+    +h(3, "textcat") Training spaCy's text classifier
+        +tag-new(2)
+
+    p
+        |  This example shows how to use and train spaCy's new
+        |  #[+api("textcategorizer") #[code TextCategorizer]] pipeline component
+        |  on IMDB movie reviews.
+
+    +github("spacy", "examples/training/train_textcat.py")
+
++section("deep-learning")
+    +h(3, "keras") Text classification with Keras
+
+    p
+        |  In this example, we're using spaCy to pre-process text for use with
+        |  a #[+a("https://keras.io") Keras] text classification model.
+
+    +github("spacy", "examples/deep_learning_keras.py")
+
+    +h(3, "keras-parikh-entailment") A decomposable attention model for Natural Language Inference
+
+    p
+        |  This example contains an implementation of the entailment prediction
+        |  model described by #[+a("https://arxiv.org/pdf/1606.01933.pdf") Parikh et al. (2016)].
+        |  The model is notable for its competitive performance with very few
+        |  parameters, and was implemented using #[+a("https://keras.io") Keras]
+        |  and spaCy.
+
+    +github("spacy", "examples/keras_parikh_entailment/__main__.py", "examples/keras_parikh_entailment")
diff --git a/website/usage/facts-figures.jade b/website/usage/facts-figures.jade
new file mode 100644
index 000000000..b6a548121
--- /dev/null
+++ b/website/usage/facts-figures.jade
@@ -0,0 +1,32 @@
+//- 💫 DOCS > USAGE > FACTS & FIGURES
+
+include ../_includes/_mixins
+
++section("comparison")
+    +h(2, "comparison") Feature comparison
+    include _facts-figures/_feature-comparison
+
++section("benchmarks")
+    +h(2, "benchmarks") Benchmarks
+    include _facts-figures/_benchmarks
+
+
++section("powered-by")
+    +h(2, "powered-by") Powered by spaCy
+
+    p
+        |  Here's an overview of other tools and libraries that are using spaCy
+        |  behind the scenes.
+
+    +grid
+        +card("torchtext", "https://github.com/pytorch/text", "PyTorch", "github")
+            |  PyTorch's NLP datasets and loaders use spaCy for pre-processing
+            |  and tokenization.
+
+        +card("allennlp", "https://github.com/allenai/allennlp", "Allen Institute for Artificial Intelligence", "github")
+            |  The open-source NLP research library based on PyTorch uses spaCy
+            |  for pre-processing and tokenization.
+
++section("other-libraries")
+    +h(2, "other-libraries") spaCy and other libraries
+    include _facts-figures/_other-libraries
diff --git a/website/usage/index.jade b/website/usage/index.jade
new file mode 100644
index 000000000..495a9863b
--- /dev/null
+++ b/website/usage/index.jade
@@ -0,0 +1,27 @@
+//- 💫 DOCS > USAGE
+
+include ../_includes/_mixins
+
+p
+    |  spaCy is compatible with #[strong 64-bit CPython 2.6+&#8725;3.3+] and
+    |  runs on #[strong Unix/Linux], #[strong macOS/OS X] and
+    |  #[strong Windows]. The latest spaCy releases are
+    |  available over #[+a("https://pypi.python.org/pypi/spacy") pip] (source
+    |  packages only) and #[+a("https://anaconda.org/conda-forge/spacy") conda].
+    |  Installation requires a working build environment. See notes on
+    |  #[a(href="#source-ubuntu") Ubuntu], #[a(href="#source-osx") macOS/OS X]
+    |  and #[a(href="#source-windows") Windows] for details.
+
++section("quickstart")
+    include _install/_quickstart
+
++section("instructions")
+    +h(2, "installation") Installation instructions
+    include _install/_instructions
+
++section("troubleshooting")
+    +h(2, "troubleshooting") Troubleshooting guide
+    include _install/_troubleshooting
+
++section("changelog")
+    include _install/_changelog
diff --git a/website/usage/linguistic-features.jade b/website/usage/linguistic-features.jade
new file mode 100644
index 000000000..ef8783471
--- /dev/null
+++ b/website/usage/linguistic-features.jade
@@ -0,0 +1,38 @@
+//- 💫 DOCS > USAGE > LINGUISTIC FEATURES
+
+include ../_includes/_mixins
+
+p
+    |  Processing raw text intelligently is difficult: most words are rare, and
+    |  it's common for words that look completely different to mean almost the
+    |  same thing. The same words in a different order can mean something
+    |  completely different. Even splitting text into useful word-like units can
+    |  be difficult in many languages. While it's possible to solve some
+    |  problems starting from only the raw characters, it's usually better to
+    |  use linguistic knowledge to add useful information. That's exactly what
+    |  spaCy is designed to do: you put in raw text, and get back a
+    |  #[+api("doc") #[code Doc]] object, that comes with a variety of
+    |  annotations.
+
++section("pos-tagging")
+    +h(2, "pos-tagging") Part-of-speech tagging
+        +tag-model("tagger", "dependency parse")
+    include _linguistic-features/_pos-tagging
+
++section("dependency-parse")
+    +h(2, "dependency-parse") Dependency parsing
+        +tag-model("dependency parse")
+    include _linguistic-features/_dependency-parse
+
++section("named-entities")
+    +h(2, "named-entities") Named Entities
+        +tag-model("named entities")
+    include _linguistic-features/_named-entities
+
++section("tokenization")
+    +h(2, "tokenization") Tokenization
+    include _linguistic-features/_tokenization
+
++section("rule-based-matching")
+    +h(2, "rule-based-matching") Rule-based matching
+    include _linguistic-features/_rule-based-matching
diff --git a/website/usage/models.jade b/website/usage/models.jade
new file mode 100644
index 000000000..11a0901f4
--- /dev/null
+++ b/website/usage/models.jade
@@ -0,0 +1,37 @@
+//- 💫 DOCS > USAGE > MODELS
+
+include ../_includes/_mixins
+
+p
+    |  As of v1.7.0, models for spaCy can be installed as #[strong Python packages].
+    |  This means that they're a component of your application, just like any
+    |  other module. They're versioned and can be defined as a dependency in your
+    |  #[code requirements.txt]. Models can be installed from a download URL or
+    |  a local directory, manually or via #[+a("https://pypi.python.org/pypi/pip") pip].
+    |  Their data can be located anywhere on your file system.
+
++aside("Important note")
+    |  If you're upgrading to spaCy v1.7.x or v2.x, you need to
+    |  #[strong download the new models]. If you've trained statistical models
+    |  that use spaCy's annotations, you should #[strong retrain your models]
+    |  after updating spaCy. If you don't retrain, you may suffer train/test
+    |  skew, which might decrease your accuracy.
+
++section("quickstart")
+    include _models/_quickstart
+
++section("available")
+    +h(2, "available") Available models
+    include _models/_available-models
+
++section("install")
+    +h(2, "download") Installing and using models
+    include _models/_install
+
++section("languages")
+    +h(2, "languages") Language support
+    include _models/_languages
+
++section("production")
+    +h(2, "production") Using models in production
+    include _models/_production
diff --git a/website/usage/processing-pipelines.jade b/website/usage/processing-pipelines.jade
new file mode 100644
index 000000000..0bb96780e
--- /dev/null
+++ b/website/usage/processing-pipelines.jade
@@ -0,0 +1,25 @@
+//- 💫 DOCS > USAGE > PIPELINE
+
+include ../_includes/_mixins
+
+include _spacy-101/_pipelines
+
++section("pipelines")
+    +h(2, "pipelines") How pipelines work
+    include _processing-pipelines/_pipelines
+
++section("examples")
+    +h(2, "examples") Examples
+    include _processing-pipelines/_examples
+
++section("multithreading")
+    +h(2, "multithreading") Multi-threading
+    include _processing-pipelines/_multithreading
+
++section("user-hooks")
+    +h(2, "user-hooks") User hooks
+    include _processing-pipelines/_user-hooks
+
++section("serialization")
+    +h(2, "serialization") Serialization
+    include _processing-pipelines/_serialization
diff --git a/website/usage/resources.jade b/website/usage/resources.jade
new file mode 100644
index 000000000..33a2a45aa
--- /dev/null
+++ b/website/usage/resources.jade
@@ -0,0 +1,125 @@
+//- 💫 DOCS > USAGE > RESOURCES
+
+include ../_includes/_mixins
+
++aside("Contribute to this page")
+    |  Have you built something cool with spaCy or come across a paper, book or
+    |  course that should be featured here?
+    |  #[a(href="mailto:#{EMAIL}") Let us know!]
+
++section("libraries")
+    +h(2, "libraries") Third-party libraries
+
+    +grid
+        +card("neuralcoref", "https://github.com/huggingface/neuralcoref", "Hugging Face", "github")
+            |  State-of-the-art coreference resolution based on neural nets
+            |  and spaCy
+
+        +card("rasa_nlu", "https://github.com/golastmile/rasa_nlu", "LastMile", "github")
+            |  High level APIs for building your own language parser using
+            |  existing NLP and ML libraries.
+
+        +card("textacy", "https://github.com/chartbeat-labs/textacy", "Burton DeWilde", "github")
+            |  Higher-level NLP built on spaCy.
+
+        +card("spacyr", "https://github.com/kbenoit/spacyr", "Kenneth Benoit", "github")
+            |  An R wrapper for spaCy.
+
+        +card("spacy_api", "https://github.com/kootenpv/spacy_api", "Pascal van Kooten", "github")
+            |  Server/client to load models in a separate, dedicated process.
+
+        +card("spacy-api-docker", "https://github.com/jgontrum/spacy-api-docker", "Johannes Gontrum", "github")
+            |  spaCy accessed by a REST API, wrapped in a Docker container.
+
+        +card("spacy-nlp-zeromq", "https://github.com/pasupulaphani/spacy-nlp-docker", "Phaninder Pasupula", "github")
+            |  Docker image exposing spaCy with ZeroMQ bindings.
+
+        +card("spacy-nlp", "https://github.com/kengz/spacy-nlp", "Wah Loon Keng", "github")
+            |  Expose spaCy NLP text parsing to Node.js (and other languages)
+            |  via Socket.IO.
+
+    .u-text-right
+        +button("https://github.com/search?o=desc&q=spacy&s=stars&type=Repositories&utf8=%E2%9C%93", false, "primary", "small") See more projects on GitHub
+
++section("demos")
+    +h(2, "demos") Demos & Visualizations
+
+    +grid
+        +card("Neural coref", "https://huggingface.co/coref/", "Hugging Face")
+            +image("/assets/img/resources/neuralcoref.jpg").o-block-small
+            |  State-of-the-art coreference resolution based on neural nets
+            |  and spaCy.
+
+        +card("sense2vec", "https://demos.explosion.ai/sense2vec", "Matthew Honnibal and Ines Montani")
+            +image("/assets/img/resources/sense2vec.jpg").o-block-small
+            |  Semantic analysis of the Reddit hivemind using sense2vec and spaCy.
+
+        +card("displaCy", "https://demos.explosion.ai/displacy", "Ines Montani")
+            +image("/assets/img/resources/displacy.jpg").o-block-small
+            |  An open-source NLP visualiser for the modern web.
+
+        +card("displaCy ENT", "https://demos.explosion.ai/displacy-ent", "Ines Montani")
+            +image("/assets/img/resources/displacy-ent.jpg").o-block-small
+            |  An open-source named entity visualiser for the modern web.
+
++section("books")
+    +h(2, "books") Books & Courses
+
+    +grid
+        +card("Natural Language Processing Fundamentals in Python", "https://www.datacamp.com/courses/natural-language-processing-fundamentals-in-python", "Katharine Jarmul (Datacamp, 2017)", "course")
+            |  An interactive online course on everything you need to know about
+            |  Natural Language Processing in Python, featuring spaCy and NLTK.
+
+        +card("Introduction to Machine Learning with Python: A Guide for Data Scientists", "https://books.google.com/books?id=vbQlDQAAQBAJ", "Andreas C. Müller and Sarah Guido (O'Reilly, 2016)", "book")
+            |  Andreas is a lead developer of Scikit-Learn, and Sarah is a lead
+            |  data scientist at Mashable. We're proud to get a mention.
+
+        +card("Text Analytics with Python", "https://www.amazon.com/Text-Analytics-Python-Real-World-Actionable/dp/148422387X", "Dipanjan Sarkar (Apress / Springer, 2016)", "book")
+            |  A Practical Real-World Approach to Gaining Actionable Insights
+            |  from your Data
+
++section("notebooks")
+    +h(2, "notebooks") Jupyter notebooks
+
+    +grid
+        +card("Modern NLP in Python", gh("spacy-notebooks", "notebooks/conference_notebooks/modern_nlp_in_python.ipynb"), "Patrick Harrison", "jupyter")
+            |  Introduction to NLP in Python using spaCy and Gensim. Presented
+            |  at PyData DC 2016.
+
+        +card("Advanced Text Analysis", gh("spacy-notebooks", "notebooks/conference_notebooks/advanced_text_analysis.ipynb"), "Jonathan Reeve", "jupyter")
+            |  Advanced Text Analysis with spaCy and Scikit-Learn. Presented at
+            |  NYU during NYCDH Week 2017.
+
+    .u-text-right
+        +button(gh("spacy-notebooks"), false, "primary", "small") See more notebooks on GitHub
+
++section("research")
+    +h(2, "research") Research systems
+
+    p Researchers are using spaCy to build ambitious, next-generation text processing technologies. spaCy is particularly popular amongst the biomedical NLP community, who are working on extracting knowledge from the huge volume of literature in their field.
+
+    +grid
+        +card(false, "https://www.semanticscholar.org/paper/Choosing-an-NLP-Library-for-Analyzing-Software-Doc-Omran-Treude/72f280e47e91b30af24205fa24d53247605aa591", "Fouad Nasser A. Al Omran et al. (2017)", "book", "third")
+            |  Choosing an NLP Library for Analyzing Software Documentation: A
+            |  Systematic Literature Review and a Series of Experiments
+
+        +card(false, "https://www.semanticscholar.org/paper/Mixing-Dirichlet-Topic-Models-and-Word-Embeddings-Moody/bf8116e06f7b498c6abfbf97aeb67d0838c08609", "Christopher E. Moody (2016)", "book", "third")
+            |  Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
+
+        +card(false, "https://www.semanticscholar.org/paper/Refactoring-the-Genia-Event-Extraction-Shared-Task-Kim-Wang/06d94b64a7bd2d3433f57caddad5084435d6a91f", "Jin-Dong Kim et al. (2016)", "book", "third")
+            |  Refactoring the Genia Event Extraction Shared Task Toward a
+            |  General Framework for IE-Driven KB Development
+
+        +card(false, "https://www.semanticscholar.org/paper/Predicting-Pre-click-Quality-for-Native-Zhou-Redi/564985430ff2fbc3a9daa9c2af8997b7f5046da8", "Ke Zhou et al. (2016)", "book", "third")
+            |  Predicting Pre-click Quality for Native Advertisements
+
+        +card(false, "https://www.semanticscholar.org/paper/Threat-detection-in-online-discussions-Wester-%C3%98vrelid/f4150e2fb4d8646ebc2ea84f1a86afa1b593239b", "Aksel Wester et al. (2016)", "book", "third")
+            |  Threat detection in online discussions
+
+        +card(false, "https://www.semanticscholar.org/paper/Distributional-semantics-for-understanding-spoken-Korpusik-Huang/5f55c5535e80d3e5ed7f1f0b89531e32725faff5", "Mandy Korpusik et al. (2016)", "book", "third")
+            |  Distributional semantics for understanding spoken meal
+            |  descriptions
+
+    .u-text-right
+        +button("https://scholar.google.com/scholar?scisbd=2&q=spacy&hl=en&as_sdt=1,5&as_vis=1", false, "primary", "small")
+            |  See 200+ papers on Google Scholar
diff --git a/website/usage/spacy-101.jade b/website/usage/spacy-101.jade
new file mode 100644
index 000000000..3b75202f7
--- /dev/null
+++ b/website/usage/spacy-101.jade
@@ -0,0 +1,300 @@
+//- 💫 DOCS > USAGE > SPACY 101
+
+include ../_includes/_mixins
+
+p
+    |  Whether you're new to spaCy, or just want to brush up on some
+    |  NLP basics and implementation details – this page should have you covered.
+    |  Each section will explain one of spaCy's features in simple terms and
+    |  with examples or illustrations. Some sections will also reappear across
+    |  the usage guides as a quick introduction.
+
++aside("Help us improve the docs")
+    |  Did you spot a mistake or come across explanations that
+    |  are unclear? We always appreciate improvement
+    |  #[+a(gh("spaCy") + "/issues") suggestions] or
+    |  #[+a(gh("spaCy") + "/pulls") pull requests]. You can find a "Suggest
+    |  edits" link at the bottom of each page that points you to the source.
+
++h(2, "whats-spacy") What's spaCy?
+
++grid.o-no-block
+    +grid-col("half")
+        p
+            |  spaCy is a #[strong free, open-source library] for advanced
+            |  #[strong Natural Language Processing] (NLP) in Python.
+
+        p
+            |  If you're working with a lot of text, you'll eventually want to
+            |  know more about it. For example, what's it about? What do the
+            |  words mean in context? Who is doing what to whom? What companies
+            |  and products are mentioned? Which texts are similar to each other?
+
+        p
+            |  spaCy is designed specifically for #[strong production use] and
+            |  helps you build applications that process and "understand"
+            |  large volumes of text. It can be used to build
+            |  #[strong information extraction] or
+            |  #[strong natural language understanding] systems, or to
+            |  pre-process text for #[strong deep learning].
+
+    +table-of-contents
+        +item #[+a("#features") Features]
+        +item #[+a("#annotations") Linguistic annotations]
+        +item #[+a("#annotations-token") Tokenization]
+        +item #[+a("#annotations-pos-deps") POS tags and dependencies]
+        +item #[+a("#annotations-ner") Named entities]
+        +item #[+a("#vectors-similarity") Word vectors and similarity]
+        +item #[+a("#pipelines") Pipelines]
+        +item #[+a("#vocab") Vocab, hashes and lexemes]
+        +item #[+a("#serialization") Serialization]
+        +item #[+a("#training") Training]
+        +item #[+a("#language-data") Language data]
+        +item #[+a("#lightning-tour") Lightning tour]
+        +item #[+a("#architecture") Architecture]
+        +item #[+a("#community") Community & FAQ]
+
++h(3, "what-spacy-isnt") What spaCy isn't
+
++list
+    +item #[strong spaCy is not a platform or "an API"].
+        |  Unlike a platform, spaCy does not provide a software as a service, or
+        |  a web application. It's an open-source library designed to help you
+        |  build NLP applications, not a consumable service.
+    +item #[strong spaCy is not an out-of-the-box chat bot engine].
+        |  While spaCy can be used to power conversational applications, it's
+        |  not designed specifically for chat bots, and only provides the
+        |  underlying text processing capabilities.
+    +item #[strong spaCy is not research software].
+        |  It's built on the latest research, but it's designed to get
+        |  things done. This leads to fairly different design decisions than
+        |  #[+a("https://github./nltk/nltk") NLTK]
+        |  or #[+a("https://stanfordnlp.github.io/CoreNLP/") CoreNLP], which were
+        |  created as platforms for teaching and research. The main difference
+        |  is that spaCy is integrated and opinionated. spaCy tries to avoid asking
+        |  the user to choose between multiple algorithms that deliver equivalent
+        |  functionality. Keeping the menu small lets spaCy deliver generally better
+        |  performance and developer experience.
+    +item #[strong spaCy is not a company].
+        |  It's an open-source library. Our company publishing spaCy and other
+        |  software is called #[+a(COMPANY_URL, true) Explosion AI].
+
++section("features")
+    +h(2, "features") Features
+
+    p
+        |  In the documentation, you'll come across mentions of spaCy's
+        |  features and capabilities. Some of them refer to linguistic concepts,
+        |  while others are related to more general machine learning
+        |  functionality.
+
+    +aside
+        |  If one of spaCy's functionalities #[strong needs a model], it means
+        |  that you need to have one of the available
+        |  #[+a("/models") statistical models] installed. Models are used
+        |  to #[strong predict] linguistic annotations – for example, if a word
+        |  is a verb or a noun.
+
+    +table(["Name", "Description", "Needs model"])
+        +row
+            +cell #[strong Tokenization]
+            +cell Segmenting text into words, punctuations marks etc.
+            +cell #[+procon("con")]
+
+        +row
+            +cell #[strong Part-of-speech] (POS) #[strong Tagging]
+            +cell Assigning word types to tokens, like verb or noun.
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Dependency Parsing]
+            +cell
+                |  Assigning syntactic dependency labels, describing the
+                |  relations between individual tokens, like subject or object.
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Lemmatization]
+            +cell
+                |  Assigning the base forms of words. For example, the lemma of
+                |  "was" is "be", and the lemma of "rats" is "rat".
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Sentence Boundary Detection] (SBD)
+            +cell Finding and segmenting individual sentences.
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Named Entity Recongition] (NER)
+            +cell
+                |  Labelling named "real-world" objects, like persons, companies
+                |  or locations.
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Similarity]
+            +cell
+                |  Comparing words, text spans and documents and how similar
+                |  they are to each other.
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Text Classification]
+            +cell
+                |  Assigning categories or labels to a whole document, or parts
+                |  of a document.
+            +cell #[+procon("pro")]
+
+        +row
+            +cell #[strong Rule-based Matching]
+            +cell
+                |  Finding sequences of tokens based on their texts and
+                |  linguistic annotations, similar to regular expressions.
+            +cell #[+procon("con")]
+
+        +row
+            +cell #[strong Training]
+            +cell Updating and improving a statistical model's predictions.
+            +cell #[+procon("neutral")]
+
+        +row
+            +cell #[strong Serialization]
+            +cell Saving objects to files or byte strings.
+            +cell #[+procon("neutral")]
+
+    +h(2, "annotations") Linguistic annotations
+
+    p
+        |  spaCy provides a variety of linguistic annotations to give you
+        |  #[strong insights into a text&apos;s grammatical structure]. This
+        |  includes the word types, like the parts of speech, and how the words
+        |  are related to each other. For example, if you're analysing text, it
+        |  makes a huge difference whether a noun is the subject of a sentence,
+        |  or the object – or whether "google" is used as a verb, or refers to
+        |  the website or company in a specific context.
+
+    p
+        |  Once you've downloaded and installed a #[+a("/usage/models") model],
+        |  you can load it via #[+api("spacy#load") #[code spacy.load()]]. This will
+        |  return a #[code Language] object contaning all components and data needed
+        |  to process text. We usually call it #[code nlp]. Calling the #[code nlp]
+        |  object on a string of text will return a processed #[code Doc]:
+
+    +code.
+        import spacy
+
+        nlp = spacy.load('en')
+        doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
+
+    p
+        |  Even though a #[code Doc] is processed – e.g. split into individual words
+        |  and annotated – it still holds #[strong all information of the original text],
+        |  like whitespace characters. You can always get the offset of a token into the
+        |  original string, or reconstruct the original by joining the tokens and their
+        |  trailing whitespace. This way, you'll never lose any information
+        |  when processing text with spaCy.
+
+    +h(3, "annotations-token") Tokenization
+
+    include _spacy-101/_tokenization
+
+    +infobox
+        |  To learn more about how spaCy's tokenization rules work in detail,
+        |  how to #[strong customise and replace] the default tokenizer and how to
+        |  #[strong add language-specific data], see the usage guides on
+        |  #[+a("/usage/adding-languages") adding languages] and
+        |  #[+a("/usage/linguistic-features#tokenization") customising the tokenizer].
+
+    +h(3, "annotations-pos-deps") Part-of-speech tags and dependencies
+        +tag-model("dependency parse")
+
+    include _spacy-101/_pos-deps
+
+    +infobox
+        |  To learn more about #[strong part-of-speech tagging] and rule-based
+        |  morphology, and how to #[strong navigate and use the parse tree]
+        |  effectively, see the usage guides on
+        |  #[+a("/usage/linguistic-features#pos-tagging") part-of-speech tagging] and
+        |  #[+a("/usage/linguistic-features#dependency-parse") using the dependency parse].
+
+    +h(3, "annotations-ner") Named Entities
+        +tag-model("named entities")
+
+    include _spacy-101/_named-entities
+
+    +infobox
+        |  To learn more about entity recognition in spaCy, how to
+        |  #[strong add your own entities] to a document and how to
+        |  #[strong train and update] the entity predictions of a model, see the
+        |  usage guides on
+        |  #[+a("/usage/linguistic-features#named-entities") named entity recognition] and
+        |  #[+a("/usage/training#ner") training the named entity recognizer].
+
+    +h(2, "vectors-similarity") Word vectors and similarity
+        +tag-model("vectors")
+
+    include _spacy-101/_similarity
+
+    include _spacy-101/_word-vectors
+
+    +infobox
+        |  To learn more about word vectors, how to #[strong customise them] and
+        |  how to load #[strong your own vectors] into spaCy, see the usage
+        |  guide on
+        |  #[+a("/usage/vectors-similarity") using word vectors and semantic similarities].
+
+    +h(2, "pipelines") Pipelines
+
+    include _spacy-101/_pipelines
+
+    +infobox
+        |  To learn more about #[strong how processing pipelines work] in detail,
+        |  how to enable and disable their components, and how to
+        |  #[strong create your own], see the usage guide on
+        |  #[+a("/usage/processing-pipelines") language processing pipelines].
+
+    +h(2, "vocab") Vocab, hashes and lexemes
+
+    include _spacy-101/_vocab
+
+    +h(2, "serialization") Serialization
+
+    include _spacy-101/_serialization
+
+    +infobox
+        |  To learn more about how to #[strong save and load your own models],
+        |  see the usage guide on
+        |  #[+a("/usage/training#saving-loading") saving and loading].
+
+    +h(2, "training") Training
+
+    include _spacy-101/_training
+
+    +infobox
+        |  To learn more about #[strong training and updating] models, how to create
+        |  training data and how to improve spaCy's named entity recognition models,
+        |  see the usage guides on #[+a("/usage/training") training].
+
+    +h(2, "language-data") Language data
+
+    include _spacy-101/_language-data
+
+    +infobox
+        |  To learn more about the individual components of the language data and
+        |  how to #[strong add a new language] to spaCy in preparation for training
+        |  a language model, see the usage guide on
+        |  #[+a("/usage/adding-languages") adding languages].
+
+
++section("lightning-tour")
+    +h(2, "lightning-tour") Lightning tour
+    include _spacy-101/_lightning-tour
+
++section("architecture")
+    +h(2, "architecture") Architecture
+    include _spacy-101/_architecture
+
++section("community-faq")
+    +h(2, "community") Community & FAQ
+    include _spacy-101/_community-faq
diff --git a/website/usage/text-classification.jade b/website/usage/text-classification.jade
new file mode 100644
index 000000000..8a0e93450
--- /dev/null
+++ b/website/usage/text-classification.jade
@@ -0,0 +1,9 @@
+//- 💫 DOCS > USAGE > TEXT CLASSIFICATION
+
+include ../_includes/_mixins
+
++under-construction
+
++h(2, "example") Example
+
++github("spacy", "examples/training/train_textcat.py")
diff --git a/website/usage/training.jade b/website/usage/training.jade
new file mode 100644
index 000000000..8f15668c4
--- /dev/null
+++ b/website/usage/training.jade
@@ -0,0 +1,33 @@
+//- 💫 DOCS > USAGE > TRAINING
+
+include ../_includes/_mixins
+
+p
+    |  This guide describes how to train new statistical models for spaCy's
+    |  part-of-speech tagger, named entity recognizer and dependency parser.
+    |  Once the model is trained, you can then
+    |  #[+a("/usage/models#saving-loading") save and load] it.
+
++section("basics")
+    +h(2, "basics") Training basics
+    include _training/_basics
+
++section("ner")
+    +h(2, "ner") Training the named entity recognizer
+    include _training/_ner
+
++section("tagger-parser")
+    +h(2, "tagger-parser") Training the tagger and parser
+    include _training/_tagger-parser
+
++section("similarity")
+    +h(2, "similarity") Training a similarity model
+    include _training/_similarity
+
++section("textcat")
+    +h(2, "textcat") Training a text classification model
+    include _training/_textcat
+
++section("saving-loading")
+    +h(2, "saving-loading") Saving and loading models
+    include _training/_saving-loading
diff --git a/website/usage/v2.jade b/website/usage/v2.jade
new file mode 100644
index 000000000..8737c0b76
--- /dev/null
+++ b/website/usage/v2.jade
@@ -0,0 +1,520 @@
+//- 💫 DOCS > USAGE > WHAT'S NEW IN V2.0
+
+include ../_includes/_mixins
+
+p
+    |  We're very excited to finally introduce spaCy v2.0! On this page, you'll
+    |  find a summary of the new features, information on the backwards
+    |  incompatibilities, including a handy overview of what's been renamed or
+    |  deprecated. To help you make the most of v2.0, we also
+    |  #[strong re-wrote almost all of the usage guides and API docs], and added
+    |  more real-world examples. If you're new to spaCy, or just want to brush
+    |  up on some NLP basics and the details of the library, check out
+    |  the #[+a("/usage/spacy-101") spaCy 101 guide] that explains the most
+    |  important concepts with examples and illustrations.
+
++h(2, "summary") Summary
+
++grid.o-no-block
+    +grid-col("half")
+
+        p This release features
+            |  entirely new #[strong deep learning-powered models] for spaCy's tagger,
+            |  parser and entity recognizer. The new models are #[strong 20x smaller]
+            |  than the linear models that have powered spaCy until now: from 300 MB to
+            |  only 15 MB.
+
+        p
+            |  We've also made several usability improvements that are
+            |  particularly helpful for #[strong production deployments]. spaCy
+            |  v2 now fully supports the Pickle protocol, making it easy to use
+            |  spaCy with #[+a("https://spark.apache.org/") Apache Spark]. The
+            |  string-to-integer mapping is #[strong no longer stateful], making
+            |  it easy to reconcile annotations made in different processes.
+            |  Models are smaller and use less memory, and the APIs for serialization
+            |  are now much more consistent.
+
+    +table-of-contents
+        +item #[+a("#summary") Summary]
+        +item #[+a("#features") New features]
+        +item #[+a("#features-models") Neural network models]
+        +item #[+a("#features-pipelines") Improved processing pipelines]
+        +item #[+a("#features-text-classification") Text classification]
+        +item #[+a("#features-hash-ids") Hash values instead of integer IDs]
+        +item #[+a("#features-serializer") Saving, loading and serialization]
+        +item #[+a("#features-displacy") displaCy visualizer]
+        +item #[+a("#features-language") Language data and lazy loading]
+        +item #[+a("#features-matcher") Revised matcher API and phrase matcher]
+        +item #[+a("#incompat") Backwards incompatibilities]
+        +item #[+a("#migrating") Migrating from spaCy v1.x]
+        +item #[+a("#benchmarks") Benchmarks]
+
+p
+    |  The main usability improvements you'll notice in spaCy v2.0 are around
+    |  #[strong defining, training and loading your own models] and components.
+    |  The new neural network models make it much easier to train a model from
+    |  scratch, or update an existing model with a few examples. In v1.x, the
+    |  statistical models depended on the state of the #[code Vocab]. If you
+    |  taught the model a new word, you would have to save and load a lot of
+    |  data — otherwise the model wouldn't correctly recall the features of your
+    |  new example. That's no longer the case.
+
+p
+    |  Due to some clever use of hashing, the statistical models
+    |  #[strong never change size], even as they learn new vocabulary items.
+    |  The whole pipeline is also now fully differentiable. Even if you don't
+    |  have explicitly annotated data, you can update spaCy using all the
+    |  #[strong latest deep learning tricks] like adversarial training, noise
+    |  contrastive estimation or reinforcement learning.
+
++section("features")
+    +h(2, "features") New features
+
+    p
+        |  This section contains an overview of the most important
+        |  #[strong new features and improvements]. The #[+a("/api") API docs]
+        |  include additional  deprecation notes. New methods and functions that
+        |  were introduced in this version are marked with a #[+tag-new(2)] tag.
+
+    +h(3, "features-models") Convolutional neural network models
+
+    +aside-code("Example", "bash").
+        spacy download en # default English model
+        spacy download de # default German model
+        spacy download fr # default French model
+        spacy download es # default Spanish model
+        spacy download xx_ent_wiki_sm # multi-language NER
+
+    p
+        |  spaCy v2.0 features new neural models for tagging,
+        |  parsing and entity recognition. The models have
+        |  been designed and implemented from scratch specifically for spaCy, to
+        |  give you an unmatched balance of speed, size and accuracy. The new
+        |  models are #[strong 10&times; smaller], #[strong 20% more accurate],
+        |  and #[strong just as fast] as the previous generation.
+        |  #[strong GPU usage] is now supported via
+        |  #[+a("http://chainer.org") Chainer]'s CuPy module.
+
+    +infobox
+        |  #[+label-inline Usage:] #[+a("/models") Models directory],
+        |  #[+a("/usage/#gpu") Using spaCy with GPU]
+
+    +h(3, "features-pipelines") Improved processing pipelines
+
+    +aside-code("Example").
+        # Modify an existing pipeline
+        nlp = spacy.load('en')
+        nlp.pipeline.append(my_component)
+
+        # Register a factory to create a component
+        spacy.set_factory('my_factory', my_factory)
+        nlp = Language(pipeline=['my_factory', mycomponent])
+
+    p
+        |  It's now much easier to #[strong customise the pipeline] with your own
+        |  components, functions that receive a #[code Doc] object, modify and
+        |  return it. If your component is stateful, you can define and register a
+        |  factory which receives the shared #[code Vocab] object and returns a
+        |  component. spaCy's default components can be added to your pipeline by
+        |  using their string IDs. This way, you won't have to worry about finding
+        |  and implementing them – simply add #[code "tagger"] to the pipeline,
+        |  and spaCy will know what to do.
+
+    +image
+        include ../assets/img/pipeline.svg
+
+    +infobox
+        |  #[+label-inline API:] #[+api("language") #[code Language]]
+        |  #[+label-inline Usage:] #[+a("/usage/language-processing-pipeline") Processing text]
+
+    +h(3, "features-text-classification") Text classification
+
+    +aside-code("Example").
+        from spacy.lang.en import English
+        nlp = English(pipeline=['tensorizer', 'tagger', 'textcat'])
+
+    p
+        |  spaCy v2.0 lets you add text categorization models to spaCy pipelines.
+        |  The model supports classification with multiple, non-mutually exclusive
+        |  labels – so multiple labels can apply at once. You can change the model
+        |  architecture rather easily, but by default, the #[code TextCategorizer]
+        |  class uses a convolutional neural network to assign position-sensitive
+        |  vectors to each word in the document.
+
+    +infobox
+        |  #[+label-inline API:] #[+api("textcategorizer") #[code TextCategorizer]],
+        |  #[+api("doc#attributes") #[code Doc.cats]],
+        |  #[+api("goldparse#attributes") #[code GoldParse.cats]]#[br]
+        |  #[+label-inline Usage:] #[+a("/usage/text-classification") Text classification]
+
+    +h(3, "features-hash-ids") Hash values instead of integer IDs
+
+    +aside-code("Example").
+        doc = nlp(u'I love coffee')
+        assert doc.vocab.strings[u'coffee'] == 3197928453018144401
+        assert doc.vocab.strings[3197928453018144401] == u'coffee'
+
+        beer_hash = doc.vocab.strings.add(u'beer')
+        assert doc.vocab.strings[u'beer'] == beer_hash
+        assert doc.vocab.strings[beer_hash] == u'beer'
+
+    p
+        |  The #[+api("stringstore") #[code StringStore]] now resolves all strings
+        |  to hash values instead of integer IDs. This means that the string-to-int
+        |  mapping #[strong no longer depends on the vocabulary state], making a lot
+        |  of workflows much simpler, especially during training. Unlike integer IDs
+        |  in spaCy v1.x, hash values will #[strong always match] – even across
+        |  models. Strings can now be added explicitly using the new
+        |  #[+api("stringstore#add") #[code Stringstore.add]] method. A token's hash
+        |  is available via #[code token.orth].
+
+    +infobox
+        |  #[+label-inline API:] #[+api("stringstore") #[code StringStore]]
+        |  #[+label-inline Usage:] #[+a("/usage/spacy-101#vocab") Vocab, hashes and lexemes 101]
+
+    +h(3, "features-serializer") Saving, loading and serialization
+
+    +aside-code("Example").
+        nlp = spacy.load('en') # shortcut link
+        nlp = spacy.load('en_core_web_sm') # package
+        nlp = spacy.load('/path/to/en') # unicode path
+        nlp = spacy.load(Path('/path/to/en')) # pathlib Path
+
+        nlp.to_disk('/path/to/nlp')
+        nlp = English().from_disk('/path/to/nlp')
+
+    p
+        |  spay's serialization API has been made consistent across classes and
+        |  objects. All container classes, i.e. #[code Language], #[code Doc],
+        |  #[code Vocab] and #[code StringStore] now have a #[code to_bytes()],
+        |  #[code from_bytes()], #[code to_disk()] and #[code from_disk()] method
+        |  that supports the Pickle protocol.
+
+    p
+        |  The improved #[code spacy.load] makes loading models easier and more
+        |  transparent. You can load a model by supplying its
+        |  #[+a("/usage/models#usage") shortcut link], the name of an installed
+        |  #[+a("/usage/saving-loading#generating") model package] or a path.
+        |  The #[code Language] class to initialise will be determined based on the
+        |  model's settings. For a blank language, you can import the class directly,
+        |  e.g. #[code from spacy.lang.en import English].
+
+    +infobox
+        |  #[+label-inline API:] #[+api("spacy#load") #[code spacy.load]], #[+api("binder") #[code Binder]]
+        |  #[+label-inline Usage:] #[+a("/usage/saving-loading") Saving and loading]
+
+    +h(3, "features-displacy") displaCy visualizer with Jupyter support
+
+    +aside-code("Example").
+        from spacy import displacy
+        doc = nlp(u'This is a sentence about Facebook.')
+        displacy.serve(doc, style='dep') # run the web server
+        html = displacy.render(doc, style='ent') # generate HTML
+
+    p
+        |  Our popular dependency and named entity visualizers are now an official
+        |  part of the spaCy library. displaCy can run a simple web server, or
+        |  generate raw HTML markup or SVG files to be exported. You can pass in one
+        |  or more docs, and customise the style. displaCy also auto-detects whether
+        |  you're running #[+a("https://jupyter.org") Jupyter] and will render the
+        |  visualizations in your notebook.
+
+    +infobox
+        |  #[+label-inline API:] #[+api("displacy") #[code displacy]]
+        |  #[+label-inline Usage:] #[+a("/usage/visualizers") Visualizing spaCy]
+
+    +h(3, "features-language") Improved language data and lazy loading
+
+    p
+        |  Language-specfic data now lives in its own submodule, #[code spacy.lang].
+        |  Languages are lazy-loaded, i.e. only loaded when you import a
+        |  #[code Language] class, or load a model that initialises one. This allows
+        |  languages to contain more custom data, e.g. lemmatizer lookup tables, or
+        |  complex regular expressions. The language data has also been tidied up
+        |  and simplified. spaCy now also supports simple lookup-based lemmatization.
+
+    +infobox
+        |  #[+label-inline API:] #[+api("language") #[code Language]]
+        |  #[+label-inline Code:] #[+src(gh("spaCy", "spacy/lang")) #[code spacy/lang]]
+        |  #[+label-inline Usage:] #[+a("/usage/adding-languages") Adding languages]
+
+    +h(3, "features-matcher") Revised matcher API and phrase matcher
+
+    +aside-code("Example").
+        from spacy.matcher import Matcher, PhraseMatcher
+
+        matcher = Matcher(nlp.vocab)
+        matcher.add('HEARTS', None, [{'ORTH': '❤️', 'OP': '+'}])
+
+        phrasematcher = PhraseMatcher(nlp.vocab)
+        phrasematcher.add('OBAMA', None, nlp(u"Barack Obama"))
+
+    p
+        |  Patterns can now be added to the matcher by calling
+        |  #[+api("matcher-add") #[code matcher.add()]] with a match ID, an optional
+        |  callback function to be invoked on each match, and one or more patterns.
+        |  This allows you to write powerful, pattern-specific logic using only one
+        |  matcher. For example, you might only want to merge some entity types,
+        |  and set custom flags for other matched patterns. The new
+        |  #[+api("phrasematcher") #[code PhraseMatcher]] lets you efficiently
+        |  match very large terminology lists using #[code Doc] objects as match
+        |  patterns.
+
+    +infobox
+        |  #[+label-inline API:] #[+api("matcher") #[code Matcher]],
+        |  #[+api("phrasematcher") #[code PhraseMatcher]]
+        |  #[+label-inline Usage:] #[+a("/usage/rule-based-matching") Rule-based matching]
+
++section("incompat")
+    +h(2, "incompat") Backwards incompatibilities
+
+    +table(["Old", "New"])
+        +row
+            +cell
+                |  #[code spacy.en]
+                |  #[code spacy.xx]
+            +cell
+                |  #[code spacy.lang.en]
+                |  #[code spacy.lang.xx]
+
+        +row
+            +cell #[code orth]
+            +cell #[code lang.xx.lex_attrs]
+
+        +row
+            +cell #[code syntax.iterators]
+            +cell #[code lang.xx.syntax_iterators]
+
+        +row
+            +cell #[code Language.save_to_directory]
+            +cell #[+api("language#to_disk") #[code Language.to_disk]]
+
+        +row
+            +cell #[code Language.create_make_doc]
+            +cell #[+api("language#attributes") #[code Language.tokenizer]]
+
+        +row
+            +cell
+                |  #[code Vocab.load]
+                |  #[code Vocab.load_lexemes]
+            +cell
+                |  #[+api("vocab#from_disk") #[code Vocab.from_disk]]
+                |  #[+api("vocab#from_bytes") #[code Vocab.from_bytes]]
+
+        +row
+            +cell
+                |  #[code Vocab.dump]
+            +cell
+                |  #[+api("vocab#to_disk") #[code Vocab.to_disk]]#[br]
+                |  #[+api("vocab#to_bytes") #[code Vocab.to_bytes]]
+
+        +row
+            +cell
+                |  #[code Vocab.load_vectors]
+                |  #[code Vocab.load_vectors_from_bin_loc]
+            +cell
+                |  #[+api("vectors#from_disk") #[code Vectors.from_disk]]
+                |  #[+api("vectors#from_bytes") #[code Vectors.from_bytes]]
+
+        +row
+            +cell
+                |  #[code Vocab.dump_vectors]
+            +cell
+                |  #[+api("vectors#to_disk") #[code Vectors.to_disk]]
+                |  #[+api("vectors#to_bytes") #[code Vectors.to_bytes]]
+
+        +row
+            +cell
+                |  #[code StringStore.load]
+            +cell
+                |  #[+api("stringstore#from_disk") #[code StringStore.from_disk]]
+                |  #[+api("stringstore#from_bytes") #[code StringStore.from_bytes]]
+
+        +row
+            +cell
+                |  #[code StringStore.dump]
+            +cell
+                |  #[+api("stringstore#to_disk") #[code StringStore.to_disk]]
+                |  #[+api("stringstore#to_bytes") #[code StringStore.to_bytes]]
+
+        +row
+            +cell #[code Tokenizer.load]
+            +cell
+                |  #[+api("tokenizer#from_disk") #[code Tokenizer.from_disk]]
+                |  #[+api("tokenizer#from_bytes") #[code Tokenizer.from_bytes]]
+
+        +row
+            +cell #[code Tagger.load]
+            +cell
+                |  #[+api("tagger#from_disk") #[code Tagger.from_disk]]
+                |  #[+api("tagger#from_bytes") #[code Tagger.from_bytes]]
+
+        +row
+            +cell #[code DependencyParser.load]
+            +cell
+                |  #[+api("dependencyparser#from_disk") #[code DependencyParser.from_disk]]
+                |  #[+api("dependencyparser#from_bytes") #[code DependencyParser.from_bytes]]
+
+        +row
+            +cell #[code EntityRecognizer.load]
+            +cell
+                |  #[+api("entityrecognizer#from_disk") #[code EntityRecognizer.from_disk]]
+                |  #[+api("entityrecognizer#from_bytes") #[code EntityRecognizer.from_bytes]]
+
+        +row
+            +cell #[code Matcher.load]
+            +cell -
+
+        +row
+            +cell
+                |  #[code Matcher.add_pattern]
+                |  #[code Matcher.add_entity]
+            +cell #[+api("matcher#add") #[code Matcher.add]]
+
+        +row
+            +cell #[code Matcher.get_entity]
+            +cell #[+api("matcher#get") #[code Matcher.get]]
+
+        +row
+            +cell #[code Matcher.has_entity]
+            +cell #[+api("matcher#contains") #[code Matcher.__contains__]]
+
+        +row
+            +cell #[code Doc.read_bytes]
+            +cell #[+api("binder") #[code Binder]]
+
+        +row
+            +cell #[code Token.is_ancestor_of]
+            +cell #[+api("token#is_ancestor") #[code Token.is_ancestor]]
+
+        +row
+            +cell #[code cli.model]
+            +cell -
+
++section("migrating")
+    +h(2, "migrating") Migrating from spaCy 1.x
+
+    p
+        |  Because we'e made so many architectural changes to the library, we've
+        |  tried to #[strong keep breaking changes to a minimum]. A lot of projects
+        |  follow the philosophy that if you're going to break anything, you may as
+        |  well break everything. We think migration is easier if there's a logic to
+        |  what has changed.
+
+    p
+        |  We've therefore followed a policy of avoiding breaking changes to the
+        |  #[code Doc], #[code Span] and #[code Token] objects. This way, you can
+        |  focus on only migrating the code that does training, loading and
+        |  serialization — in other words, code that works with the #[code nlp]
+        |  object directly. Code that uses the annotations should continue to work.
+
+    +infobox("Important note")
+        |  If you've trained your own models, keep in mind that your train and
+        |  runtime inputs must match. This means you'll have to
+        |  #[strong retrain your models] with spaCy v2.0.
+
+    +h(3, "migrating-saving-loading") Saving, loading and serialization
+
+    p
+        |  Double-check all calls to #[code spacy.load()] and make sure they don't
+        |  use the #[code path] keyword argument. If you're only loading in binary
+        |  data and not a model package that can construct its own #[code Language]
+        |  class and pipeline, you should now use the
+        |  #[+api("language#from_disk") #[code Language.from_disk()]] method.
+
+    +code-new.
+        nlp = spacy.load('/model')
+        nlp = English().from_disk('/model/data')
+    +code-old nlp = spacy.load('en', path='/model')
+
+    p
+        |  Review all other code that writes state to disk or bytes.
+        |  All containers, now share the same, consistent API for saving and
+        |  loading. Replace saving with #[code to_disk()] or #[code to_bytes()], and
+        |  loading with #[code from_disk()] and #[code from_bytes()].
+
+    +code-new.
+        nlp.to_disk('/model')
+        nlp.vocab.to_disk('/vocab')
+
+    +code-old.
+        nlp.save_to_directory('/model')
+        nlp.vocab.dump('/vocab')
+
+    p
+        |  If you've trained models with input from v1.x, you'll need to
+        |  #[strong retrain them] with spaCy v2.0. All previous models will not
+        |  be compatible with the new version.
+
+    +h(3, "migrating-strings") Strings and hash values
+
+    p
+        |  The change from integer IDs to hash values may not actually affect your
+        |  code very much. However, if you're adding strings to the vocab manually,
+        |  you now need to call #[+api("stringstore#add") #[code StringStore.add()]]
+        |  explicitly. You can also now be sure that the string-to-hash mapping will
+        |  always match across vocabularies.
+
+    +code-new.
+        nlp.vocab.strings.add(u'coffee')
+        nlp.vocab.strings[u'coffee']       # 3197928453018144401
+        other_nlp.vocab.strings[u'coffee'] # 3197928453018144401
+
+    +code-old.
+        nlp.vocab.strings[u'coffee']       # 3672
+        other_nlp.vocab.strings[u'coffee'] # 40259
+
+    +h(3, "migrating-languages") Processing pipelines and language data
+
+    p
+        |  If you're importing language data or #[code Language] classes, make sure
+        |  to change your import statements to import from #[code spacy.lang]. If
+        |  you've added your own custom language, it needs to be moved to
+        |  #[code spacy/lang/xx] and adjusted accordingly.
+
+    +code-new from spacy.lang.en import English
+    +code-old from spacy.en import English
+
+    p
+        |  If you've been using custom pipeline components, check out the new
+        |  guide on #[+a("/usage/language-processing-pipelines") processing pipelines].
+        |  Appending functions to the pipeline still works – but you might be able
+        |  to make this more convenient by registering "component factories".
+        |  Components of the processing pipeline can now be disabled by passing a
+        |  list of their names to the #[code disable] keyword argument on loading
+        |  or processing.
+
+    +code-new.
+        nlp = spacy.load('en', disable=['tagger', 'ner'])
+        doc = nlp(u"I don't want parsed", disable=['parser'])
+    +code-old.
+        nlp = spacy.load('en', tagger=False, entity=False)
+        doc = nlp(u"I don't want parsed", parse=False)
+
+    +h(3, "migrating-matcher") Adding patterns and callbacks to the matcher
+
+    p
+        |  If you're using the matcher, you can now add patterns in one step. This
+        |  should be easy to update – simply merge the ID, callback and patterns
+        |  into one call to #[+api("matcher#add") #[code Matcher.add()]].
+
+    +code-new.
+        matcher.add('GoogleNow', merge_phrases, [{ORTH: 'Google'}, {ORTH: 'Now'}])
+
+    +code-old.
+        matcher.add_entity('GoogleNow', on_match=merge_phrases)
+        matcher.add_pattern('GoogleNow', [{ORTH: 'Google'}, {ORTH: 'Now'}])
+
+    p
+        |  If you've been using #[strong acceptor functions], you'll need to move
+        |  this logic into the
+        |  #[+a("/usage/rule-based-matching#on_match") #[code on_match] callbacks].
+        |  The callback function is invoked on every match and will give you access to
+        |  the doc, the index of the current match and all total matches. This lets
+        |  you both accept or reject the match, and define the actions to be
+        |  triggered.
+
++section("benchmarks")
+    +h(2, "benchmarks") Benchmarks
+
+    include _facts-figures/_benchmarks-models
diff --git a/website/usage/vectors-similarity.jade b/website/usage/vectors-similarity.jade
new file mode 100644
index 000000000..1e1139b20
--- /dev/null
+++ b/website/usage/vectors-similarity.jade
@@ -0,0 +1,18 @@
+//- 💫 DOCS > USAGE > WORD VECTORS & SIMILARITIES
+
+include ../_includes/_mixins
+
++section("basics")
+    include _vectors-similarity/_basics
+
++section("in-context")
+    +h(2, "in-context") Similarities in context
+    include _vectors-similarity/_in-context
+
++section("custom")
+    +h(2, "custom") Customising word vectors
+    include _vectors-similarity/_custom
+
++section("gpu")
+    +h(2, "gpu") Storing vectors on a GPU
+    include _vectors-similarity/_gpu
diff --git a/website/docs/usage/visualizers.jade b/website/usage/visualizers.jade
similarity index 97%
rename from website/docs/usage/visualizers.jade
rename to website/usage/visualizers.jade
index 96a6bd49f..39d34aea6 100644
--- a/website/docs/usage/visualizers.jade
+++ b/website/usage/visualizers.jade
@@ -1,6 +1,6 @@
 //- 💫 DOCS > USAGE > VISUALIZERS
 
-include ../../_includes/_mixins
+include ../_includes/_mixins
 
 p
     |  As of v2.0, our popular visualizers, #[+a(DEMOS_URL + "/displacy") displaCy]
@@ -19,8 +19,8 @@ p
     |  #[+a("#jupyter") ready to be rendered and exported].
 
 +aside("What about the old visualizers?")
-    |  Our JavaScript-based visualizers #[+src(gh("displacy")) displacy.js] and
-    |  #[+src(gh("displacy-ent")) displacy-ent.js] will still be available on
+    |  Our JavaScript-based visualizers #[+src(gh("displacy")) #[code displacy.js]] and
+    |  #[+src(gh("displacy-ent")) #[code displacy-ent.js]] will still be available on
     |  GitHub. If you're looking to implement web-based visualizations, we
     |  generally recommend using those instead of spaCy's built-in
     |  #[code displacy] module. It'll allow your application to perform all
@@ -148,7 +148,7 @@ p
     |  will render whichever spans and labels it receives. This makes it
     |  especially easy to work with custom entity types. By default, displaCy
     |  comes with colours for all
-    |  #[+a("/docs/api/annotation#named-entities") entity types supported by spaCy].
+    |  #[+a("/api/annotation#named-entities") entity types supported by spaCy].
     |  If you're using custom entity types, you can use the #[code colors]
     |  setting to add your own colours for them.
 
@@ -274,7 +274,7 @@ p
     |  #[code jupyter] keyword argument – e.g. to return raw HTML in a notebook,
     |  or to force Jupyter rendering if auto-detection fails.
 
-+image("/assets/img/docs/displacy_jupyter.jpg", 700, false, "Example of using the displaCy dependency and named entity visualizer in a Jupyter notebook")
++image("/assets/img/displacy_jupyter.jpg", 700, false, "Example of using the displaCy dependency and named entity visualizer in a Jupyter notebook")
 
 p
     |  Internally, displaCy imports #[code display] and #[code HTML] from

From 808f7ee417a0aa9d6828eec12297472ba1eddab3 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 14:27:22 +0200
Subject: [PATCH 166/213] Update API documentation

---
 website/api/_annotation/_biluo.jade           |  43 ++
 .../api/_annotation/_dep-labels.jade          |   0
 .../api/_annotation/_named-entities.jade      |   0
 .../{docs => }/api/_annotation/_pos-tags.jade |   0
 website/api/_architecture/_cython.jade        | 115 ++++++
 website/api/_architecture/_nn-model.jade      | 141 +++++++
 website/{docs => }/api/_data.json             | 127 +++---
 .../api/cli.jade => api/_top-level/_cli.jade} | 122 +++---
 website/api/_top-level/_compat.jade           |  91 ++++
 .../_top-level/_displacy.jade}                |  24 +-
 .../spacy.jade => api/_top-level/_spacy.jade} |  64 ++-
 .../util.jade => api/_top-level/_util.jade}   |  62 ++-
 website/api/annotation.jade                   | 131 ++++++
 website/{docs => }/api/binder.jade            |   2 +-
 website/api/dependencyparser.jade             |   5 +
 website/{docs => }/api/doc.jade               |  48 ++-
 website/api/entityrecognizer.jade             |   5 +
 website/{docs => }/api/goldcorpus.jade        |   6 +-
 website/{docs => }/api/goldparse.jade         |  14 +-
 website/api/index.jade                        |  14 +
 website/{docs => }/api/language.jade          |  42 +-
 website/api/lemmatizer.jade                   |   5 +
 website/{docs => }/api/lexeme.jade            |  14 +-
 website/{docs => }/api/matcher.jade           |  23 +-
 website/api/phrasematcher.jade                | 181 ++++++++
 website/api/pipe.jade                         | 390 ++++++++++++++++++
 website/{docs => }/api/span.jade              |  32 +-
 website/{docs => }/api/stringstore.jade       |  22 +-
 website/api/tagger.jade                       |   5 +
 website/api/tensorizer.jade                   |   5 +
 website/api/textcategorizer.jade              |  19 +
 website/{docs => }/api/token.jade             |  45 +-
 website/{docs => }/api/tokenizer.jade         |  16 +-
 website/api/top-level.jade                    |  24 ++
 website/api/vectors.jade                      | 333 +++++++++++++++
 website/{docs => }/api/vocab.jade             | 131 +++++-
 website/docs/api/annotation.jade              | 156 -------
 website/docs/api/dependencyparser.jade        | 111 -----
 website/docs/api/entityrecognizer.jade        | 109 -----
 website/docs/api/index.jade                   | 241 -----------
 website/docs/api/language-models.jade         |  93 -----
 website/docs/api/tagger.jade                  |  93 -----
 website/docs/api/tensorizer.jade              |   7 -
 website/docs/api/textcategorizer.jade         |  21 -
 website/docs/api/vectors.jade                 |   7 -
 website/usage/_models/_languages.jade         |  72 ++++
 46 files changed, 2070 insertions(+), 1141 deletions(-)
 create mode 100644 website/api/_annotation/_biluo.jade
 rename website/{docs => }/api/_annotation/_dep-labels.jade (100%)
 rename website/{docs => }/api/_annotation/_named-entities.jade (100%)
 rename website/{docs => }/api/_annotation/_pos-tags.jade (100%)
 create mode 100644 website/api/_architecture/_cython.jade
 create mode 100644 website/api/_architecture/_nn-model.jade
 rename website/{docs => }/api/_data.json (55%)
 rename website/{docs/api/cli.jade => api/_top-level/_cli.jade} (74%)
 create mode 100644 website/api/_top-level/_compat.jade
 rename website/{docs/api/displacy.jade => api/_top-level/_displacy.jade} (91%)
 rename website/{docs/api/spacy.jade => api/_top-level/_spacy.jade} (72%)
 rename website/{docs/api/util.jade => api/_top-level/_util.jade} (87%)
 create mode 100644 website/api/annotation.jade
 rename website/{docs => }/api/binder.jade (79%)
 create mode 100644 website/api/dependencyparser.jade
 rename website/{docs => }/api/doc.jade (97%)
 create mode 100644 website/api/entityrecognizer.jade
 rename website/{docs => }/api/goldcorpus.jade (71%)
 rename website/{docs => }/api/goldparse.jade (95%)
 create mode 100644 website/api/index.jade
 rename website/{docs => }/api/language.jade (92%)
 create mode 100644 website/api/lemmatizer.jade
 rename website/{docs => }/api/lexeme.jade (98%)
 rename website/{docs => }/api/matcher.jade (96%)
 create mode 100644 website/api/phrasematcher.jade
 create mode 100644 website/api/pipe.jade
 rename website/{docs => }/api/span.jade (97%)
 rename website/{docs => }/api/stringstore.jade (96%)
 create mode 100644 website/api/tagger.jade
 create mode 100644 website/api/tensorizer.jade
 create mode 100644 website/api/textcategorizer.jade
 rename website/{docs => }/api/token.jade (96%)
 rename website/{docs => }/api/tokenizer.jade (96%)
 create mode 100644 website/api/top-level.jade
 create mode 100644 website/api/vectors.jade
 rename website/{docs => }/api/vocab.jade (66%)
 delete mode 100644 website/docs/api/annotation.jade
 delete mode 100644 website/docs/api/dependencyparser.jade
 delete mode 100644 website/docs/api/entityrecognizer.jade
 delete mode 100644 website/docs/api/index.jade
 delete mode 100644 website/docs/api/language-models.jade
 delete mode 100644 website/docs/api/tagger.jade
 delete mode 100644 website/docs/api/tensorizer.jade
 delete mode 100644 website/docs/api/textcategorizer.jade
 delete mode 100644 website/docs/api/vectors.jade
 create mode 100644 website/usage/_models/_languages.jade

diff --git a/website/api/_annotation/_biluo.jade b/website/api/_annotation/_biluo.jade
new file mode 100644
index 000000000..dc6168732
--- /dev/null
+++ b/website/api/_annotation/_biluo.jade
@@ -0,0 +1,43 @@
+//- 💫 DOCS > API > ANNOTATION > BILUO
+
++table([ "Tag", "Description" ])
+    +row
+        +cell #[code #[span.u-color-theme B] EGIN]
+        +cell The first token of a multi-token entity.
+
+    +row
+        +cell #[code #[span.u-color-theme I] N]
+        +cell An inner token of a multi-token entity.
+
+    +row
+        +cell #[code #[span.u-color-theme L] AST]
+        +cell The final token of a multi-token entity.
+
+    +row
+        +cell #[code #[span.u-color-theme U] NIT]
+        +cell A single-token entity.
+
+    +row
+        +cell #[code #[span.u-color-theme O] UT]
+        +cell A non-entity token.
+
++aside("Why BILUO, not IOB?")
+    |  There are several coding schemes for encoding entity annotations as
+    |  token tags.  These coding schemes are equally expressive, but not
+    |  necessarily equally learnable.
+    |  #[+a("http://www.aclweb.org/anthology/W09-1119") Ratinov and Roth]
+    |  showed that the minimal #[strong Begin], #[strong In], #[strong Out]
+    |  scheme was more difficult to learn than the #[strong BILUO] scheme that
+    |  we use, which explicitly marks boundary tokens.
+
+p
+    |  spaCy translates the character offsets into this scheme, in order to
+    |  decide the cost of each action given the current state of the entity
+    |  recogniser. The costs are then used to calculate the gradient of the
+    |  loss, to train the model. The exact algorithm is a pastiche of
+    |  well-known methods, and is not currently described in any single
+    |  publication. The model is a greedy transition-based parser guided by a
+    |  linear model whose weights are learned using the averaged perceptron
+    |  loss, via the #[+a("http://www.aclweb.org/anthology/C12-1059") dynamic oracle]
+    |  imitation learning strategy. The transition system is equivalent to the
+    |  BILOU tagging scheme.
diff --git a/website/docs/api/_annotation/_dep-labels.jade b/website/api/_annotation/_dep-labels.jade
similarity index 100%
rename from website/docs/api/_annotation/_dep-labels.jade
rename to website/api/_annotation/_dep-labels.jade
diff --git a/website/docs/api/_annotation/_named-entities.jade b/website/api/_annotation/_named-entities.jade
similarity index 100%
rename from website/docs/api/_annotation/_named-entities.jade
rename to website/api/_annotation/_named-entities.jade
diff --git a/website/docs/api/_annotation/_pos-tags.jade b/website/api/_annotation/_pos-tags.jade
similarity index 100%
rename from website/docs/api/_annotation/_pos-tags.jade
rename to website/api/_annotation/_pos-tags.jade
diff --git a/website/api/_architecture/_cython.jade b/website/api/_architecture/_cython.jade
new file mode 100644
index 000000000..84b98b824
--- /dev/null
+++ b/website/api/_architecture/_cython.jade
@@ -0,0 +1,115 @@
+//- 💫 DOCS > API > ARCHITECTURE > CYTHON
+
++aside("What's Cython?")
+    |  #[+a("http://cython.org/") Cython] is a language for writing
+    |  C extensions for Python. Most Python code is also valid Cython, but
+    |  you can add type declarations to get efficient memory-managed code
+    |  just like C or C++.
+
+p
+    |  spaCy's core data structures are implemented as
+    |  #[+a("http://cython.org/") Cython] #[code cdef] classes. Memory is
+    |  managed through the #[+a(gh("cymem")) #[code cymem]]
+    |  #[code cymem.Pool] class, which allows you
+    |  to allocate memory which will be freed when the #[code Pool] object
+    |  is garbage collected. This means you usually don't have to worry
+    |  about freeing memory. You just have to decide which Python object
+    |  owns the memory, and make it own the #[code Pool]. When that object
+    |  goes out of scope, the memory will be freed. You do have to take
+    |  care that no pointers outlive the object that owns them — but this
+    |  is generally quite easy.
+
+p
+    |  All Cython modules should have the #[code # cython: infer_types=True]
+    |  compiler directive at the top of the file. This makes the code much
+    |  cleaner, as it avoids the need for many type declarations. If
+    |  possible, you should prefer to declare your functions #[code nogil],
+    |  even if you don't especially care about multi-threading. The reason
+    |  is that #[code nogil] functions help the Cython compiler reason about
+    |  your code quite a lot — you're telling the compiler that no Python
+    |  dynamics are possible. This lets many errors be raised, and ensures
+    |  your function will run at C speed.
+
+
+p
+    |  Cython gives you many choices of sequences: you could have a Python
+    |  list, a numpy array, a memory view, a C++ vector, or a pointer.
+    |  Pointers are preferred, because they are fastest, have the most
+    |  explicit semantics, and let the compiler check your code more
+    |  strictly. C++ vectors are also great — but you should only use them
+    |  internally in functions. It's less friendly to accept a vector as an
+    |  argument, because that asks the user to do much more work. Here's
+    |  how to get a pointer from a numpy array, memory view or vector:
+
++code.
+    cdef void get_pointers(np.ndarray[int, mode='c'] numpy_array, vector[int] cpp_vector, int[::1] memory_view) nogil:
+    pointer1 = &lt;int*&gt;numpy_array.data
+    pointer2 = cpp_vector.data()
+    pointer3 = &memory_view[0]
+
+p
+    |  Both C arrays and C++ vectors reassure the compiler that no Python
+    |  operations are possible on your variable. This is a big advantage:
+    |  it lets the Cython compiler raise many more errors for you.
+
+p
+    |  When getting a pointer from a numpy array or memoryview, take care
+    |  that the data is actually stored in C-contiguous order — otherwise
+    |  you'll get a pointer to nonsense. The type-declarations in the code
+    |  above should generate runtime errors if buffers with incorrect
+    |  memory layouts are passed in. To iterate over the array, the
+    |  following style is preferred:
+
++code.
+    cdef int c_total(const int* int_array, int length) nogil:
+        total = 0
+        for item in int_array[:length]:
+            total += item
+        return total
+
+p
+    |  If this is confusing, consider that the compiler couldn't deal with
+    |  #[code for item in int_array:] — there's no length attached to a raw
+    |  pointer, so how could we figure out where to stop? The length is
+    |  provided in the slice notation as a solution to this. Note that we
+    |  don't have to declare the type of #[code item] in the code above —
+    |  the compiler can easily infer it. This gives us tidy code that looks
+    |  quite like Python, but is exactly as fast as C — because we've made
+    |  sure the compilation to C is trivial.
+
+p
+    |  Your functions cannot be declared #[code nogil] if they need to
+    |  create Python objects or call Python functions. This is perfectly
+    |  okay — you shouldn't torture your code just to get #[code nogil]
+    |  functions. However, if your function isn't #[code nogil], you should
+    |  compile your module with #[code cython -a --cplus my_module.pyx] and
+    |  open the resulting #[code my_module.html] file in a browser. This
+    |  will let you see how Cython is compiling your code. Calls into the
+    |  Python run-time will be in bright yellow. This lets you easily see
+    |  whether Cython is able to correctly type your code, or whether there
+    |  are unexpected problems.
+
+p
+    |  Working in Cython is very rewarding once you're over the initial
+    |  learning curve. As with C and C++, the first way you write something
+    |  in Cython will often be the performance-optimal approach. In
+    |  contrast, Python optimisation generally requires a lot of
+    |  experimentation. Is it faster to have an #[code if item in my_dict]
+    |  check, or to use #[code .get()]? What about
+    |  #[code try]/#[code except]? Does this numpy operation create a copy?
+    |  There's no way to guess the answers to these questions, and you'll
+    |  usually be dissatisfied with your results — so there's no way to
+    |  know when to stop this process. In the worst case, you'll make a
+    |  mess that invites the next reader to try their luck too. This is
+    |  like one of those
+    |  #[+a("http://www.wemjournal.org/article/S1080-6032%2809%2970088-2/abstract") volcanic gas-traps],
+    |  where the rescuers keep passing out from low oxygen, causing
+    |  another rescuer to follow — only to succumb themselves. In short,
+    |  just say no to optimizing your Python. If it's not fast enough the
+    |  first time, just switch to Cython.
+
++infobox("Resources")
+    +list.o-no-block
+        +item #[+a("http://docs.cython.org/en/latest/") Official Cython documentation] (cython.org)
+        +item #[+a("https://explosion.ai/blog/writing-c-in-cython", true) Writing C in Cython] (explosion.ai)
+        +item #[+a("https://explosion.ai/blog/multithreading-with-cython") Multi-threading spaCy’s parser and named entity recogniser] (explosion.ai)
diff --git a/website/api/_architecture/_nn-model.jade b/website/api/_architecture/_nn-model.jade
new file mode 100644
index 000000000..8080af2ec
--- /dev/null
+++ b/website/api/_architecture/_nn-model.jade
@@ -0,0 +1,141 @@
+//- 💫 DOCS > API > ARCHITECTURE > NN MODEL ARCHITECTURE
+
+p
+    |  The parsing model is a blend of recent results. The two recent
+    |  inspirations have been the work of Eli Klipperwasser and Yoav Goldberg at
+    |  Bar Ilan#[+fn(1)], and the SyntaxNet team from Google. The foundation of
+    |  the parser is still based on the work of Joakim Nivre#[+fn(2)], who
+    |  introduced the transition-based framework#[+fn(3)], the arc-eager
+    |  transition system, and the imitation learning objective. The model is
+    |  implemented using #[+a(gh("thinc")) Thinc], spaCy's machine learning
+    |  library. We first predict context-sensitive vectors for each word in the
+    |  input:
+
++code.
+    (embed_lower | embed_prefix | embed_suffix | embed_shape)
+        &gt;&gt; Maxout(token_width)
+        &gt;&gt; convolution ** 4
+
+p
+    |  This convolutional layer is shared between the tagger, parser and NER,
+    |  and will also be shared by the future neural lemmatizer. Because the
+    |  parser shares these layers with the tagger, the parser does not require
+    |  tag features. I got this trick from David Weiss's "Stack Combination"
+    |  paper#[+fn(4)].
+
+p
+    |  To boost the representation, the tagger actually predicts a "super tag"
+    |  with POS, morphology and dependency label#[+fn(5)]. The tagger predicts
+    |  these supertags by adding a softmax layer onto the convolutional layer –
+    |  so, we're teaching the convolutional layer to give us a representation
+    |  that's one affine transform from this informative lexical information.
+    |  This is obviously good for the parser (which backprops to the
+    |  convolutions too). The parser model makes a state vector by concatenating
+    |  the vector representations for its context tokens.  The current context
+    |  tokens:
+
++table
+    +row
+        +cell #[code S0], #[code S1], #[code S2]
+        +cell Top three words on the stack.
+
+    +row
+        +cell #[code B0], #[code B1]
+        +cell First two words of the buffer.
+
+    +row
+        +cell.u-nowrap
+            |  #[code S0L1], #[code S1L1], #[code S2L1], #[code B0L1],
+            |  #[code B1L1]#[br]
+            |  #[code S0L2], #[code S1L2], #[code S2L2], #[code B0L2],
+            |  #[code B1L2]
+        +cell
+            |  Leftmost and second leftmost children of #[code S0], #[code S1],
+            |  #[code S2], #[code B0] and #[code B1].
+
+    +row
+        +cell.u-nowrap
+            |  #[code S0R1], #[code S1R1], #[code S2R1], #[code B0R1],
+            |  #[code B1R1]#[br]
+            |  #[code S0R2], #[code S1R2], #[code S2R2], #[code B0R2],
+            |  #[code B1R2]
+        +cell
+            |  Rightmost and second rightmost children of #[code S0], #[code S1],
+            |  #[code S2], #[code B0] and #[code B1].
+
+p
+    |  This makes the state vector quite long: #[code 13*T], where #[code T] is
+    |  the token vector width (128 is working well). Fortunately, there's a way
+    |  to structure the computation to save some expense (and make it more
+    |  GPU-friendly).
+
+p
+    |  The parser typically visits #[code 2*N] states for a sentence of length
+    |  #[code N] (although it may visit more, if it back-tracks with a
+    |  non-monotonic transition#[+fn(4)]). A naive implementation would require
+    |  #[code 2*N (B, 13*T) @ (13*T, H)] matrix multiplications for a batch of
+    |  size #[code B]. We can instead perform one #[code (B*N, T) @ (T, 13*H)]
+    |  multiplication, to pre-compute the hidden weights for each positional
+    |  feature with respect to the words in the batch. (Note that our token
+    |  vectors come from the CNN — so we can't play this trick over the
+    |  vocabulary. That's how Stanford's NN parser#[+fn(3)] works — and why its
+    |  model is so big.)
+
+p
+    |  This pre-computation strategy allows a nice compromise between
+    |  GPU-friendliness and implementation simplicity. The CNN and the wide
+    |  lower layer are computed on the GPU, and then the precomputed hidden
+    |  weights are moved to the CPU, before we start the transition-based
+    |  parsing process. This makes a lot of things much easier. We don't have to
+    |  worry about variable-length batch sizes, and we don't have to implement
+    |  the dynamic oracle in CUDA to train.
+
+p
+    |  Currently the parser's loss function is multilabel log loss#[+fn(6)], as
+    |  the dynamic oracle allows multiple states to be 0 cost. This is defined
+    |  as follows, where #[code gZ] is the sum of the scores assigned to gold
+    |  classes:
+
++code.
+    (exp(score) / Z) - (exp(score) / gZ)
+
++bibliography
+    +item
+        |  #[+a("https://www.semanticscholar.org/paper/Simple-and-Accurate-Dependency-Parsing-Using-Bidir-Kiperwasser-Goldberg/3cf31ecb2724b5088783d7c96a5fc0d5604cbf41") Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations]
+        br
+        |  Eliyahu Kiperwasser, Yoav Goldberg. (2016)
+
+    +item
+        |  #[+a("https://www.semanticscholar.org/paper/A-Dynamic-Oracle-for-Arc-Eager-Dependency-Parsing-Goldberg-Nivre/22697256ec19ecc3e14fcfc63624a44cf9c22df4") A Dynamic Oracle for Arc-Eager Dependency Parsing]
+        br
+        |  Yoav Goldberg, Joakim Nivre (2012)
+
+    +item
+        |  #[+a("https://explosion.ai/blog/parsing-english-in-python") Parsing English in 500 Lines of Python]
+        br
+        |  Matthew Honnibal (2013)
+
+    +item
+        |  #[+a("https://www.semanticscholar.org/paper/Stack-propagation-Improved-Representation-Learning-Zhang-Weiss/0c133f79b23e8c680891d2e49a66f0e3d37f1466") Stack-propagation: Improved Representation Learning for Syntax]
+        br
+        |  Yuan Zhang, David Weiss (2016)
+
+    +item
+        |  #[+a("https://www.semanticscholar.org/paper/Deep-multi-task-learning-with-low-level-tasks-supe-S%C3%B8gaard-Goldberg/03ad06583c9721855ccd82c3d969a01360218d86") Deep multi-task learning with low level tasks supervised at lower layers]
+        br
+        |  Anders Søgaard, Yoav Goldberg (2016)
+
+    +item
+        |  #[+a("https://www.semanticscholar.org/paper/An-Improved-Non-monotonic-Transition-System-for-De-Honnibal-Johnson/4094cee47ade13b77b5ab4d2e6cb9dd2b8a2917c") An Improved Non-monotonic Transition System for Dependency Parsing]
+        br
+        |  Matthew Honnibal, Mark Johnson (2015)
+
+    +item
+        |  #[+a("http://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf") A Fast and Accurate Dependency Parser using Neural Networks]
+        br
+        |  Danqi Cheng, Christopher D. Manning (2014)
+
+    +item
+        |  #[+a("https://www.semanticscholar.org/paper/Parsing-the-Wall-Street-Journal-using-a-Lexical-Fu-Riezler-King/0ad07862a91cd59b7eb5de38267e47725a62b8b2") Parsing the Wall Street Journal using a Lexical-Functional Grammar and Discriminative Estimation Techniques]
+        br
+        |  Stefan Riezler et al. (2002)
diff --git a/website/docs/api/_data.json b/website/api/_data.json
similarity index 55%
rename from website/docs/api/_data.json
rename to website/api/_data.json
index e413f200c..83b98f1fa 100644
--- a/website/docs/api/_data.json
+++ b/website/api/_data.json
@@ -1,29 +1,32 @@
 {
     "sidebar": {
-        "Introduction": {
-            "Facts & Figures": "./",
-            "Languages": "language-models",
-            "Annotation Specs": "annotation"
+        "Overview": {
+            "Architecture": "./",
+            "Annotation Specs": "annotation",
+            "Functions": "top-level"
         },
-        "Top-level": {
-            "spacy": "spacy",
-            "displacy": "displacy",
-            "Utility Functions": "util",
-            "Command line": "cli"
-        },
-        "Classes": {
+        "Containers": {
             "Doc": "doc",
             "Token": "token",
             "Span": "span",
+            "Lexeme": "lexeme"
+        },
+
+        "Pipeline": {
             "Language": "language",
-            "Tokenizer": "tokenizer",
+            "Pipe": "pipe",
             "Tensorizer": "tensorizer",
             "Tagger": "tagger",
             "DependencyParser": "dependencyparser",
             "EntityRecognizer": "entityrecognizer",
             "TextCategorizer": "textcategorizer",
+            "Tokenizer": "tokenizer",
+            "Lemmatizer": "lemmatizer",
             "Matcher": "matcher",
-            "Lexeme": "lexeme",
+            "PhraseMatcher": "phrasematcher"
+        },
+
+        "Other": {
             "Vocab": "vocab",
             "StringStore": "stringstore",
             "Vectors": "vectors",
@@ -34,52 +37,37 @@
     },
 
     "index": {
-        "title": "Facts & Figures",
-        "next": "language-models"
+        "title": "Architecture",
+        "next": "annotation",
+        "menu": {
+            "Basics": "basics",
+            "Neural Network Model": "nn-model",
+            "Cython Conventions": "cython"
+        }
     },
 
-    "language-models": {
-        "title": "Languages",
-        "next": "philosophy"
-    },
-
-    "philosophy": {
-        "title": "Philosophy"
-    },
-
-    "spacy": {
-        "title": "spaCy top-level functions",
-        "source": "spacy/__init__.py",
-        "next": "displacy"
-    },
-
-    "displacy": {
-        "title": "displaCy",
-        "tag": "module",
-        "source": "spacy/displacy",
-        "next": "util"
-    },
-
-    "util": {
-        "title": "Utility Functions",
-        "source": "spacy/util.py",
-        "next": "cli"
-    },
-
-    "cli": {
-        "title": "Command Line Interface",
-        "source": "spacy/cli"
+    "top-level": {
+        "title": "Top-level Functions",
+        "menu": {
+            "spacy": "spacy",
+            "displacy": "displacy",
+            "Utility Functions": "util",
+            "Compatibility": "compat",
+            "Command Line": "cli"
+        }
     },
 
     "language": {
         "title": "Language",
         "tag": "class",
+        "teaser": "A text-processing pipeline.",
         "source": "spacy/language.py"
     },
 
     "doc": {
         "title": "Doc",
         "tag": "class",
+        "teaser": "A container for accessing linguistic annotations.",
         "source": "spacy/tokens/doc.pyx"
     },
 
@@ -103,6 +91,7 @@
 
     "vocab": {
         "title": "Vocab",
+        "teaser": "A storage class for vocabulary and other data shared across a language.",
         "tag": "class",
         "source": "spacy/vocab.pyx"
     },
@@ -115,10 +104,27 @@
 
     "matcher": {
         "title": "Matcher",
+        "teaser": "Match sequences of tokens, based on pattern rules.",
         "tag": "class",
         "source": "spacy/matcher.pyx"
     },
 
+    "phrasematcher": {
+        "title": "PhraseMatcher",
+        "teaser": "Match sequences of tokens, based on documents.",
+        "tag": "class",
+        "tag_new": 2,
+        "source": "spacy/matcher.pyx"
+    },
+
+    "pipe": {
+        "title": "Pipe",
+        "teaser": "Abstract base class defining the API for pipeline components.",
+        "tag": "class",
+        "tag_new": 2,
+        "source": "spacy/pipeline.pyx"
+    },
+
     "dependenyparser": {
         "title": "DependencyParser",
         "tag": "class",
@@ -127,18 +133,22 @@
 
     "entityrecognizer": {
         "title": "EntityRecognizer",
+        "teaser": "Annotate named entities on documents.",
         "tag": "class",
         "source": "spacy/pipeline.pyx"
     },
 
     "textcategorizer": {
         "title": "TextCategorizer",
+        "teaser": "Add text categorization models to spaCy pipelines.",
         "tag": "class",
+        "tag_new": 2,
         "source": "spacy/pipeline.pyx"
     },
 
     "dependencyparser": {
         "title": "DependencyParser",
+        "teaser": "Annotate syntactic dependencies on documents.",
         "tag": "class",
         "source": "spacy/pipeline.pyx"
     },
@@ -149,15 +159,23 @@
         "source": "spacy/tokenizer.pyx"
     },
 
+    "lemmatizer": {
+        "title": "Lemmatizer",
+        "tag": "class"
+    },
+
     "tagger": {
         "title": "Tagger",
+        "teaser": "Annotate part-of-speech tags on documents.",
         "tag": "class",
         "source": "spacy/pipeline.pyx"
     },
 
     "tensorizer": {
         "title": "Tensorizer",
+        "teaser": "Add a tensor with position-sensitive meaning representations to a document.",
         "tag": "class",
+        "tag_new": 2,
         "source": "spacy/pipeline.pyx"
     },
 
@@ -169,23 +187,38 @@
 
     "goldcorpus": {
         "title": "GoldCorpus",
+        "teaser": "An annotated corpus, using the JSON file format.",
         "tag": "class",
+        "tag_new": 2,
         "source": "spacy/gold.pyx"
     },
 
     "binder": {
         "title": "Binder",
         "tag": "class",
+        "tag_new": 2,
         "source": "spacy/tokens/binder.pyx"
     },
 
     "vectors": {
         "title": "Vectors",
+        "teaser": "Store, save and load word vectors.",
         "tag": "class",
+        "tag_new": 2,
         "source": "spacy/vectors.pyx"
     },
 
     "annotation": {
-        "title": "Annotation Specifications"
+        "title": "Annotation Specifications",
+        "teaser": "Schemes used for labels, tags and training data.",
+        "menu": {
+            "Tokenization": "tokenization",
+            "Sentence Boundaries": "sbd",
+            "POS Tagging": "pos-tagging",
+            "Lemmatization": "lemmatization",
+            "Dependencies": "dependency-parsing",
+            "Named Entities": "named-entities",
+            "Training Data": "training"
+        }
     }
 }
diff --git a/website/docs/api/cli.jade b/website/api/_top-level/_cli.jade
similarity index 74%
rename from website/docs/api/cli.jade
rename to website/api/_top-level/_cli.jade
index 26aa1f883..52884988e 100644
--- a/website/docs/api/cli.jade
+++ b/website/api/_top-level/_cli.jade
@@ -1,26 +1,17 @@
-//- 💫 DOCS > USAGE > COMMAND LINE INTERFACE
-
-include ../../_includes/_mixins
+//- 💫 DOCS > API > TOP-LEVEL > COMMAND LINE INTERFACE
 
 p
     |  As of v1.7.0, spaCy comes with new command line helpers to download and
     |  link models and show useful debugging information. For a list of available
     |  commands, type #[code spacy --help].
 
-+infobox("⚠️ Deprecation note")
-    |  As of spaCy 2.0, the #[code model] command to initialise a model data
-    |  directory is deprecated. The command was only necessary because previous
-    |  versions of spaCy expected a model directory to already be set up. This
-    |  has since been changed, so you can use the #[+api("cli#train") #[code train]]
-    |  command straight away.
-
-+h(2, "download") Download
++h(3, "download") Download
 
 p
-    |  Download #[+a("/docs/usage/models") models] for spaCy. The downloader finds the
+    |  Download #[+a("/usage/models") models] for spaCy. The downloader finds the
     |  best-matching compatible version, uses pip to download the model as a
     |  package and automatically creates a
-    |  #[+a("/docs/usage/models#usage") shortcut link] to load the model by name.
+    |  #[+a("/usage/models#usage") shortcut link] to load the model by name.
     |  Direct downloads don't perform any compatibility checks and require the
     |  model name to be specified with its version (e.g., #[code en_core_web_sm-1.2.0]).
 
@@ -49,15 +40,15 @@ p
     |  detailed messages in case things go wrong. It's #[strong not recommended]
     |  to use this command as part of an automated process. If you know which
     |  model your project needs, you should consider a
-    |  #[+a("/docs/usage/models#download-pip") direct download via pip], or
+    |  #[+a("/usage/models#download-pip") direct download via pip], or
     |  uploading the model to a local PyPi installation and fetching it straight
     |  from there. This will also allow you to add it as a versioned package
     |  dependency to your project.
 
-+h(2, "link") Link
++h(3, "link") Link
 
 p
-    |  Create a #[+a("/docs/usage/models#usage") shortcut link] for a model,
+    |  Create a #[+a("/usage/models#usage") shortcut link] for a model,
     |  either a Python package or a local directory. This will let you load
     |  models from any location using a custom name via
     |  #[+api("spacy#load") #[code spacy.load()]].
@@ -95,7 +86,7 @@ p
         +cell flag
         +cell Show help message and available arguments.
 
-+h(2, "info") Info
++h(3, "info") Info
 
 p
     |  Print information about your spaCy installation, models and local setup,
@@ -122,15 +113,15 @@ p
         +cell flag
         +cell Show help message and available arguments.
 
-+h(2, "convert") Convert
++h(3, "convert") Convert
 
 p
-    |  Convert files into spaCy's #[+a("/docs/api/annotation#json-input") JSON format]
+    |  Convert files into spaCy's #[+a("/api/annotation#json-input") JSON format]
     |  for use with the #[code train] command and other experiment management
     |  functions. The right converter is chosen based on the file extension of
     |  the input file. Currently only supports #[code .conllu].
 
-+code(false, "bash", "$").
++code(false, "bash", "$", false, false, true).
     spacy convert [input_file] [output_dir] [--n-sents] [--morphology]
 
 +table(["Argument", "Type", "Description"])
@@ -159,14 +150,18 @@ p
         +cell flag
         +cell Show help message and available arguments.
 
-+h(2, "train") Train
++h(3, "train") Train
 
 p
     |  Train a model. Expects data in spaCy's
-    |  #[+a("/docs/api/annotation#json-input") JSON format].
+    |  #[+a("/api/annotation#json-input") JSON format]. On each epoch, a model
+    |  will be saved out to the directory. Accuracy scores and model details
+    |  will be added to a #[+a("/usage/training#models-generating") #[code meta.json]]
+    |  to allow packaging the model using the
+    |  #[+api("cli#package") #[code package]] command.
 
-+code(false, "bash", "$").
-    spacy train [lang] [output_dir] [train_data] [dev_data] [--n-iter] [--n-sents] [--use-gpu] [--no-tagger] [--no-parser] [--no-entities]
++code(false, "bash", "$", false, false, true).
+    spacy train [lang] [output_dir] [train_data] [dev_data] [--n-iter] [--n-sents] [--use-gpu] [--meta-path] [--vectors] [--no-tagger] [--no-parser] [--no-entities] [--gold-preproc]
 
 +table(["Argument", "Type", "Description"])
     +row
@@ -204,6 +199,27 @@ p
         +cell option
         +cell Use GPU.
 
+    +row
+        +cell #[code --vectors], #[code -v]
+        +cell option
+        +cell Model to load vectors from.
+
+    +row
+        +cell #[code --meta-path], #[code -m]
+        +cell option
+        +cell
+            |  #[+tag-new(2)] Optional path to model
+            |  #[+a("/usage/training#models-generating") #[code meta.json]].
+            |  All relevant properties like #[code lang], #[code pipeline] and
+            |  #[code spacy_version] will be overwritten.
+
+    +row
+        +cell #[code --version], #[code -V]
+        +cell option
+        +cell
+            |  Model version. Will be written out to the model's
+            |  #[code meta.json] after training.
+
     +row
         +cell #[code --no-tagger], #[code -T]
         +cell flag
@@ -219,12 +235,18 @@ p
         +cell flag
         +cell Don't train NER.
 
+    +row
+        +cell #[code --gold-preproc], #[code -G]
+        +cell flag
+        +cell Use gold preprocessing.
+
     +row
         +cell #[code --help], #[code -h]
         +cell flag
         +cell Show help message and available arguments.
 
-+h(3, "train-hyperparams") Environment variables for hyperparameters
++h(4, "train-hyperparams") Environment variables for hyperparameters
+    +tag-new(2)
 
 p
     |  spaCy lets you set hyperparameters for training via environment variables.
@@ -236,98 +258,96 @@ p
 +code(false, "bash").
     parser_hidden_depth=2 parser_maxout_pieces=1 train-parser
 
-+under-construction
-
 +table(["Name", "Description", "Default"])
     +row
         +cell #[code dropout_from]
-        +cell
+        +cell Initial dropout rate.
         +cell #[code 0.2]
 
     +row
         +cell #[code dropout_to]
-        +cell
+        +cell Final dropout rate.
         +cell #[code 0.2]
 
     +row
         +cell #[code dropout_decay]
-        +cell
+        +cell Rate of dropout change.
         +cell #[code 0.0]
 
     +row
         +cell #[code batch_from]
-        +cell
+        +cell Initial batch size.
         +cell #[code 1]
 
     +row
         +cell #[code batch_to]
-        +cell
+        +cell Final batch size.
         +cell #[code 64]
 
     +row
         +cell #[code batch_compound]
-        +cell
+        +cell Rate of batch size acceleration.
         +cell #[code 1.001]
 
     +row
         +cell #[code token_vector_width]
-        +cell
+        +cell Width of embedding tables and convolutional layers.
         +cell #[code 128]
 
     +row
         +cell #[code embed_size]
-        +cell
+        +cell Number of rows in embedding tables.
         +cell #[code 7500]
 
     +row
         +cell #[code parser_maxout_pieces]
-        +cell
+        +cell Number of pieces in the parser's and NER's first maxout layer.
         +cell #[code 2]
 
     +row
         +cell #[code parser_hidden_depth]
-        +cell
+        +cell Number of hidden layers in the parser and NER.
         +cell #[code 1]
 
     +row
         +cell #[code hidden_width]
-        +cell
+        +cell Size of the parser's and NER's hidden layers.
         +cell #[code 128]
 
     +row
         +cell #[code learn_rate]
-        +cell
+        +cell Learning rate.
         +cell #[code 0.001]
 
     +row
         +cell #[code optimizer_B1]
-        +cell
+        +cell Momentum for the Adam solver.
         +cell #[code 0.9]
 
     +row
         +cell #[code optimizer_B2]
-        +cell
+        +cell Adagrad-momentum for the Adam solver.
         +cell #[code 0.999]
 
     +row
         +cell #[code optimizer_eps]
-        +cell
+        +cell Epsylon value for the Adam solver.
         +cell #[code 1e-08]
 
     +row
         +cell #[code L2_penalty]
-        +cell
+        +cell L2 regularisation penalty.
         +cell #[code 1e-06]
 
     +row
         +cell #[code grad_norm_clip]
-        +cell
+        +cell Gradient L2 norm constraint.
         +cell #[code 1.0]
 
-+h(2, "package") Package
++h(3, "package") Package
 
 p
-    |  Generate a #[+a("/docs/usage/saving-loading#generating") model Python package]
+    |  Generate a #[+a("/usage/training#models-generating") model Python package]
     |  from an existing model data directory. All data files are copied over.
     |  If the path to a meta.json is supplied, or a meta.json is found in the
     |  input directory, this file is used. Otherwise, the data can be entered
@@ -336,8 +356,8 @@ p
     |  sure you're always using the latest versions. This means you need to be
     |  connected to the internet to use this command.
 
-+code(false, "bash", "$").
-    spacy package [input_dir] [output_dir] [--meta] [--force]
++code(false, "bash", "$", false, false, true).
+    spacy package [input_dir] [output_dir] [--meta-path] [--create-meta] [--force]
 
 +table(["Argument", "Type", "Description"])
     +row
@@ -353,14 +373,14 @@ p
     +row
         +cell #[code --meta-path], #[code -m]
         +cell option
-        +cell Path to meta.json file (optional).
+        +cell #[+tag-new(2)] Path to meta.json file (optional).
 
     +row
         +cell #[code --create-meta], #[code -c]
         +cell flag
         +cell
-            |  Create a meta.json file on the command line, even if one already
-            |  exists in the directory.
+            |  #[+tag-new(2)] Create a meta.json file on the command line, even
+            |  if one already exists in the directory.
 
     +row
         +cell #[code --force], #[code -f]
diff --git a/website/api/_top-level/_compat.jade b/website/api/_top-level/_compat.jade
new file mode 100644
index 000000000..dfd42c55f
--- /dev/null
+++ b/website/api/_top-level/_compat.jade
@@ -0,0 +1,91 @@
+//- 💫 DOCS > API > TOP-LEVEL > COMPATIBILITY
+
+p
+    |  All Python code is written in an
+    |  #[strong intersection of Python 2 and Python 3]. This is easy in Cython,
+    |  but somewhat ugly in Python. Logic that deals with Python or platform
+    |  compatibility only lives in #[code spacy.compat]. To distinguish them from
+    |  the builtin functions, replacement functions are suffixed with an
+    |  undersocre, e.e #[code unicode_]. For specific checks, spaCy uses the
+    |  #[code six] and #[code ftfy] packages.
+
++aside-code("Example").
+    from spacy.compat import unicode_, json_dumps
+
+    compatible_unicode = unicode_('hello world')
+    compatible_json = json_dumps({'key': 'value'})
+
++table(["Name", "Python 2", "Python 3"])
+    +row
+        +cell #[code compat.bytes_]
+        +cell #[code str]
+        +cell #[code bytes]
+
+    +row
+        +cell #[code compat.unicode_]
+        +cell #[code unicode]
+        +cell #[code str]
+
+    +row
+        +cell #[code compat.basestring_]
+        +cell #[code basestring]
+        +cell #[code str]
+
+    +row
+        +cell #[code compat.input_]
+        +cell #[code raw_input]
+        +cell #[code input]
+
+    +row
+        +cell #[code compat.json_dumps]
+        +cell #[code ujson.dumps] with #[code .decode('utf8')]
+        +cell #[code ujson.dumps]
+
+    +row
+        +cell #[code compat.path2str]
+        +cell #[code str(path)] with #[code .decode('utf8')]
+        +cell #[code str(path)]
+
++h(3, "is_config") compat.is_config
+    +tag function
+
+p
+    |  Check if a specific configuration of Python version and operating system
+    |  matches the user's setup. Mostly used to display targeted error messages.
+
++aside-code("Example").
+    from spacy.compat import is_config
+
+    if is_config(python2=True, windows=True):
+        print("You are using Python 2 on Windows.")
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code python2]
+        +cell bool
+        +cell spaCy is executed with Python 2.x.
+
+    +row
+        +cell #[code python3]
+        +cell bool
+        +cell spaCy is executed with Python 3.x.
+
+    +row
+        +cell #[code windows]
+        +cell bool
+        +cell spaCy is executed on Windows.
+
+    +row
+        +cell #[code linux]
+        +cell bool
+        +cell spaCy is executed on Linux.
+
+    +row
+        +cell #[code osx]
+        +cell bool
+        +cell spaCy is executed on OS X or macOS.
+
+    +row("foot")
+        +cell returns
+        +cell bool
+        +cell Whether the specified configuration matches the user's platform.
diff --git a/website/docs/api/displacy.jade b/website/api/_top-level/_displacy.jade
similarity index 91%
rename from website/docs/api/displacy.jade
rename to website/api/_top-level/_displacy.jade
index 59fcca3ca..a3d7240d6 100644
--- a/website/docs/api/displacy.jade
+++ b/website/api/_top-level/_displacy.jade
@@ -1,14 +1,12 @@
-//- 💫 DOCS > API > DISPLACY
-
-include ../../_includes/_mixins
+//- 💫 DOCS > API > TOP-LEVEL > DISPLACY
 
 p
     |  As of v2.0, spaCy comes with a built-in visualization suite. For more
     |  info and examples, see the usage guide on
-    |  #[+a("/docs/usage/visualizers") visualizing spaCy].
+    |  #[+a("/usage/visualizers") visualizing spaCy].
 
 
-+h(2, "serve") displacy.serve
++h(3, "displacy.serve") displacy.serve
     +tag method
     +tag-new(2)
 
@@ -60,7 +58,7 @@ p
         +cell bool
         +cell
             |  Don't parse #[code Doc] and instead, expect a dict or list of
-            |  dicts. #[+a("/docs/usage/visualizers#manual-usage") See here]
+            |  dicts. #[+a("/usage/visualizers#manual-usage") See here]
             |  for formats and examples.
         +cell #[code False]
 
@@ -70,7 +68,7 @@ p
         +cell Port to serve visualization.
         +cell #[code 5000]
 
-+h(2, "render") displacy.render
++h(3, "displacy.render") displacy.render
     +tag method
     +tag-new(2)
 
@@ -127,24 +125,24 @@ p Render a dependency parse tree or named entity visualization.
         +cell bool
         +cell
             |  Don't parse #[code Doc] and instead, expect a dict or list of
-            |  dicts. #[+a("/docs/usage/visualizers#manual-usage") See here]
+            |  dicts. #[+a("/usage/visualizers#manual-usage") See here]
             |  for formats and examples.
         +cell #[code False]
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell unicode
         +cell Rendered HTML markup.
         +cell
 
-+h(2, "options") Visualizer options
++h(3, "displacy_options") Visualizer options
 
 p
     |  The #[code options] argument lets you specify additional settings for
     |  each visualizer. If a setting is not present in the options, the default
     |  value will be used.
 
-+h(3, "options-dep") Dependency Visualizer options
++h(4, "options-dep") Dependency Visualizer options
 
 +aside-code("Example").
     options = {'compact': True, 'color': 'blue'}
@@ -219,7 +217,7 @@ p
         +cell Distance between words in px.
         +cell #[code 175] / #[code 85] (compact)
 
-+h(3, "options-ent") Named Entity Visualizer options
++h(4, "displacy_options-ent") Named Entity Visualizer options
 
 +aside-code("Example").
     options = {'ents': ['PERSON', 'ORG', 'PRODUCT'],
@@ -244,6 +242,6 @@ p
 
 p
     |  By default, displaCy comes with colours for all
-    |  #[+a("/docs/api/annotation#named-entities") entity types supported by spaCy].
+    |  #[+a("/api/annotation#named-entities") entity types supported by spaCy].
     |  If you're using custom entity types, you can use the #[code colors]
     |  setting to add your own colours for them.
diff --git a/website/docs/api/spacy.jade b/website/api/_top-level/_spacy.jade
similarity index 72%
rename from website/docs/api/spacy.jade
rename to website/api/_top-level/_spacy.jade
index a45307378..c14f62f7e 100644
--- a/website/docs/api/spacy.jade
+++ b/website/api/_top-level/_spacy.jade
@@ -1,15 +1,13 @@
-//- 💫 DOCS > API > SPACY
+//- 💫 DOCS > API > TOP-LEVEL > SPACY
 
-include ../../_includes/_mixins
-
-+h(2, "load") spacy.load
++h(3, "spacy.load") spacy.load
     +tag function
     +tag-model
 
 p
-    |  Load a model via its #[+a("/docs/usage/models#usage") shortcut link],
+    |  Load a model via its #[+a("/usage/models#usage") shortcut link],
     |  the name of an installed
-    |  #[+a("/docs/usage/saving-loading#generating") model package], a unicode
+    |  #[+a("/usage/training#models-generating") model package], a unicode
     |  path or a #[code Path]-like object. spaCy will try resolving the load
     |  argument in this order. If a model is loaded from a shortcut link or
     |  package name, spaCy will assume it's a Python package and import it and
@@ -38,25 +36,57 @@ p
         +cell list
         +cell
             |  Names of pipeline components to
-            |  #[+a("/docs/usage/language-processing-pipeline#disabling") disable].
+            |  #[+a("/usage/processing-pipelines#disabling") disable].
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Language]
         +cell A #[code Language] object with the loaded model.
 
-+infobox("⚠️ Deprecation note")
++infobox("Deprecation note", "⚠️")
     .o-block
         |  As of spaCy 2.0, the #[code path] keyword argument is deprecated. spaCy
         |  will also raise an error if no model could be loaded and never just
         |  return an empty #[code Language] object. If you need a blank language,
-        |  you need to import it explicitly (#[code from spacy.lang.en import English])
-        |  or use #[+api("util#get_lang_class") #[code util.get_lang_class]].
+        |  you can use the new function #[+api("spacy#blank") #[code spacy.blank()]]
+        |  or import the class explicitly, e.g.
+        |  #[code from spacy.lang.en import English].
 
     +code-new nlp = spacy.load('/model')
     +code-old nlp = spacy.load('en', path='/model')
 
-+h(2, "info") spacy.info
++h(3, "spacy.blank") spacy.blank
+    +tag function
+    +tag-new(2)
+
+p
+    |  Create a blank model of a given language class. This function is the
+    |  twin of #[code spacy.load()].
+
++aside-code("Example").
+    nlp_en = spacy.blank('en')
+    nlp_de = spacy.blank('de')
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code name]
+        +cell unicode
+        +cell ISO code of the language class to load.
+
+    +row
+        +cell #[code disable]
+        +cell list
+        +cell
+            |  Names of pipeline components to
+            |  #[+a("/usage/processing-pipelines#disabling") disable].
+
+    +row("foot")
+        +cell returns
+        +cell #[code Language]
+        +cell An empty #[code Language] object of the appropriate subclass.
+
+
++h(4, "spacy.info") spacy.info
     +tag function
 
 p
@@ -83,13 +113,13 @@ p
         +cell Print information as Markdown.
 
 
-+h(2, "explain") spacy.explain
++h(3, "spacy.explain") spacy.explain
     +tag function
 
 p
     |  Get a description for a given POS tag, dependency label or entity type.
     |  For a list of available terms, see
-    |  #[+src(gh("spacy", "spacy/glossary.py")) glossary.py].
+    |  #[+src(gh("spacy", "spacy/glossary.py")) #[code glossary.py]].
 
 +aside-code("Example").
     spacy.explain('NORP')
@@ -107,18 +137,18 @@ p
         +cell unicode
         +cell Term to explain.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell unicode
         +cell The explanation, or #[code None] if not found in the glossary.
 
-+h(2, "set_factory") spacy.set_factory
++h(3, "spacy.set_factory") spacy.set_factory
     +tag function
     +tag-new(2)
 
 p
     |  Set a factory that returns a custom
-    |  #[+a("/docs/usage/language-processing-pipeline") processing pipeline]
+    |  #[+a("/usage/processing-pipelines") processing pipeline]
     |  component. Factories are useful for creating stateful components, especially ones which depend on shared data.
 
 +aside-code("Example").
diff --git a/website/docs/api/util.jade b/website/api/_top-level/_util.jade
similarity index 87%
rename from website/docs/api/util.jade
rename to website/api/_top-level/_util.jade
index 2127446df..1770a111e 100644
--- a/website/docs/api/util.jade
+++ b/website/api/_top-level/_util.jade
@@ -1,10 +1,8 @@
-//- 💫 DOCS > API > UTIL
-
-include ../../_includes/_mixins
+//- 💫 DOCS > API > TOP-LEVEL > UTIL
 
 p
     |  spaCy comes with a small collection of utility functions located in
-    |  #[+src(gh("spaCy", "spacy/util.py")) spacy/util.py].
+    |  #[+src(gh("spaCy", "spacy/util.py")) #[code spacy/util.py]].
     |  Because utility functions are mostly intended for
     |  #[strong internal use within spaCy], their behaviour may change with
     |  future releases. The functions documented on this page should be safe
@@ -12,7 +10,7 @@ p
     |  recommend having additional tests in place if your application depends on
     |  any of spaCy's utilities.
 
-+h(2, "get_data_path") util.get_data_path
++h(3, "util.get_data_path") util.get_data_path
     +tag function
 
 p
@@ -25,12 +23,12 @@ p
         +cell bool
         +cell Only return path if it exists, otherwise return #[code None].
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Path] / #[code None]
         +cell Data path or #[code None].
 
-+h(2, "set_data_path") util.set_data_path
++h(3, "util.set_data_path") util.set_data_path
     +tag function
 
 p
@@ -47,12 +45,12 @@ p
         +cell unicode or #[code Path]
         +cell Path to new data directory.
 
-+h(2, "get_lang_class") util.get_lang_class
++h(3, "util.get_lang_class") util.get_lang_class
     +tag function
 
 p
     |  Import and load a #[code Language] class. Allows lazy-loading
-    |  #[+a("/docs/usage/adding-languages") language data] and importing
+    |  #[+a("/usage/adding-languages") language data] and importing
     |  languages using the two-letter language code.
 
 +aside-code("Example").
@@ -67,12 +65,12 @@ p
         +cell unicode
         +cell Two-letter language code, e.g. #[code 'en'].
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Language]
         +cell Language class.
 
-+h(2, "load_model") util.load_model
++h(3, "util.load_model") util.load_model
     +tag function
     +tag-new(2)
 
@@ -101,12 +99,12 @@ p
         +cell -
         +cell Specific overrides, like pipeline components to disable.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Language]
         +cell #[code Language] class with the loaded model.
 
-+h(2, "load_model_from_path") util.load_model_from_path
++h(3, "util.load_model_from_path") util.load_model_from_path
     +tag function
     +tag-new(2)
 
@@ -139,18 +137,18 @@ p
         +cell -
         +cell Specific overrides, like pipeline components to disable.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Language]
         +cell #[code Language] class with the loaded model.
 
-+h(2, "load_model_from_init_py") util.load_model_from_init_py
++h(3, "util.load_model_from_init_py") util.load_model_from_init_py
     +tag function
     +tag-new(2)
 
 p
     |  A helper function to use in the #[code load()] method of a model package's
-    |  #[+src(gh("spacy-dev-resources", "templates/model/en_model_name/__init__.py")) __init__.py].
+    |  #[+src(gh("spacy-dev-resources", "templates/model/en_model_name/__init__.py")) #[code __init__.py]].
 
 +aside-code("Example").
     from spacy.util import load_model_from_init_py
@@ -169,12 +167,12 @@ p
         +cell -
         +cell Specific overrides, like pipeline components to disable.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Language]
         +cell #[code Language] class with the loaded model.
 
-+h(2, "get_model_meta") util.get_model_meta
++h(3, "util.get_model_meta") util.get_model_meta
     +tag function
     +tag-new(2)
 
@@ -190,17 +188,17 @@ p
         +cell unicode or #[code Path]
         +cell Path to model directory.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell dict
         +cell The model's meta data.
 
-+h(2, "is_package") util.is_package
++h(3, "util.is_package") util.is_package
     +tag function
 
 p
     |  Check if string maps to a package installed via pip. Mainly used to
-    |  validate #[+a("/docs/usage/models") model packages].
+    |  validate #[+a("/usage/models") model packages].
 
 +aside-code("Example").
     util.is_package('en_core_web_sm') # True
@@ -212,18 +210,18 @@ p
         +cell unicode
         +cell Name of package.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code bool]
         +cell #[code True] if installed package, #[code False] if not.
 
-+h(2, "get_package_path") util.get_package_path
++h(3, "util.get_package_path") util.get_package_path
     +tag function
     +tag-new(2)
 
 p
     |  Get path to an installed package. Mainly used to resolve the location of
-    |  #[+a("/docs/usage/models") model packages]. Currently imports the package
+    |  #[+a("/usage/models") model packages]. Currently imports the package
     |  to find its path.
 
 +aside-code("Example").
@@ -236,12 +234,12 @@ p
         +cell unicode
         +cell Name of installed package.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Path]
         +cell Path to model package directory.
 
-+h(2, "is_in_jupyter") util.is_in_jupyter
++h(3, "util.is_in_jupyter") util.is_in_jupyter
     +tag function
     +tag-new(2)
 
@@ -257,17 +255,17 @@ p
         return display(HTML(html))
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell bool
         +cell #[code True] if in Jupyter, #[code False] if not.
 
-+h(2, "update_exc") util.update_exc
++h(3, "util.update_exc") util.update_exc
     +tag function
 
 p
     |  Update, validate and overwrite
-    |  #[+a("/docs/usage/adding-languages#tokenizer-exceptions") tokenizer exceptions].
+    |  #[+a("/usage/adding-languages#tokenizer-exceptions") tokenizer exceptions].
     |  Used to combine global  exceptions with custom, language-specific
     |  exceptions. Will raise an error if key doesn't match #[code ORTH] values.
 
@@ -288,20 +286,20 @@ p
         +cell dicts
         +cell Exception dictionaries to add to the base exceptions, in order.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell dict
         +cell Combined tokenizer exceptions.
 
 
-+h(2, "prints") util.prints
++h(3, "util.prints") util.prints
     +tag function
     +tag-new(2)
 
 p
     |  Print a formatted, text-wrapped message with optional title. If a text
     |  argument is a #[code Path], it's converted to a string. Should only
-    |  be used for interactive components like the #[+api("cli") cli].
+    |  be used for interactive components like the command-line interface.
 
 +aside-code("Example").
     data_path = Path('/some/path')
diff --git a/website/api/annotation.jade b/website/api/annotation.jade
new file mode 100644
index 000000000..efada23d7
--- /dev/null
+++ b/website/api/annotation.jade
@@ -0,0 +1,131 @@
+//- 💫 DOCS > API > ANNOTATION SPECS
+
+include ../_includes/_mixins
+
+p This document describes the target annotations spaCy is trained to predict.
+
+
++section("tokenization")
+    +h(2, "tokenization") Tokenization
+
+    p
+        |  Tokenization standards are based on the
+        |  #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] corpus.
+        |  The tokenizer differs from most by including tokens for significant
+        |  whitespace. Any sequence of whitespace characters beyond a single space
+        |  (#[code ' ']) is included as a token.
+
+    +aside-code("Example").
+        from spacy.lang.en import English
+        nlp = English()
+        tokens = nlp('Some\nspaces  and\ttab characters')
+        tokens_text = [t.text for t in tokens]
+        assert tokens_text == ['Some', '\n', 'spaces', ' ', 'and',
+                            '\t', 'tab', 'characters']
+
+    p
+        |  The whitespace tokens are useful for much the same reason punctuation is
+        |  – it's often an important delimiter in the text. By preserving it in the
+        |  token output, we are able to maintain a simple alignment between the
+        |  tokens and the original string, and we ensure that no information is
+        |  lost during processing.
+
++section("sbd")
+    +h(2, "sentence-boundary") Sentence boundary detection
+
+    p
+        |  Sentence boundaries are calculated from the syntactic parse tree, so
+        |  features such as punctuation and capitalisation play an important but
+        |  non-decisive role in determining the sentence boundaries. Usually this
+        |  means that the sentence boundaries will at least coincide with clause
+        |  boundaries, even given poorly punctuated text.
+
++section("pos-tagging")
+    +h(2, "pos-tagging") Part-of-speech Tagging
+
+    +aside("Tip: Understanding tags")
+        |  You can also use #[code spacy.explain()] to get the description for the
+        |  string representation of a tag. For example,
+        |  #[code spacy.explain("RB")] will return "adverb".
+
+    include _annotation/_pos-tags
+
++section("lemmatization")
+    +h(2, "lemmatization") Lemmatization
+
+    p A "lemma" is the uninflected form of a word. In English, this means:
+
+    +list
+        +item #[strong Adjectives]: The form like "happy", not "happier" or "happiest"
+        +item #[strong Adverbs]: The form like "badly", not "worse" or "worst"
+        +item #[strong Nouns]: The form like "dog", not "dogs"; like "child", not "children"
+        +item #[strong Verbs]: The form like "write", not "writes", "writing", "wrote" or "written"
+
+    p
+        |  The lemmatization data is taken from
+        |  #[+a("https://wordnet.princeton.edu") WordNet]. However, we also add a
+        |  special case for pronouns: all pronouns are lemmatized to the special
+        |  token #[code -PRON-].
+
+    +infobox("About spaCy's custom pronoun lemma")
+        |  Unlike verbs and common nouns, there's no clear base form of a personal
+        |  pronoun. Should the lemma of "me" be "I", or should we normalize person
+        |  as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
+        |  novel symbol, #[code -PRON-], which is used as the lemma for
+        |  all personal pronouns.
+
++section("dependency-parsing")
+    +h(2, "dependency-parsing") Syntactic Dependency Parsing
+
+    +aside("Tip: Understanding labels")
+        |  You can also use #[code spacy.explain()] to get the description for the
+        |  string representation of a label. For example,
+        |  #[code spacy.explain("prt")] will return "particle".
+
+    include _annotation/_dep-labels
+
++section("named-entities")
+    +h(2, "named-entities") Named Entity Recognition
+
+    +aside("Tip: Understanding entity types")
+        |  You can also use #[code spacy.explain()] to get the description for the
+        |  string representation of an entity label. For example,
+        |  #[code spacy.explain("LANGUAGE")] will return "any named language".
+
+    include _annotation/_named-entities
+
+    +h(3, "biluo") BILUO Scheme
+
+    include _annotation/_biluo
+
++section("training")
+    +h(2, "json-input") JSON input format for training
+
+    +under-construction
+
+    p spaCy takes training data in the following format:
+
+    +code("Example structure").
+        doc: {
+            id: string,
+            paragraphs: [{
+                raw: string,
+                sents: [int],
+                tokens: [{
+                    start: int,
+                    tag: string,
+                    head: int,
+                    dep: string
+                }],
+                ner: [{
+                    start: int,
+                    end: int,
+                    label: string
+                }],
+                brackets: [{
+                    start: int,
+                    end: int,
+                    label: string
+                }]
+            }]
+        }
diff --git a/website/docs/api/binder.jade b/website/api/binder.jade
similarity index 79%
rename from website/docs/api/binder.jade
rename to website/api/binder.jade
index 0dea1b339..e47cb597d 100644
--- a/website/docs/api/binder.jade
+++ b/website/api/binder.jade
@@ -1,6 +1,6 @@
 //- 💫 DOCS > API > BINDER
 
-include ../../_includes/_mixins
+include ../_includes/_mixins
 
 p A container class for serializing collections of #[code Doc] objects.
 
diff --git a/website/api/dependencyparser.jade b/website/api/dependencyparser.jade
new file mode 100644
index 000000000..ca56d6816
--- /dev/null
+++ b/website/api/dependencyparser.jade
@@ -0,0 +1,5 @@
+//- 💫 DOCS > API > DEPENDENCYPARSER
+
+include ../_includes/_mixins
+
+!=partial("pipe", { subclass: "DependencyParser", short: "parser", pipeline_id: "parser" })
diff --git a/website/docs/api/doc.jade b/website/api/doc.jade
similarity index 97%
rename from website/docs/api/doc.jade
rename to website/api/doc.jade
index 7fbbcce97..85932c605 100644
--- a/website/docs/api/doc.jade
+++ b/website/api/doc.jade
@@ -1,8 +1,6 @@
 //- 💫 DOCS > API > DOC
 
-include ../../_includes/_mixins
-
-p A container for accessing linguistic annotations.
+include ../_includes/_mixins
 
 p
     |  A #[code Doc] is a sequence of #[+api("token") #[code Token]] objects.
@@ -47,7 +45,7 @@ p
             |  subsequent space. Must have the same length as #[code words], if
             |  specified. Defaults to a sequence of #[code True].
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Doc]
         +cell The newly constructed object.
@@ -73,7 +71,7 @@ p
         +cell int
         +cell The index of the token.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Token]
         +cell The token at #[code doc[i]].
@@ -96,7 +94,7 @@ p
         +cell tuple
         +cell The slice of the document to get.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Span]
         +cell The span at #[code doc[start : end]].
@@ -120,7 +118,7 @@ p
     |  from Cython.
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Token]
         +cell A #[code Token] object.
@@ -135,7 +133,7 @@ p Get the number of tokens in the document.
     assert len(doc) == 7
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell int
         +cell The number of tokens in the document.
@@ -172,7 +170,7 @@ p Create a #[code Span] object from the slice #[code doc.text[start : end]].
         +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
         +cell A meaning representation of the span.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Span]
         +cell The newly constructed object.
@@ -200,7 +198,7 @@ p
             |  The object to compare with. By default, accepts #[code Doc],
             |  #[code Span], #[code Token] and #[code Lexeme] objects.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell float
         +cell A scalar similarity score. Higher is more similar.
@@ -226,7 +224,7 @@ p
         +cell int
         +cell The attribute ID
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell dict
         +cell A dictionary mapping attributes to integer counts.
@@ -251,7 +249,7 @@ p
         +cell list
         +cell A list of attribute ID ints.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code.u-break numpy.ndarray[ndim=2, dtype='int32']]
         +cell
@@ -285,7 +283,7 @@ p
         +cell #[code.u-break numpy.ndarray[ndim=2, dtype='int32']]
         +cell The attribute values to load.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Doc]
         +cell Itself.
@@ -326,7 +324,7 @@ p Loads state from a directory. Modifies the object in place and returns it.
             |  A path to a directory. Paths may be either strings or
             |  #[code Path]-like objects.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Doc]
         +cell The modified #[code Doc] object.
@@ -341,7 +339,7 @@ p Serialize, i.e. export the document contents to a binary string.
     doc_bytes = doc.to_bytes()
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell bytes
         +cell
@@ -367,7 +365,7 @@ p Deserialize, i.e. import the document contents from a binary string.
         +cell bytes
         +cell The string to load from.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Doc]
         +cell The #[code Doc] object.
@@ -378,7 +376,7 @@ p Deserialize, i.e. import the document contents from a binary string.
 p
     |  Retokenize the document, such that the span at
     |  #[code doc.text[start_idx : end_idx]] is merged into a single token. If
-    |  #[code start_idx] and #[end_idx] do not mark start and end token
+    |  #[code start_idx] and #[code end_idx] do not mark start and end token
     |  boundaries, the document remains unchanged.
 
 +aside-code("Example").
@@ -405,7 +403,7 @@ p
             |  attributes are inherited from the syntactic root token of
             |  the span.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Token]
         +cell
@@ -440,7 +438,7 @@ p
         +cell bool
         +cell Don't include arcs or modifiers.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell dict
         +cell Parse tree as dict.
@@ -462,7 +460,7 @@ p
     assert ents[0].text == 'Mr. Best'
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Span]
         +cell Entities in the document.
@@ -485,7 +483,7 @@ p
     assert chunks[1].text == "another phrase"
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Span]
         +cell Noun chunks in the document.
@@ -507,7 +505,7 @@ p
     assert [s.root.text for s in sents] == ["is", "'s"]
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Span]
         +cell Sentences in the document.
@@ -525,7 +523,7 @@ p
     assert doc.has_vector
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell bool
         +cell Whether the document has a vector data attached.
@@ -544,7 +542,7 @@ p
     assert doc.vector.shape == (300,)
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
         +cell A 1D numpy array representing the document's semantics.
@@ -564,7 +562,7 @@ p
     assert doc1.vector_norm != doc2.vector_norm
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell float
         +cell The L2 norm of the vector representation.
diff --git a/website/api/entityrecognizer.jade b/website/api/entityrecognizer.jade
new file mode 100644
index 000000000..aff33bde7
--- /dev/null
+++ b/website/api/entityrecognizer.jade
@@ -0,0 +1,5 @@
+//- 💫 DOCS > API > ENTITYRECOGNIZER
+
+include ../_includes/_mixins
+
+!=partial("pipe", { subclass: "EntityRecognizer", short: "ner", pipeline_id: "ner" })
diff --git a/website/docs/api/goldcorpus.jade b/website/api/goldcorpus.jade
similarity index 71%
rename from website/docs/api/goldcorpus.jade
rename to website/api/goldcorpus.jade
index 3b3d92823..0f7105f65 100644
--- a/website/docs/api/goldcorpus.jade
+++ b/website/api/goldcorpus.jade
@@ -1,14 +1,12 @@
 //- 💫 DOCS > API > GOLDCORPUS
 
-include ../../_includes/_mixins
+include ../_includes/_mixins
 
 p
-    |  An annotated corpus, using the JSON file format. Manages annotations for
-    |  tagging, dependency parsing and NER.
+    |  This class manages annotations for tagging, dependency parsing and NER.
 
 +h(2, "init") GoldCorpus.__init__
     +tag method
-    +tag-new(2)
 
 p Create a #[code GoldCorpus].
 
diff --git a/website/docs/api/goldparse.jade b/website/api/goldparse.jade
similarity index 95%
rename from website/docs/api/goldparse.jade
rename to website/api/goldparse.jade
index 03118343d..c27badee9 100644
--- a/website/docs/api/goldparse.jade
+++ b/website/api/goldparse.jade
@@ -1,6 +1,6 @@
 //- 💫 DOCS > API > GOLDPARSE
 
-include ../../_includes/_mixins
+include ../_includes/_mixins
 
 p Collection for training annotations.
 
@@ -40,7 +40,7 @@ p Create a #[code GoldParse].
         +cell iterable
         +cell A sequence of named entity annotations, either as BILUO tag strings, or as #[code (start_char, end_char, label)] tuples, representing the entity positions.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code GoldParse]
         +cell The newly constructed object.
@@ -51,7 +51,7 @@ p Create a #[code GoldParse].
 p Get the number of gold-standard tokens.
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell int
         +cell The number of gold-standard tokens.
@@ -64,7 +64,7 @@ p
     |  tree.
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell bool
         +cell Whether annotations form projective tree.
@@ -119,7 +119,7 @@ p
 
 p
     |  Encode labelled spans into per-token tags, using the
-    |  #[+a("/docs/api/annotation#biluo") BILUO scheme] (Begin/In/Last/Unit/Out).
+    |  #[+a("/api/annotation#biluo") BILUO scheme] (Begin/In/Last/Unit/Out).
 
 p
     |  Returns a list of unicode strings, describing the tags. Each tag string
@@ -157,11 +157,11 @@ p
             |  and #[code end] should be character-offset integers denoting the
             |  slice into the original string.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell list
         +cell
             |  Unicode strings, describing the
-            |  #[+a("/docs/api/annotation#biluo") BILUO] tags.
+            |  #[+a("/api/annotation#biluo") BILUO] tags.
 
 
diff --git a/website/api/index.jade b/website/api/index.jade
new file mode 100644
index 000000000..8035c9ff5
--- /dev/null
+++ b/website/api/index.jade
@@ -0,0 +1,14 @@
+//- 💫 DOCS > API > ARCHITECTURE
+
+include ../_includes/_mixins
+
++section("basics")
+    include ../usage/_spacy-101/_architecture
+
++section("nn-model")
+    +h(2, "nn-model") Neural network model architecture
+    include _architecture/_nn-model
+
++section("cython")
+    +h(2, "cython") Cython conventions
+    include _architecture/_cython
diff --git a/website/docs/api/language.jade b/website/api/language.jade
similarity index 92%
rename from website/docs/api/language.jade
rename to website/api/language.jade
index 69665ee9d..617c81599 100644
--- a/website/docs/api/language.jade
+++ b/website/api/language.jade
@@ -1,10 +1,10 @@
 //- 💫 DOCS > API > LANGUAGE
 
-include ../../_includes/_mixins
+include ../_includes/_mixins
 
 p
-    |  A text-processing pipeline. Usually you'll load this once per process,
-    |  and pass the instance around your application.
+    |  Usually you'll load this once per process as #[code nlp] and pass the
+    |  instance around your application.
 
 +h(2, "init") Language.__init__
     +tag method
@@ -49,7 +49,7 @@ p Initialise a #[code Language] object.
             |  Custom meta data for the #[code Language] class. Is written to by
             |  models to add model meta data.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Language]
         +cell The newly constructed object.
@@ -77,14 +77,14 @@ p
         +cell list
         +cell
             |  Names of pipeline components to
-            |  #[+a("/docs/usage/language-processing-pipeline#disabling") disable].
+            |  #[+a("/usage/processing-pipelines#disabling") disable].
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Doc]
         +cell A container for accessing the annotations.
 
-+infobox("⚠️ Deprecation note")
++infobox("Deprecation note", "⚠️")
     .o-block
         |  Pipeline components to prevent from being loaded can now be added as
         |  a list to #[code disable], instead of specifying one keyword argument
@@ -136,9 +136,9 @@ p
         +cell list
         +cell
             |  Names of pipeline components to
-            |  #[+a("/docs/usage/language-processing-pipeline#disabling") disable].
+            |  #[+a("/usage/processing-pipelines#disabling") disable].
 
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Doc]
         +cell Documents in the order of the original text.
@@ -175,7 +175,7 @@ p Update the models in the pipeline.
         +cell callable
         +cell An optimizer.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell dict
         +cell Results from the update.
@@ -200,7 +200,7 @@ p
         +cell -
         +cell Config parameters.
 
-    +footrow
+    +row("foot")
         +cell yields
         +cell tuple
         +cell An optimizer.
@@ -242,7 +242,7 @@ p
         +cell iterable
         +cell Tuples of #[code Doc] and #[code GoldParse] objects.
 
-    +footrow
+    +row("foot")
         +cell yields
         +cell tuple
         +cell Tuples of #[code Doc] and #[code GoldParse] objects.
@@ -271,7 +271,7 @@ p
         +cell list
         +cell
             |  Names of pipeline components to
-            |  #[+a("/docs/usage/language-processing-pipeline#disabling") disable]
+            |  #[+a("/usage/processing-pipelines#disabling") disable]
             |  and prevent from being saved.
 
 +h(2, "from_disk") Language.from_disk
@@ -300,14 +300,14 @@ p
         +cell list
         +cell
             |  Names of pipeline components to
-            |  #[+a("/docs/usage/language-processing-pipeline#disabling") disable].
+            |  #[+a("/usage/processing-pipelines#disabling") disable].
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Language]
         +cell The modified #[code Language] object.
 
-+infobox("⚠️ Deprecation note")
++infobox("Deprecation note", "⚠️")
     .o-block
         |  As of spaCy v2.0, the #[code save_to_directory] method has been
         |  renamed to #[code to_disk], to improve consistency across classes.
@@ -332,10 +332,10 @@ p Serialize the current state to a binary string.
         +cell list
         +cell
             |  Names of pipeline components to
-            |  #[+a("/docs/usage/language-processing-pipeline#disabling") disable]
+            |  #[+a("/usage/processing-pipelines#disabling") disable]
             |  and prevent from being serialized.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell bytes
         +cell The serialized form of the #[code Language] object.
@@ -362,14 +362,14 @@ p Load state from a binary string.
         +cell list
         +cell
             |  Names of pipeline components to
-            |  #[+a("/docs/usage/language-processing-pipeline#disabling") disable].
+            |  #[+a("/usage/processing-pipelines#disabling") disable].
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Language]
         +cell The #[code Language] object.
 
-+infobox("⚠️ Deprecation note")
++infobox("Deprecation note", "⚠️")
     .o-block
         |  Pipeline components to prevent from being loaded can now be added as
         |  a list to #[code disable], instead of specifying one keyword argument
diff --git a/website/api/lemmatizer.jade b/website/api/lemmatizer.jade
new file mode 100644
index 000000000..9699395b1
--- /dev/null
+++ b/website/api/lemmatizer.jade
@@ -0,0 +1,5 @@
+//- 💫 DOCS > API > LEMMATIZER
+
+include ../_includes/_mixins
+
++under-construction
diff --git a/website/docs/api/lexeme.jade b/website/api/lexeme.jade
similarity index 98%
rename from website/docs/api/lexeme.jade
rename to website/api/lexeme.jade
index 6e3f68493..dddefd2d7 100644
--- a/website/docs/api/lexeme.jade
+++ b/website/api/lexeme.jade
@@ -1,6 +1,6 @@
 //- 💫 DOCS > API > LEXEME
 
-include ../../_includes/_mixins
+include ../_includes/_mixins
 
 p
     |  An entry in the vocabulary. A #[code Lexeme] has no string context – it's
@@ -24,7 +24,7 @@ p Create a #[code Lexeme] object.
         +cell int
         +cell The orth id of the lexeme.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Lexeme]
         +cell The newly constructed object.
@@ -65,7 +65,7 @@ p Check the value of a boolean flag.
         +cell int
         +cell The attribute ID of the flag to query.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell bool
         +cell The value of the flag.
@@ -91,7 +91,7 @@ p Compute a semantic similarity estimate. Defaults to cosine over vectors.
             |  The object to compare with. By default, accepts #[code Doc],
             |  #[code Span], #[code Token] and #[code Lexeme] objects.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell float
         +cell A scalar similarity score. Higher is more similar.
@@ -110,7 +110,7 @@ p
     assert apple.has_vector
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell bool
         +cell Whether the lexeme has a vector data attached.
@@ -127,7 +127,7 @@ p A real-valued meaning representation.
     assert apple.vector.shape == (300,)
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
         +cell A 1D numpy array representing the lexeme's semantics.
@@ -146,7 +146,7 @@ p The L2 norm of the lexeme's vector representation.
     assert apple.vector_norm != pasta.vector_norm
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell float
         +cell The L2 norm of the vector representation.
diff --git a/website/docs/api/matcher.jade b/website/api/matcher.jade
similarity index 96%
rename from website/docs/api/matcher.jade
rename to website/api/matcher.jade
index 95819e553..35aba4cba 100644
--- a/website/docs/api/matcher.jade
+++ b/website/api/matcher.jade
@@ -1,10 +1,8 @@
 //- 💫 DOCS > API > MATCHER
 
-include ../../_includes/_mixins
+include ../_includes/_mixins
 
-p Match sequences of tokens, based on pattern rules.
-
-+infobox("⚠️ Deprecation note")
++infobox("Deprecation note", "⚠️")
     |  As of spaCy 2.0, #[code Matcher.add_pattern] and #[code Matcher.add_entity]
     |  are deprecated and have been replaced with a simpler
     |  #[+api("matcher#add") #[code Matcher.add]] that lets you add a list of
@@ -39,7 +37,7 @@ p Create the rule-based #[code Matcher].
         +cell dict
         +cell Patterns to add to the matcher, keyed by ID.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Matcher]
         +cell The newly constructed object.
@@ -64,7 +62,7 @@ p Find all token sequences matching the supplied patterns on the #[code Doc].
         +cell #[code Doc]
         +cell The document to match over.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell list
         +cell
@@ -81,7 +79,7 @@ p Find all token sequences matching the supplied patterns on the #[code Doc].
     |  actions per pattern within the same matcher. For example, you might only
     |  want to merge some entity types, and set custom flags for other matched
     |  patterns. For more details and examples, see the usage guide on
-    |  #[+a("/docs/usage/rule-based-matching") rule-based matching].
+    |  #[+a("/usage/linguistic-features#rule-based-matching") rule-based matching].
 
 +h(2, "pipe") Matcher.pipe
     +tag method
@@ -113,7 +111,7 @@ p Match a stream of documents, yielding them in turn.
             |  parallel, if the #[code Matcher] implementation supports
             |  multi-threading.
 
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Doc]
         +cell Documents, in order.
@@ -134,7 +132,7 @@ p
     assert len(matcher) == 1
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell int
         +cell The number of rules.
@@ -156,7 +154,8 @@ p Check whether the matcher contains rules for a match ID.
         +cell #[code key]
         +cell unicode
         +cell The match ID.
-    +footrow
+
+    +row("foot")
         +cell returns
         +cell int
         +cell Whether the matcher contains rules for this match ID.
@@ -203,7 +202,7 @@ p
             |  Match pattern. A pattern consists of a list of dicts, where each
             |  dict describes a token.
 
-+infobox("⚠️ Deprecation note")
++infobox("Deprecation note", "⚠️")
     .o-block
         |  As of spaCy 2.0, #[code Matcher.add_pattern] and #[code Matcher.add_entity]
         |  are deprecated and have been replaced with a simpler
@@ -257,7 +256,7 @@ p
         +cell unicode
         +cell The ID of the match rule.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell tuple
         +cell The rule, as an #[code (on_match, patterns)] tuple.
diff --git a/website/api/phrasematcher.jade b/website/api/phrasematcher.jade
new file mode 100644
index 000000000..5c49a03d5
--- /dev/null
+++ b/website/api/phrasematcher.jade
@@ -0,0 +1,181 @@
+//- 💫 DOCS > API > PHRASEMATCHER
+
+include ../_includes/_mixins
+
+p
+    |  The #[code PhraseMatcher] lets you efficiently match large terminology
+    |  lists. While the #[+api("matcher") #[code Matcher]] lets you match
+    |  squences based on lists of token descriptions, the #[code PhraseMatcher]
+    |  accepts match patterns in the form of #[code Doc] objects.
+
++h(2, "init") PhraseMatcher.__init__
+    +tag method
+
+p Create the rule-based #[code PhraseMatcher].
+
++aside-code("Example").
+    from spacy.matcher import PhraseMatcher
+    matcher = Matcher(nlp.vocab, max_length=6)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code vocab]
+        +cell #[code Vocab]
+        +cell
+            |  The vocabulary object, which must be shared with the documents
+            |  the matcher will operate on.
+
+    +row
+        +cell #[code max_length]
+        +cell int
+        +cell Mamimum length of a phrase pattern to add.
+
+    +row("foot")
+        +cell returns
+        +cell #[code PhraseMatcher]
+        +cell The newly constructed object.
+
++h(2, "call") PhraseMatcher.__call__
+    +tag method
+
+p Find all token sequences matching the supplied patterns on the #[code Doc].
+
++aside-code("Example").
+    from spacy.matcher import Matcher
+
+    matcher = Matcher(nlp.vocab)
+    matcher.add('OBAMA', None, nlp(u"Barack Obama"))
+    doc = nlp(u"Barack Obama lifts America one last time in emotional farewell")
+    matches = matcher(doc)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code doc]
+        +cell #[code Doc]
+        +cell The document to match over.
+
+    +row("foot")
+        +cell returns
+        +cell list
+        +cell
+            |  A list of #[code (match_id, start, end)] tuples, describing the
+            |  matches. A match tuple describes a span #[code doc[start:end]].
+            |  The #[code match_id] is the ID of the added match pattern.
+
++h(2, "pipe") PhraseMatcher.pipe
+    +tag method
+
+p Match a stream of documents, yielding them in turn.
+
++aside-code("Example").
+    from spacy.matcher import PhraseMatcher
+    matcher = PhraseMatcher(nlp.vocab)
+    for doc in matcher.pipe(texts, batch_size=50, n_threads=4):
+        pass
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code docs]
+        +cell iterable
+        +cell A stream of documents.
+
+    +row
+        +cell #[code batch_size]
+        +cell int
+        +cell The number of documents to accumulate into a working set.
+
+    +row
+        +cell #[code n_threads]
+        +cell int
+        +cell
+            |  The number of threads with which to work on the buffer in
+            |  parallel, if the #[code PhraseMatcher] implementation supports
+            |  multi-threading.
+
+    +row("foot")
+        +cell yields
+        +cell #[code Doc]
+        +cell Documents, in order.
+
++h(2, "len") PhraseMatcher.__len__
+    +tag method
+
+p
+    |  Get the number of rules added to the matcher. Note that this only returns
+    |  the number of rules (identical with the number of IDs), not the number
+    |  of individual patterns.
+
++aside-code("Example").
+    matcher = PhraseMatcher(nlp.vocab)
+    assert len(matcher) == 0
+    matcher.add('OBAMA', None, nlp(u"Barack Obama"))
+    assert len(matcher) == 1
+
++table(["Name", "Type", "Description"])
+    +row("foot")
+        +cell returns
+        +cell int
+        +cell The number of rules.
+
++h(2, "contains") PhraseMatcher.__contains__
+    +tag method
+
+p Check whether the matcher contains rules for a match ID.
+
++aside-code("Example").
+    matcher = PhraseMatcher(nlp.vocab)
+    assert len(matcher) == 0
+    matcher.add('OBAMA', None, nlp(u"Barack Obama"))
+    assert len(matcher) == 1
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code key]
+        +cell unicode
+        +cell The match ID.
+
+    +row("foot")
+        +cell returns
+        +cell int
+        +cell Whether the matcher contains rules for this match ID.
+
++h(2, "add") PhraseMatcher.add
+    +tag method
+
+p
+    |  Add a rule to the matcher, consisting of an ID key, one or more patterns, and
+    |  a callback function to act on the matches. The callback function will
+    |  receive the arguments #[code matcher], #[code doc], #[code i] and
+    |  #[code matches]. If a pattern already exists for the given ID, the
+    |  patterns will be extended. An #[code on_match] callback will be
+    |  overwritten.
+
++aside-code("Example").
+    def on_match(matcher, doc, id, matches):
+        print('Matched!', matches)
+
+    matcher = PhraseMatcher(nlp.vocab)
+    matcher.add('OBAMA', on_match, nlp(u"Barack Obama"))
+    matcher.add('HEALTH', on_match, nlp(u"health care reform"),
+                                    nlp(u"healthcare reform"))
+    doc = nlp(u"Barack Obama urges Congress to find courage to defend his healthcare reforms")
+    matches = matcher(doc)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code match_id]
+        +cell unicode
+        +cell An ID for the thing you're matching.
+
+    +row
+        +cell #[code on_match]
+        +cell callable or #[code None]
+        +cell
+            |  Callback function to act on matches. Takes the arguments
+            |  #[code matcher], #[code doc], #[code i] and #[code matches].
+
+    +row
+        +cell #[code *docs]
+        +cell list
+        +cell
+            |  #[code Doc] objects of the phrases to match.
diff --git a/website/api/pipe.jade b/website/api/pipe.jade
new file mode 100644
index 000000000..66bdbcc62
--- /dev/null
+++ b/website/api/pipe.jade
@@ -0,0 +1,390 @@
+//- 💫 DOCS > API > PIPE
+
+include ../_includes/_mixins
+
+//- This page can be used as a template for all other classes that inherit
+//-  from `Pipe`.
+
+if subclass
+    +infobox
+        |  This class is a subclass of #[+api("pipe") #[code Pipe]] and
+        |  follows the same API. The pipeline component is available in the
+        |  #[+a("/usage/processing-pipelines") processing pipeline] via the ID
+        |  #[code "#{pipeline_id}"].
+
+else
+    p
+        |  This class is not instantiated directly. Components inherit from it,
+        |  and it defines the interface that components should follow to
+        |  function as components in a spaCy analysis pipeline.
+
+- CLASSNAME = subclass || 'Pipe'
+- VARNAME = short || CLASSNAME.toLowerCase()
+
+
++h(2, "model") #{CLASSNAME}.Model
+    +tag classmethod
+
+p
+    |  Initialise a model for the pipe. The model should implement the
+    |  #[code thinc.neural.Model] API. Wrappers are available for
+    |  #[+a("/usage/deep-learning") most major machine learning libraries].
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code **kwargs]
+        +cell -
+        +cell Parameters for initialising the model
+
+    +row("foot")
+        +cell returns
+        +cell object
+        +cell The initialised model.
+
++h(2, "init") #{CLASSNAME}.__init__
+    +tag method
+
+p Create a new pipeline instance.
+
++aside-code("Example").
+    from spacy.pipeline import #{CLASSNAME}
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code vocab]
+        +cell #[code Vocab]
+        +cell The shared vocabulary.
+
+    +row
+        +cell #[code model]
+        +cell #[code thinc.neural.Model] or #[code True]
+        +cell
+            |  The model powering the pipeline component. If no model is
+            |  supplied, the model is created when you call
+            |  #[code begin_training], #[code from_disk] or #[code from_bytes].
+
+    +row
+        +cell #[code **cfg]
+        +cell -
+        +cell Configuration parameters.
+
+    +row("foot")
+        +cell returns
+        +cell #[code=CLASSNAME]
+        +cell The newly constructed object.
+
++h(2, "call") #{CLASSNAME}.__call__
+    +tag method
+
+p
+    |  Apply the pipe to one document. The document is modified in place, and
+    |  returned. Both #[code #{CLASSNAME}.__call__] and
+    |  #[code #{CLASSNAME}.pipe] should delegate to the
+    |  #[code #{CLASSNAME}.predict] and #[code #{CLASSNAME}.set_annotations]
+    |  methods.
+
++aside-code("Example").
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+    doc = nlp(u"This is a sentence.")
+    processed = #{VARNAME}(doc)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code doc]
+        +cell #[code Doc]
+        +cell The document to process.
+
+    +row("foot")
+        +cell returns
+        +cell #[code Doc]
+        +cell The processed document.
+
++h(2, "pipe") #{CLASSNAME}.pipe
+    +tag method
+
+p
+    |  Apply the pipe to a stream of documents. Both
+    |  #[code #{CLASSNAME}.__call__] and #[code #{CLASSNAME}.pipe] should
+    |  delegate to the #[code #{CLASSNAME}.predict] and
+    |  #[code #{CLASSNAME}.set_annotations] methods.
+
++aside-code("Example").
+    texts = [u'One doc', u'...', u'Lots of docs']
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+    for doc in #{VARNAME}.pipe(texts, batch_size=50):
+        pass
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code stream]
+        +cell iterable
+        +cell A stream of documents.
+
+    +row
+        +cell #[code batch_size]
+        +cell int
+        +cell The number of texts to buffer. Defaults to #[code 128].
+
+    +row
+        +cell #[code n_threads]
+        +cell int
+        +cell
+            |  The number of worker threads to use. If #[code -1], OpenMP will
+            |  decide how many to use at run time. Default is #[code -1].
+
+    +row("foot")
+        +cell yields
+        +cell #[code Doc]
+        +cell Processed documents in the order of the original text.
+
++h(2, "predict") #{CLASSNAME}.predict
+    +tag method
+
+p
+    |  Apply the pipeline's model to a batch of docs, without modifying them.
+
++aside-code("Example").
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+    scores = #{VARNAME}.predict([doc1, doc2])
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code docs]
+        +cell iterable
+        +cell The documents to predict.
+
+    +row("foot")
+        +cell returns
+        +cell -
+        +cell Scores from the model.
+
++h(2, "set_annotations") #{CLASSNAME}.set_annotations
+    +tag method
+
+p
+    |  Modify a batch of documents, using pre-computed scores.
+
++aside-code("Example").
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+    scores = #{VARNAME}.predict([doc1, doc2])
+    #{VARNAME}.set_annotations([doc1, doc2], scores)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code docs]
+        +cell iterable
+        +cell The documents to modify.
+
+    +row
+        +cell #[code scores]
+        +cell -
+        +cell The scores to set, produced by #[code #{CLASSNAME}.predict].
+
++h(2, "update") #{CLASSNAME}.update
+    +tag method
+
+p
+    |  Learn from a batch of documents and gold-standard information, updating
+    |  the pipe's model. Delegates to #[code #{CLASSNAME}.predict] and
+    |  #[code #{CLASSNAME}.get_loss].
+
++aside-code("Example").
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+    losses = {}
+    optimizer = nlp.begin_training()
+    #{VARNAME}.update([doc1, doc2], [gold1, gold2], losses=losses, sgd=optimizer)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code docs]
+        +cell iterable
+        +cell A batch of documents to learn from.
+
+    +row
+        +cell #[code golds]
+        +cell iterable
+        +cell The gold-standard data. Must have the same length as #[code docs].
+
+    +row
+        +cell #[code drop]
+        +cell int
+        +cell The dropout rate.
+
+    +row
+        +cell #[code sgd]
+        +cell callable
+        +cell
+            |  The optimizer. Should take two arguments #[code weights] and
+            |  #[code gradient], and an optional ID.
+
+    +row
+        +cell #[code losses]
+        +cell dict
+        +cell
+            |  Optional record of the loss during training. The value keyed by
+            |  the model's name is updated.
+
++h(2, "get_loss") #{CLASSNAME}.get_loss
+    +tag method
+
+p
+    |  Find the loss and gradient of loss for the batch of documents and their
+    |  predicted scores.
+
++aside-code("Example").
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+    scores = #{VARNAME}.predict([doc1, doc2])
+    loss, d_loss = #{VARNAME}.get_loss([doc1, doc2], [gold1, gold2], scores)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code docs]
+        +cell iterable
+        +cell The batch of documents.
+
+    +row
+        +cell #[code golds]
+        +cell iterable
+        +cell The gold-standard data. Must have the same length as #[code docs].
+
+    +row
+        +cell #[code scores]
+        +cell -
+        +cell Scores representing the model's predictions.
+
+    +row("foot")
+        +cell returns
+        +cell tuple
+        +cell The loss and the gradient, i.e. #[code (loss, gradient)].
+
++h(2, "begin_training") #{CLASSNAME}.begin_training
+    +tag method
+
+p
+    |  Initialize the pipe for training, using data exampes if available. If no
+    |  model has been initialized yet, the model is added.
+
++aside-code("Example").
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+    nlp.pipeline.append(#{VARNAME})
+    #{VARNAME}.begin_training(pipeline=nlp.pipeline)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code gold_tuples]
+        +cell iterable
+        +cell
+            |  Optional gold-standard annotations from which to construct
+            |  #[+api("goldparse") #[code GoldParse]] objects.
+
+    +row
+        +cell #[code pipeline]
+        +cell list
+        +cell
+            |  Optional list of #[+api("pipe") #[code Pipe]] components that
+            |  this component is part of.
+
++h(2, "use_params") #{CLASSNAME}.use_params
+    +tag method
+    +tag contextmanager
+
+p Modify the pipe's model, to use the given parameter values.
+
++aside-code("Example").
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+    with #{VARNAME}.use_params():
+        #{VARNAME}.to_disk('/best_model')
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code params]
+        +cell -
+        +cell
+            |  The parameter values to use in the model. At the end of the
+            |  context, the original parameters are restored.
+
++h(2, "to_disk") #{CLASSNAME}.to_disk
+    +tag method
+
+p Serialize the pipe to disk.
+
++aside-code("Example").
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+    #{VARNAME}.to_disk('/path/to/#{VARNAME}')
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code path]
+        +cell unicode or #[code Path]
+        +cell
+            |  A path to a directory, which will be created if it doesn't exist.
+            |  Paths may be either strings or #[code Path]-like objects.
+
++h(2, "from_disk") #{CLASSNAME}.from_disk
+    +tag method
+
+p Load the pipe from disk. Modifies the object in place and returns it.
+
++aside-code("Example").
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+    #{VARNAME}.from_disk('/path/to/#{VARNAME}')
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code path]
+        +cell unicode or #[code Path]
+        +cell
+            |  A path to a directory. Paths may be either strings or
+            |  #[code Path]-like objects.
+
+    +row("foot")
+        +cell returns
+        +cell #[code=CLASSNAME]
+        +cell The modified #[code=CLASSNAME] object.
+
++h(2, "to_bytes") #{CLASSNAME}.to_bytes
+    +tag method
+
++aside-code("example").
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+    #{VARNAME}_bytes = #{VARNAME}.to_bytes()
+
+p Serialize the pipe to a bytestring.
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code **exclude]
+        +cell -
+        +cell Named attributes to prevent from being serialized.
+
+    +row("foot")
+        +cell returns
+        +cell bytes
+        +cell The serialized form of the #[code=CLASSNAME] object.
+
++h(2, "from_bytes") #{CLASSNAME}.from_bytes
+    +tag method
+
+p Load the pipe from a bytestring. Modifies the object in place and returns it.
+
++aside-code("Example").
+    #{VARNAME}_bytes = #{VARNAME}.to_bytes()
+    #{VARNAME} = #{CLASSNAME}(nlp.vocab)
+    #{VARNAME}.from_bytes(#{VARNAME}_bytes)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code bytes_data]
+        +cell bytes
+        +cell The data to load from.
+
+    +row
+        +cell #[code **exclude]
+        +cell -
+        +cell Named attributes to prevent from being loaded.
+
+    +row("foot")
+        +cell returns
+        +cell #[code=CLASSNAME]
+        +cell The #[code=CLASSNAME] object.
diff --git a/website/docs/api/span.jade b/website/api/span.jade
similarity index 97%
rename from website/docs/api/span.jade
rename to website/api/span.jade
index 72821ab04..067e709f0 100644
--- a/website/docs/api/span.jade
+++ b/website/api/span.jade
@@ -1,6 +1,6 @@
 //- 💫 DOCS > API > SPAN
 
-include ../../_includes/_mixins
+include ../_includes/_mixins
 
 p A slice from a #[+api("doc") #[code Doc]] object.
 
@@ -40,7 +40,7 @@ p Create a Span object from the #[code slice doc[start : end]].
         +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
         +cell A meaning representation of the span.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Span]
         +cell The newly constructed object.
@@ -61,7 +61,7 @@ p Get a #[code Token] object.
         +cell int
         +cell The index of the token within the span.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Token]
         +cell The token at #[code span[i]].
@@ -79,7 +79,7 @@ p Get a #[code Span] object.
         +cell tuple
         +cell The slice of the span to get.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Span]
         +cell The span at #[code span[start : end]].
@@ -95,7 +95,7 @@ p Iterate over #[code Token] objects.
     assert [t.text for t in span] == ['it', 'back', '!']
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Token]
         +cell A #[code Token] object.
@@ -111,7 +111,7 @@ p Get the number of tokens in the span.
     assert len(span) == 3
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell int
         +cell The number of tokens in the span.
@@ -140,7 +140,7 @@ p
             |  The object to compare with. By default, accepts #[code Doc],
             |  #[code Span], #[code Token] and #[code Lexeme] objects.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell float
         +cell A scalar similarity score. Higher is more similar.
@@ -167,7 +167,7 @@ p
         +cell list
         +cell A list of attribute ID ints.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code.u-break numpy.ndarray[long, ndim=2]]
         +cell
@@ -194,7 +194,7 @@ p Retokenize the document, such that the span is merged into a single token.
             |  Attributes to assign to the merged token. By default, attributes
             |  are inherited from the syntactic root token of the span.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Token]
         +cell The newly merged token.
@@ -216,7 +216,7 @@ p
     assert new_york.root.text == 'York'
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Token]
         +cell The root token.
@@ -233,7 +233,7 @@ p Tokens that are to the left of the span, whose head is within the span.
     assert lefts == [u'New']
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Token]
         +cell A left-child of a token of the span.
@@ -250,7 +250,7 @@ p Tokens that are to the right of the span, whose head is within the span.
     assert rights == [u'in']
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Token]
         +cell A right-child of a token of the span.
@@ -267,7 +267,7 @@ p Tokens that descend from tokens in the span, but fall outside it.
     assert subtree == [u'Give', u'it', u'back', u'!']
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Token]
         +cell A descendant of a token within the span.
@@ -285,7 +285,7 @@ p
     assert doc[1:].has_vector
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell bool
         +cell Whether the span has a vector data attached.
@@ -304,7 +304,7 @@ p
     assert doc[1:].vector.shape == (300,)
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
         +cell A 1D numpy array representing the span's semantics.
@@ -323,7 +323,7 @@ p
     assert doc[1:].vector_norm != doc[2:].vector_norm
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell float
         +cell The L2 norm of the vector representation.
diff --git a/website/docs/api/stringstore.jade b/website/api/stringstore.jade
similarity index 96%
rename from website/docs/api/stringstore.jade
rename to website/api/stringstore.jade
index c17fb1db9..9d03404cc 100644
--- a/website/docs/api/stringstore.jade
+++ b/website/api/stringstore.jade
@@ -1,6 +1,6 @@
 //- 💫 DOCS > API > STRINGSTORE
 
-include ../../_includes/_mixins
+include ../_includes/_mixins
 
 p
     |  Look up strings by 64-bit hashes. As of v2.0, spaCy uses hash values
@@ -23,7 +23,7 @@ p
         +cell iterable
         +cell A sequence of unicode strings to add to the store.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code StringStore]
         +cell The newly constructed object.
@@ -38,7 +38,7 @@ p Get the number of strings in the store.
     assert len(stringstore) == 2
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell int
         +cell The number of strings in the store.
@@ -60,7 +60,7 @@ p Retrieve a string from a given hash, or vice versa.
         +cell bytes, unicode or uint64
         +cell The value to encode.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell unicode or int
         +cell The value to be retrieved.
@@ -81,7 +81,7 @@ p Check whether a string is in the store.
         +cell unicode
         +cell The string to check.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell bool
         +cell Whether the store contains the string.
@@ -100,7 +100,7 @@ p
     assert all_strings == [u'apple', u'orange']
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell unicode
         +cell A string in the store.
@@ -125,7 +125,7 @@ p Add a string to the #[code StringStore].
         +cell unicode
         +cell The string to add.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell uint64
         +cell The string's hash value.
@@ -166,7 +166,7 @@ p Loads state from a directory. Modifies the object in place and returns it.
             |  A path to a directory. Paths may be either strings or
             |  #[code Path]-like objects.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code StringStore]
         +cell The modified #[code StringStore] object.
@@ -185,7 +185,7 @@ p Serialize the current state to a binary string.
         +cell -
         +cell Named attributes to prevent from being serialized.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell bytes
         +cell The serialized form of the #[code StringStore] object.
@@ -211,7 +211,7 @@ p Load state from a binary string.
         +cell -
         +cell Named attributes to prevent from being loaded.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code StringStore]
         +cell The #[code StringStore] object.
@@ -233,7 +233,7 @@ p Get a 64-bit hash for a given string.
         +cell unicode
         +cell The string to hash.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell uint64
         +cell The hash.
diff --git a/website/api/tagger.jade b/website/api/tagger.jade
new file mode 100644
index 000000000..4c8ce916f
--- /dev/null
+++ b/website/api/tagger.jade
@@ -0,0 +1,5 @@
+//- 💫 DOCS > API > TAGGER
+
+include ../_includes/_mixins
+
+!=partial("pipe", { subclass: "Tagger", pipeline_id: "tagger" })
diff --git a/website/api/tensorizer.jade b/website/api/tensorizer.jade
new file mode 100644
index 000000000..b54e20514
--- /dev/null
+++ b/website/api/tensorizer.jade
@@ -0,0 +1,5 @@
+//- 💫 DOCS > API > TENSORIZER
+
+include ../_includes/_mixins
+
+!=partial("pipe", { subclass: "Tensorizer", pipeline_id: "tensorizer" })
diff --git a/website/api/textcategorizer.jade b/website/api/textcategorizer.jade
new file mode 100644
index 000000000..2d550f699
--- /dev/null
+++ b/website/api/textcategorizer.jade
@@ -0,0 +1,19 @@
+//- 💫 DOCS > API > TEXTCATEGORIZER
+
+include ../_includes/_mixins
+
+p
+    |  The model supports classification with multiple, non-mutually exclusive
+    |  labels. You can change the model architecture rather easily, but by
+    |  default, the #[code TextCategorizer] class uses a convolutional
+    |  neural network to assign position-sensitive vectors to each word in the
+    |  document. This step is similar to the #[+api("tensorizer") #[code Tensorizer]]
+    |  component, but the #[code TextCategorizer] uses its own CNN model, to
+    |  avoid sharing weights with the other pipeline components. The document
+    |  tensor is then
+    |  summarized by concatenating max and mean pooling, and a multilayer
+    |  perceptron is used to predict an output vector of length #[code nr_class],
+    |  before a logistic activation is applied elementwise. The value of each
+    |  output neuron is the probability that some class is present.
+
+!=partial("pipe", { subclass: "TextCategorizer", short: "textcat", pipeline_id: "textcat" })
diff --git a/website/docs/api/token.jade b/website/api/token.jade
similarity index 96%
rename from website/docs/api/token.jade
rename to website/api/token.jade
index db445d09b..4eebc262c 100644
--- a/website/docs/api/token.jade
+++ b/website/api/token.jade
@@ -1,6 +1,6 @@
 //- 💫 DOCS > API > TOKEN
 
-include ../../_includes/_mixins
+include ../_includes/_mixins
 
 p An individual token — i.e. a word, punctuation symbol, whitespace, etc.
 
@@ -30,7 +30,7 @@ p Construct a #[code Token] object.
         +cell int
         +cell The index of the token within the document.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Token]
         +cell The newly constructed object.
@@ -46,7 +46,7 @@ p The number of unicode characters in the token, i.e. #[code token.text].
     assert len(token) == 4
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell int
         +cell The number of unicode characters in the token.
@@ -68,7 +68,7 @@ p Check the value of a boolean flag.
         +cell int
         +cell The attribute ID of the flag to check.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell bool
         +cell Whether the flag is set.
@@ -93,7 +93,7 @@ p Compute a semantic similarity estimate. Defaults to cosine over vectors.
             |  The object to compare with. By default, accepts #[code Doc],
             |  #[code Span], #[code Token] and #[code Lexeme] objects.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell float
         +cell A scalar similarity score. Higher is more similar.
@@ -114,7 +114,7 @@ p Get a neighboring token.
         +cell int
         +cell The relative position of the token to get. Defaults to #[code 1].
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Token]
         +cell The token at position #[code self.doc[self.i+i]].
@@ -139,7 +139,7 @@ p
         +cell #[code Token]
         +cell Another token.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell bool
         +cell Whether this token is the ancestor of the descendant.
@@ -158,7 +158,7 @@ p The rightmost token of this token's syntactic descendants.
     assert [t.text for t in he_ancestors] == [u'pleaded']
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Token]
         +cell
@@ -177,7 +177,7 @@ p A sequence of coordinated tokens, including the token itself.
     assert [t.text for t in apples_conjuncts] == [u'oranges']
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Token]
         +cell A coordinated token.
@@ -194,7 +194,7 @@ p A sequence of the token's immediate syntactic children.
     assert [t.text for t in give_children] == [u'it', u'back', u'!']
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Token]
         +cell A child token such that #[code child.head==self].
@@ -211,7 +211,7 @@ p A sequence of all the token's syntactic descendents.
     assert [t.text for t in give_subtree] == [u'Give', u'it', u'back', u'!']
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Token]
         +cell A descendant token such that #[code self.is_ancestor(descendant)].
@@ -230,7 +230,7 @@ p
     assert apples.has_vector
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell bool
         +cell Whether the token has a vector data attached.
@@ -248,7 +248,7 @@ p A real-valued meaning representation.
     assert apples.vector.shape == (300,)
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
         +cell A 1D numpy array representing the token's semantics.
@@ -268,7 +268,7 @@ p The L2 norm of the token's vector representation.
     assert apples.vector_norm != pasta.vector_norm
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell float
         +cell The L2 norm of the vector representation.
@@ -280,20 +280,29 @@ p The L2 norm of the token's vector representation.
         +cell #[code text]
         +cell unicode
         +cell Verbatim text content.
+
     +row
         +cell #[code text_with_ws]
         +cell unicode
         +cell Text content, with trailing space character if present.
 
-    +row
-        +cell #[code whitespace]
-        +cell int
-        +cell Trailing space character if present.
     +row
         +cell #[code whitespace_]
         +cell unicode
         +cell Trailing space character if present.
 
+    +row
+        +cell #[code orth]
+        +cell int
+        +cell ID of the verbatim text content.
+
+    +row
+        +cell #[code orth_]
+        +cell unicode
+        +cell
+            |  Verbatim text content (identical to #[code Token.text]). Existst
+            |  mostly for consistency with the other attributes.
+
     +row
         +cell #[code vocab]
         +cell #[code Vocab]
diff --git a/website/docs/api/tokenizer.jade b/website/api/tokenizer.jade
similarity index 96%
rename from website/docs/api/tokenizer.jade
rename to website/api/tokenizer.jade
index 196f886b7..7a8a34838 100644
--- a/website/docs/api/tokenizer.jade
+++ b/website/api/tokenizer.jade
@@ -1,6 +1,6 @@
 //- 💫 DOCS > API > TOKENIZER
 
-include ../../_includes/_mixins
+include ../_includes/_mixins
 
 p
     |  Segment text, and create #[code Doc] objects with the discovered segment
@@ -57,7 +57,7 @@ p Create a #[code Tokenizer], to create #[code Doc] objects given unicode text.
         +cell callable
         +cell A boolean function matching strings to be recognised as tokens.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Tokenizer]
         +cell The newly constructed object.
@@ -77,7 +77,7 @@ p Tokenize a string.
         +cell unicode
         +cell The string to tokenize.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Doc]
         +cell A container for linguistic annotations.
@@ -110,7 +110,7 @@ p Tokenize a stream of texts.
             |  The number of threads to use, if the implementation supports
             |  multi-threading. The default tokenizer is single-threaded.
 
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Doc]
         +cell A sequence of Doc objects, in order.
@@ -126,7 +126,7 @@ p Find internal split points of the string.
         +cell unicode
         +cell The string to split.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell list
         +cell
@@ -147,7 +147,7 @@ p
         +cell unicode
         +cell The string to segment.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell int
         +cell The length of the prefix if present, otherwise #[code None].
@@ -165,7 +165,7 @@ p
         +cell unicode
         +cell The string to segment.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell int / #[code None]
         +cell The length of the suffix if present, otherwise #[code None].
@@ -176,7 +176,7 @@ p
 p
     |  Add a special-case tokenization rule. This mechanism is also used to add
     |  custom tokenizer exceptions to the language data. See the usage guide
-    |  on #[+a("/docs/usage/adding-languages#tokenizer-exceptions") adding languages]
+    |  on #[+a("/usage/adding-languages#tokenizer-exceptions") adding languages]
     |  for more details and examples.
 
 +aside-code("Example").
diff --git a/website/api/top-level.jade b/website/api/top-level.jade
new file mode 100644
index 000000000..46d2e8750
--- /dev/null
+++ b/website/api/top-level.jade
@@ -0,0 +1,24 @@
+//- 💫 DOCS > API > TOP-LEVEL
+
+include ../_includes/_mixins
+
++section("spacy")
+    //-+h(2, "spacy") spaCy
+    //- spacy/__init__.py
+    include _top-level/_spacy
+
++section("displacy")
+    +h(2, "displacy", "spacy/displacy") displaCy
+    include _top-level/_displacy
+
++section("util")
+    +h(2, "util", "spacy/util.py") Utility functions
+    include _top-level/_util
+
++section("compat")
+    +h(2, "compat", "spacy/compaty.py") Compatibility functions
+    include _top-level/_compat
+
++section("cli", "spacy/cli")
+    +h(2, "cli") Command line
+    include _top-level/_cli
diff --git a/website/api/vectors.jade b/website/api/vectors.jade
new file mode 100644
index 000000000..a58736506
--- /dev/null
+++ b/website/api/vectors.jade
@@ -0,0 +1,333 @@
+//- 💫 DOCS > API > VECTORS
+
+include ../_includes/_mixins
+
+p
+    |  Vectors data is kept in the #[code Vectors.data] attribute, which should
+    |  be an instance of #[code numpy.ndarray] (for CPU vectors) or
+    |  #[code cupy.ndarray] (for GPU vectors).
+
++h(2, "init") Vectors.__init__
+    +tag method
+
+p
+    |  Create a new vector store. To keep the vector table empty, pass
+    |  #[code data_or_width=0]. You can also create the vector table and add
+    |  vectors one by one, or set the vector values directly on initialisation.
+
++aside-code("Example").
+    from spacy.vectors import Vectors
+    from spacy.strings import StringStore
+
+    empty_vectors = Vectors(StringStore())
+
+    vectors = Vectors([u'cat'], 300)
+    vectors[u'cat'] = numpy.random.uniform(-1, 1, (300,))
+
+    vector_table = numpy.zeros((3, 300), dtype='f')
+    vectors = Vectors(StringStore(), vector_table)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code strings]
+        +cell #[code StringStore] or list
+        +cell
+            |  List of strings, or a #[+api("stringstore") #[code StringStore]]
+            |  that maps strings to hash values, and vice versa.
+
+    +row
+        +cell #[code data_or_width]
+        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']] or int
+        +cell Vector data or number of dimensions.
+
+    +row("foot")
+        +cell returns
+        +cell #[code Vectors]
+        +cell The newly created object.
+
++h(2, "getitem") Vectors.__getitem__
+    +tag method
+
+p
+    |  Get a vector by key. If key is a string, it is hashed to an integer ID
+    |  using the #[code Vectors.strings] table. If the integer key is not found
+    |  in the table, a #[code KeyError] is raised.
+
++aside-code("Example").
+    vectors = Vectors(StringStore(), 300)
+    vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
+    cat_vector = vectors[u'cat']
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code key]
+        +cell unicode / int
+        +cell The key to get the vector for.
+
+    +row
+        +cell returns
+        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
+        +cell The vector for the key.
+
++h(2, "setitem") Vectors.__setitem__
+    +tag method
+
+p
+    |  Set a vector for the given key. If key is a string, it is hashed to an
+    |  integer ID using the #[code Vectors.strings] table.
+
++aside-code("Example").
+    vectors = Vectors(StringStore(), 300)
+    vectors[u'cat'] = numpy.random.uniform(-1, 1, (300,))
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code key]
+        +cell unicode / int
+        +cell The key to set the vector for.
+
+    +row
+        +cell #[code vector]
+        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
+        +cell The vector to set.
+
++h(2, "iter") Vectors.__iter__
+    +tag method
+
+p Yield vectors from the table.
+
++aside-code("Example").
+    vector_table = numpy.zeros((3, 300), dtype='f')
+    vectors = Vectors(StringStore(), vector_table)
+    for vector in vectors:
+        print(vector)
+
++table(["Name", "Type", "Description"])
+    +row("foot")
+        +cell yields
+        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
+        +cell A vector from the table.
+
++h(2, "len") Vectors.__len__
+    +tag method
+
+p Return the number of vectors that have been assigned.
+
++aside-code("Example").
+    vector_table = numpy.zeros((3, 300), dtype='f')
+    vectors = Vectors(StringStore(), vector_table)
+    assert len(vectors) == 3
+
++table(["Name", "Type", "Description"])
+    +row("foot")
+        +cell returns
+        +cell int
+        +cell The number of vectors in the data.
+
++h(2, "contains") Vectors.__contains__
+    +tag method
+
+p
+    |  Check whether a key has a vector entry in the table. If key is a string,
+    |  it is hashed to an integer ID using the #[code Vectors.strings] table.
+
++aside-code("Example").
+    vectors = Vectors(StringStore(), 300)
+    vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
+    assert u'cat' in vectors
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code key]
+        +cell unicode / int
+        +cell The key to check.
+
+    +row("foot")
+        +cell returns
+        +cell bool
+        +cell Whether the key has a vector entry.
+
++h(2, "add") Vectors.add
+    +tag method
+
+p
+    |  Add a key to the table, optionally setting a vector value as well. If
+    |  key is a string, it is hashed to an integer ID using the
+    |  #[code Vectors.strings] table.
+
++aside-code("Example").
+    vectors = Vectors(StringStore(), 300)
+    vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code key]
+        +cell unicode / int
+        +cell The key to add.
+
+    +row
+        +cell #[code vector]
+        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
+        +cell An optional vector to add.
+
++h(2, "items") Vectors.items
+    +tag method
+
+p Iterate over #[code (string key, vector)] pairs, in order.
+
++aside-code("Example").
+    vectors = Vectors(StringStore(), 300)
+    vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
+    for key, vector in vectors.items():
+        print(key, vector)
+
++table(["Name", "Type", "Description"])
+    +row("foot")
+        +cell yields
+        +cell tuple
+        +cell #[code (string key, vector)] pairs, in order.
+
++h(2, "shape") Vectors.shape
+    +tag property
+
+p
+    |  Get #[code (rows, dims)] tuples of number of rows and number of
+    |  dimensions in the vector table.
+
++aside-code("Example").
+    vectors = Vectors(StringStore(), 300)
+    vectors.add(u'cat', numpy.random.uniform(-1, 1, (300,)))
+    rows, dims = vectors.shape
+    assert rows == 1
+    assert dims == 300
+
++table(["Name", "Type", "Description"])
+    +row("foot")
+        +cell returns
+        +cell tuple
+        +cell #[code (rows, dims)] pairs.
+
++h(2, "from_glove") Vectors.from_glove
+    +tag method
+
+p
+    |  Load #[+a("https://nlp.stanford.edu/projects/glove/") GloVe] vectors from
+    |  a directory. Assumes binary format, that the vocab is in a
+    |  #[code vocab.txt], and that vectors are named
+    |  #[code vectors.{size}.[fd].bin], e.g. #[code vectors.128.f.bin] for 128d
+    |  float32 vectors, #[code vectors.300.d.bin] for 300d float64 (double)
+    |  vectors, etc. By default GloVe outputs 64-bit vectors.
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code path]
+        +cell unicode / #[code Path]
+        +cell The path to load the GloVe vectors from.
+
++h(2, "to_disk") Vectors.to_disk
+    +tag method
+
+p Save the current state to a directory.
+
++aside-code("Example").
+    vectors.to_disk('/path/to/vectors')
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code path]
+        +cell unicode or #[code Path]
+        +cell
+            |  A path to a directory, which will be created if it doesn't exist.
+            |  Paths may be either strings or #[code Path]-like objects.
+
++h(2, "from_disk") Vectors.from_disk
+    +tag method
+
+p Loads state from a directory. Modifies the object in place and returns it.
+
++aside-code("Example").
+    vectors = Vectors(StringStore())
+    vectors.from_disk('/path/to/vectors')
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code path]
+        +cell unicode or #[code Path]
+        +cell
+            |  A path to a directory. Paths may be either strings or
+            |  #[code Path]-like objects.
+
+    +row("foot")
+        +cell returns
+        +cell #[code Vectors]
+        +cell The modified #[code Vectors] object.
+
++h(2, "to_bytes") Vectors.to_bytes
+    +tag method
+
+p Serialize the current state to a binary string.
+
++aside-code("Example").
+    vectors_bytes = vectors.to_bytes()
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code **exclude]
+        +cell -
+        +cell Named attributes to prevent from being serialized.
+
+    +row("foot")
+        +cell returns
+        +cell bytes
+        +cell The serialized form of the #[code Vectors] object.
+
++h(2, "from_bytes") Vectors.from_bytes
+    +tag method
+
+p Load state from a binary string.
+
++aside-code("Example").
+    fron spacy.vectors import Vectors
+    vectors_bytes = vectors.to_bytes()
+    new_vectors = Vectors(StringStore())
+    new_vectors.from_bytes(vectors_bytes)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code bytes_data]
+        +cell bytes
+        +cell The data to load from.
+
+    +row
+        +cell #[code **exclude]
+        +cell -
+        +cell Named attributes to prevent from being loaded.
+
+    +row("foot")
+        +cell returns
+        +cell #[code Vectors]
+        +cell The #[code Vectors] object.
+
++h(2, "attributes") Attributes
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code data]
+        +cell #[code numpy.ndarray] / #[code cupy.ndarray]
+        +cell
+            |  Stored vectors data. #[code numpy] is used for CPU vectors,
+            |  #[code cupy] for GPU vectors.
+
+    +row
+        +cell #[code key2row]
+        +cell dict
+        +cell
+            |  Dictionary mapping word hashes to rows in the
+            |  #[code Vectors.data] table.
+
+    +row
+        +cell #[code keys]
+        +cell #[code numpy.ndarray]
+        +cell
+            |  Array keeping the keys in order, such that
+            |  #[code keys[vectors.key2row[key]] == key]
diff --git a/website/docs/api/vocab.jade b/website/api/vocab.jade
similarity index 66%
rename from website/docs/api/vocab.jade
rename to website/api/vocab.jade
index 4d3e0828a..6faefc064 100644
--- a/website/docs/api/vocab.jade
+++ b/website/api/vocab.jade
@@ -1,17 +1,22 @@
 //- 💫 DOCS > API > VOCAB
 
-include ../../_includes/_mixins
+include ../_includes/_mixins
 
 p
-    |  A lookup table that allows you to access #[code Lexeme] objects. The
-    |  #[code Vocab] instance also provides access to the #[code StringStore],
-    |  and owns underlying C-data that is shared between #[code Doc] objects.
+    |  The #[code Vocab] object provides a lookup table that allows you to
+    |  access #[+api("lexeme") #[code Lexeme]] objects, as well as the
+    |  #[+api("stringstore") #[code StringStore]]. It also owns underlying
+    |  C-data that is shared between #[code Doc] objects.
 
 +h(2, "init") Vocab.__init__
     +tag method
 
 p Create the vocabulary.
 
++aside-code("Example").
+    from spacy.vocab import Vocab
+    vocab = Vocab(strings=[u'hello', u'world'])
+
 +table(["Name", "Type", "Description"])
     +row
         +cell #[code lex_attr_getters]
@@ -39,7 +44,7 @@ p Create the vocabulary.
             |  A #[+api("stringstore") #[code StringStore]] that maps
             |  strings to hash values, and vice versa, or a list of strings.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Vocab]
         +cell The newly constructed object.
@@ -54,7 +59,7 @@ p Get the current number of lexemes in the vocabulary.
     assert len(nlp.vocab) > 0
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell returns
         +cell int
         +cell The number of lexems in the vocabulary.
@@ -76,7 +81,7 @@ p
         +cell int / unicode
         +cell The hash value of a word, or its unicode string.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Lexeme]
         +cell The lexeme indicated by the given ID.
@@ -90,7 +95,7 @@ p Iterate over the lexemes in the vocabulary.
     stop_words = (lex for lex in nlp.vocab if lex.is_stop)
 
 +table(["Name", "Type", "Description"])
-    +footrow
+    +row("foot")
         +cell yields
         +cell #[code Lexeme]
         +cell An entry in the vocabulary.
@@ -115,7 +120,7 @@ p
         +cell unicode
         +cell The ID string.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell bool
         +cell Whether the string has an entry in the vocabulary.
@@ -152,11 +157,100 @@ p
             |  which the flag will be stored. If #[code -1], the lowest
             |  available bit will be chosen.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell int
         +cell The integer ID by which the flag value can be checked.
 
++h(2, "add_flag") Vocab.clear_vectors
+    +tag method
+    +tag-new(2)
+
+p
+    |  Drop the current vector table. Because all vectors must be the same
+    |  width, you have to call this to change the size of the vectors.
+
++aside-code("Example").
+    nlp.vocab.clear_vectors(new_dim=300)
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code new_dim]
+        +cell int
+        +cell
+            |  Number of dimensions of the new vectors. If #[code None], size
+            |  is not changed.
+
++h(2, "add_flag") Vocab.get_vector
+    +tag method
+    +tag-new(2)
+
+p
+    |  Retrieve a vector for a word in the vocabulary. Words can be looked up
+    |  by string or hash value. If no vectors data is loaded, a
+    |  #[code ValueError] is raised.
+
++aside-code("Example").
+    nlp.vocab.get_vector(u'apple')
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code orth]
+        +cell int / unicode
+        +cell The hash value of a word, or its unicode string.
+
+    +row("foot")
+        +cell returns
+        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
+        +cell
+            |  A word vector. Size and shape are determined by the
+            |  #[code Vocab.vectors] instance.
+
++h(2, "add_flag") Vocab.set_vector
+    +tag method
+    +tag-new(2)
+
+p
+    |  Set a vector for a word in the vocabulary. Words can be referenced by
+    |  by string or hash value.
+
++aside-code("Example").
+    nlp.vocab.set_vector(u'apple', array([...]))
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code orth]
+        +cell int / unicode
+        +cell The hash value of a word, or its unicode string.
+
+    +row
+        +cell #[code vector]
+        +cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
+        +cell The vector to set.
+
++h(2, "add_flag") Vocab.has_vector
+    +tag method
+    +tag-new(2)
+
+p
+    |  Check whether a word has a vector. Returns #[code False] if no vectors
+    |  are loaded. Words can be looked up by string or hash value.
+
++aside-code("Example").
+    if nlp.vocab.has_vector(u'apple'):
+        vector = nlp.vocab.get_vector(u'apple')
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code orth]
+        +cell int / unicode
+        +cell The hash value of a word, or its unicode string.
+
+    +row("foot")
+        +cell returns
+        +cell bool
+        +cell Whether the word has a vector.
+
 +h(2, "to_disk") Vocab.to_disk
     +tag method
     +tag-new(2)
@@ -192,7 +286,7 @@ p Loads state from a directory. Modifies the object in place and returns it.
             |  A path to a directory. Paths may be either strings or
             |  #[code Path]-like objects.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Vocab]
         +cell The modified #[code Vocab] object.
@@ -211,7 +305,7 @@ p Serialize the current state to a binary string.
         +cell -
         +cell Named attributes to prevent from being serialized.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell bytes
         +cell The serialized form of the #[code Vocab] object.
@@ -238,7 +332,7 @@ p Load state from a binary string.
         +cell -
         +cell Named attributes to prevent from being loaded.
 
-    +footrow
+    +row("foot")
         +cell returns
         +cell #[code Vocab]
         +cell The #[code Vocab] object.
@@ -256,3 +350,14 @@ p Load state from a binary string.
         +cell #[code strings]
         +cell #[code StringStore]
         +cell A table managing the string-to-int mapping.
+
+    +row
+        +cell #[code vectors]
+            +tag-new(2)
+        +cell #[code Vectors]
+        +cell A table associating word IDs to word vectors.
+
+    +row
+        +cell #[code vectors_length]
+        +cell int
+        +cell Number of dimensions for each word vector.
diff --git a/website/docs/api/annotation.jade b/website/docs/api/annotation.jade
deleted file mode 100644
index ce18878b7..000000000
--- a/website/docs/api/annotation.jade
+++ /dev/null
@@ -1,156 +0,0 @@
-//- 💫 DOCS > API > ANNOTATION SPECS
-
-include ../../_includes/_mixins
-
-p This document describes the target annotations spaCy is trained to predict.
-
-+h(2, "tokenization") Tokenization
-
-p
-    |  Tokenization standards are based on the
-    |  #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] corpus.
-    |  The tokenizer differs from most by including tokens for significant
-    |  whitespace. Any sequence of whitespace characters beyond a single space
-    |  (#[code ' ']) is included as a token.
-
-+aside-code("Example").
-    from spacy.lang.en import English
-    nlp = English()
-    tokens = nlp('Some\nspaces  and\ttab characters')
-    tokens_text = [t.text for t in tokens]
-    assert tokens_text == ['Some', '\n', 'spaces', ' ', 'and',
-                           '\t', 'tab', 'characters']
-
-p
-    |  The whitespace tokens are useful for much the same reason punctuation is
-    |  – it's often an important delimiter in the text. By preserving it in the
-    |  token output, we are able to maintain a simple alignment between the
-    |  tokens and the original string, and we ensure that no information is
-    |  lost during processing.
-
-+h(2, "sentence-boundary") Sentence boundary detection
-
-p
-    |  Sentence boundaries are calculated from the syntactic parse tree, so
-    |  features such as punctuation and capitalisation play an important but
-    |  non-decisive role in determining the sentence boundaries. Usually this
-    |  means that the sentence boundaries will at least coincide with clause
-    |  boundaries, even given poorly punctuated text.
-
-+h(2, "pos-tagging") Part-of-speech Tagging
-
-+aside("Tip: Understanding tags")
-    |  You can also use #[code spacy.explain()] to get the description for the
-    |  string representation of a tag. For example,
-    |  #[code spacy.explain("RB")] will return "adverb".
-
-include _annotation/_pos-tags
-
-+h(2, "lemmatization") Lemmatization
-
-p A "lemma" is the uninflected form of a word. In English, this means:
-
-+list
-    +item #[strong Adjectives]: The form like "happy", not "happier" or "happiest"
-    +item #[strong Adverbs]: The form like "badly", not "worse" or "worst"
-    +item #[strong Nouns]: The form like "dog", not "dogs"; like "child", not "children"
-    +item #[strong Verbs]: The form like "write", not "writes", "writing", "wrote" or "written"
-
-p
-    |  The lemmatization data is taken from
-    |  #[+a("https://wordnet.princeton.edu") WordNet]. However, we also add a
-    |  special case for pronouns: all pronouns are lemmatized to the special
-    |  token #[code -PRON-].
-
-+infobox("About spaCy's custom pronoun lemma")
-    |  Unlike verbs and common nouns, there's no clear base form of a personal
-    |  pronoun. Should the lemma of "me" be "I", or should we normalize person
-    |  as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
-    |  novel symbol, #[code -PRON-], which is used as the lemma for
-    |  all personal pronouns.
-
-+h(2, "dependency-parsing") Syntactic Dependency Parsing
-
-+aside("Tip: Understanding labels")
-    |  You can also use #[code spacy.explain()] to get the description for the
-    |  string representation of a label. For example,
-    |  #[code spacy.explain("prt")] will return "particle".
-
-include _annotation/_dep-labels
-
-+h(2, "named-entities") Named Entity Recognition
-
-+aside("Tip: Understanding entity types")
-    |  You can also use #[code spacy.explain()] to get the description for the
-    |  string representation of an entity label. For example,
-    |  #[code spacy.explain("LANGUAGE")] will return "any named language".
-
-include _annotation/_named-entities
-
-+h(3, "biluo") BILUO Scheme
-
-p
-    |  spaCy translates character offsets into the BILUO scheme, in order to
-    |  decide the cost of each action given the current state of the entity
-    |  recognizer. The costs are then used to calculate the gradient of the
-    |  loss, to train the model.
-
-+aside("Why BILUO, not IOB?")
-    |  There are several coding schemes for encoding entity annotations as
-    |  token tags.  These coding schemes are equally expressive, but not
-    |  necessarily equally learnable.
-    |  #[+a("http://www.aclweb.org/anthology/W09-1119") Ratinov and Roth]
-    |  showed that the minimal #[strong Begin], #[strong In], #[strong Out]
-    |  scheme was more difficult to learn than the #[strong BILUO] scheme that
-    |  we use, which explicitly marks boundary tokens.
-
-+table([ "Tag", "Description" ])
-    +row
-        +cell #[code #[span.u-color-theme B] EGIN]
-        +cell The first token of a multi-token entity.
-
-    +row
-        +cell #[code #[span.u-color-theme I] N]
-        +cell An inner token of a multi-token entity.
-
-    +row
-        +cell #[code #[span.u-color-theme L] AST]
-        +cell The final token of a multi-token entity.
-
-    +row
-        +cell #[code #[span.u-color-theme U] NIT]
-        +cell A single-token entity.
-
-    +row
-        +cell #[code #[span.u-color-theme O] UT]
-        +cell A non-entity token.
-
-+h(2, "json-input") JSON input format for training
-
-p
-    |  spaCy takes training data in the following format:
-
-+code("Example structure").
-    doc: {
-        id: string,
-        paragraphs: [{
-            raw: string,
-            sents: [int],
-            tokens: [{
-                start: int,
-                tag: string,
-                head: int,
-                dep: string
-            }],
-            ner: [{
-                start: int,
-                end: int,
-                label: string
-            }],
-            brackets: [{
-                start: int,
-                end: int,
-                label: string
-            }]
-        }]
-    }
diff --git a/website/docs/api/dependencyparser.jade b/website/docs/api/dependencyparser.jade
deleted file mode 100644
index a1a7e0b36..000000000
--- a/website/docs/api/dependencyparser.jade
+++ /dev/null
@@ -1,111 +0,0 @@
-//- 💫 DOCS > API > DEPENDENCYPARSER
-
-include ../../_includes/_mixins
-
-p Annotate syntactic dependencies on #[code Doc] objects.
-
-+under-construction
-
-+h(2, "init") DependencyParser.__init__
-    +tag method
-
-p Create a #[code DependencyParser].
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell The vocabulary. Must be shared with documents to be processed.
-
-    +row
-        +cell #[code model]
-        +cell #[thinc.linear.AveragedPerceptron]
-        +cell The statistical model.
-
-    +footrow
-        +cell returns
-        +cell #[code DependencyParser]
-        +cell The newly constructed object.
-
-+h(2, "call") DependencyParser.__call__
-    +tag method
-
-p
-    |  Apply the dependency parser, setting the heads and dependency relations
-    |  onto the #[code Doc] object.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The document to be processed.
-
-    +footrow
-        +cell returns
-        +cell #[code None]
-        +cell -
-
-+h(2, "pipe") DependencyParser.pipe
-    +tag method
-
-p Process a stream of documents.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code stream]
-        +cell -
-        +cell The sequence of documents to process.
-
-    +row
-        +cell #[code batch_size]
-        +cell int
-        +cell The number of documents to accumulate into a working set.
-
-    +row
-        +cell #[code n_threads]
-        +cell int
-        +cell
-            |  The number of threads with which to work on the buffer in
-            |  parallel.
-
-    +footrow
-        +cell yields
-        +cell #[code Doc]
-        +cell Documents, in order.
-
-+h(2, "update") DependencyParser.update
-    +tag method
-
-p Update the statistical model.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The example document for the update.
-
-    +row
-        +cell #[code gold]
-        +cell #[code GoldParse]
-        +cell The gold-standard annotations, to calculate the loss.
-
-    +footrow
-        +cell returns
-        +cell int
-        +cell The loss on this example.
-
-+h(2, "step_through") DependencyParser.step_through
-    +tag method
-
-p Set up a stepwise state, to introspect and control the transition sequence.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The document to step through.
-
-    +footrow
-        +cell returns
-        +cell #[code StepwiseState]
-        +cell A state object, to step through the annotation process.
diff --git a/website/docs/api/entityrecognizer.jade b/website/docs/api/entityrecognizer.jade
deleted file mode 100644
index e3775b7f4..000000000
--- a/website/docs/api/entityrecognizer.jade
+++ /dev/null
@@ -1,109 +0,0 @@
-//- 💫 DOCS > API > ENTITYRECOGNIZER
-
-include ../../_includes/_mixins
-
-p Annotate named entities on #[code Doc] objects.
-
-+under-construction
-
-+h(2, "init") EntityRecognizer.__init__
-    +tag method
-
-p Create an #[code EntityRecognizer].
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell The vocabulary. Must be shared with documents to be processed.
-
-    +row
-        +cell #[code model]
-        +cell #[thinc.linear.AveragedPerceptron]
-        +cell The statistical model.
-
-    +footrow
-        +cell returns
-        +cell #[code EntityRecognizer]
-        +cell The newly constructed object.
-
-+h(2, "call") EntityRecognizer.__call__
-    +tag method
-
-p Apply the entity recognizer, setting the NER tags onto the #[code Doc] object.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The document to be processed.
-
-    +footrow
-        +cell returns
-        +cell #[code None]
-        +cell -
-
-+h(2, "pipe") EntityRecognizer.pipe
-    +tag method
-
-p Process a stream of documents.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code stream]
-        +cell -
-        +cell The sequence of documents to process.
-
-    +row
-        +cell #[code batch_size]
-        +cell int
-        +cell The number of documents to accumulate into a working set.
-
-    +row
-        +cell #[code n_threads]
-        +cell int
-        +cell
-            |  The number of threads with which to work on the buffer in
-            |  parallel.
-
-    +footrow
-        +cell yields
-        +cell #[code Doc]
-        +cell Documents, in order.
-
-+h(2, "update") EntityRecognizer.update
-    +tag method
-
-p Update the statistical model.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The example document for the update.
-
-    +row
-        +cell #[code gold]
-        +cell #[code GoldParse]
-        +cell The gold-standard annotations, to calculate the loss.
-
-    +footrow
-        +cell returns
-        +cell int
-        +cell The loss on this example.
-
-+h(2, "step_through") EntityRecognizer.step_through
-    +tag method
-
-p Set up a stepwise state, to introspect and control the transition sequence.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The document to step through.
-
-    +footrow
-        +cell returns
-        +cell #[code StepwiseState]
-        +cell A state object, to step through the annotation process.
diff --git a/website/docs/api/index.jade b/website/docs/api/index.jade
deleted file mode 100644
index f92080975..000000000
--- a/website/docs/api/index.jade
+++ /dev/null
@@ -1,241 +0,0 @@
-//- 💫 DOCS > API > FACTS & FIGURES
-
-include ../../_includes/_mixins
-
-+under-construction
-
-+h(2, "comparison") Feature comparison
-
-p
-    |  Here's a quick comparison of the functionalities offered by spaCy,
-    |  #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet],
-    |  #[+a("http://www.nltk.org/py-modindex.html") NLTK] and
-    |  #[+a("http://stanfordnlp.github.io/CoreNLP/") CoreNLP].
-
-+table([ "", "spaCy", "SyntaxNet", "NLTK", "CoreNLP"])
-    +row
-        +cell Easy installation
-        each icon in [ "pro", "con", "pro", "pro" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell Python API
-        each icon in [ "pro", "con", "pro", "con" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell Multi-language support
-        each icon in [ "neutral", "pro", "pro", "pro" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell Tokenization
-        each icon in [ "pro", "pro", "pro", "pro" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell Part-of-speech tagging
-        each icon in [ "pro", "pro", "pro", "pro" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell Sentence segmentation
-        each icon in [ "pro", "pro", "pro", "pro" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell Dependency parsing
-        each icon in [ "pro", "pro", "con", "pro" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell Entity Recognition
-        each icon in [ "pro", "con", "pro", "pro" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell Integrated word vectors
-        each icon in [ "pro", "con", "con", "con" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell Sentiment analysis
-        each icon in [ "pro", "con", "pro", "pro" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell Coreference resolution
-        each icon in [ "con", "con", "con", "pro" ]
-            +cell.u-text-center #[+procon(icon)]
-
-+h(2, "benchmarks") Benchmarks
-
-p
-    |  Two peer-reviewed papers in 2015 confirm that spaCy offers the
-    |  #[strong fastest syntactic parser in the world] and that
-    |  #[strong its accuracy is within 1% of the best] available. The few
-    |  systems that are more accurate are 20&times; slower or more.
-
-+aside("About the evaluation")
-    |  The first of the evaluations was published by #[strong Yahoo! Labs] and
-    |  #[strong Emory University], as part of a survey of current parsing
-    |  technologies #[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") (Choi et al., 2015)].
-    |  Their results and subsequent discussions helped us develop a novel
-    |  psychologically-motivated technique to improve spaCy's accuracy, which
-    |  we published in joint work with Macquarie University
-    |  #[+a("https://aclweb.org/anthology/D/D15/D15-1162.pdf") (Honnibal and Johnson, 2015)].
-
-+table([ "System", "Language", "Accuracy", "Speed (wps)"])
-    +row
-        each data in [ "spaCy", "Cython", "91.8", "13,963" ]
-            +cell #[strong=data]
-    +row
-        each data in [ "ClearNLP", "Java", "91.7", "10,271" ]
-            +cell=data
-
-    +row
-        each data in [ "CoreNLP", "Java", "89.6", "8,602"]
-            +cell=data
-
-    +row
-        each data in [ "MATE", "Java", "92.5", "550"]
-            +cell=data
-
-    +row
-        each data in [ "Turbo", "C++", "92.4", "349" ]
-            +cell=data
-
-+h(3, "parse-accuracy") Parse accuracy
-
-p
-    |  In 2016, Google released their
-    |  #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet]
-    |  library, setting a new state of the art for syntactic dependency parsing
-    |  accuracy. SyntaxNet's algorithm is very similar to spaCy's. The main
-    |  difference is that SyntaxNet uses a neural network while spaCy uses a
-    |  sparse linear model.
-
-+aside("Methodology")
-    |  #[+a("http://arxiv.org/abs/1603.06042") Andor et al. (2016)] chose
-    |  slightly different experimental conditions from
-    |  #[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") Choi et al. (2015)],
-    |  so the two accuracy tables here do not present directly comparable
-    |  figures. We have only evaluated spaCy in the "News" condition following
-    |  the SyntaxNet methodology. We don't yet have benchmark figures for the
-    |  "Web" and "Questions" conditions.
-
-+table([ "System", "News", "Web", "Questions" ])
-    +row
-        +cell spaCy
-        each data in [ 92.8, "n/a", "n/a" ]
-            +cell=data
-
-    +row
-        +cell #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") Parsey McParseface]
-        each data in [ 94.15, 89.08, 94.77 ]
-            +cell=data
-
-    +row
-        +cell #[+a("http://www.cs.cmu.edu/~ark/TurboParser/") Martins et al. (2013)]
-        each data in [ 93.10, 88.23, 94.21 ]
-            +cell=data
-
-    +row
-        +cell #[+a("http://research.google.com/pubs/archive/38148.pdf") Zhang and McDonald (2014)]
-        each data in [ 93.32, 88.65, 93.37 ]
-            +cell=data
-
-    +row
-        +cell #[+a("http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43800.pdf") Weiss et al. (2015)]
-        each data in [ 93.91, 89.29, 94.17 ]
-            +cell=data
-
-    +row
-        +cell #[strong #[+a("http://arxiv.org/abs/1603.06042") Andor et al. (2016)]]
-        each data in [ 94.44, 90.17, 95.40 ]
-            +cell #[strong=data]
-
-+h(3, "speed-comparison") Detailed speed comparison
-
-p
-    |  Here we compare the per-document processing time of various spaCy
-    |  functionalities against other NLP libraries. We show both absolute
-    |  timings (in ms) and relative performance (normalized to spaCy). Lower is
-    |  better.
-
-+aside("Methodology")
-    |  #[strong Set up:] 100,000 plain-text documents were streamed from an
-    |  SQLite3 database, and processed with an NLP library, to one of three
-    |  levels of detail — tokenization, tagging, or parsing. The tasks are
-    |  additive: to parse the text you have to tokenize and tag it. The
-    |  pre-processing was not subtracted from the times — I report the time
-    |  required for the pipeline to complete. I report mean times per document,
-    |  in milliseconds.#[br]#[br]
-    |  #[strong Hardware]: Intel i7-3770 (2012)#[br]
-    |  #[strong Implementation]: #[+src(gh("spacy-benchmarks")) spacy-benchmarks]
-
-+table
-    +row.u-text-label.u-text-center
-        th.c-table__head-cell
-        th.c-table__head-cell(colspan="3") Absolute (ms per doc)
-        th.c-table__head-cell(colspan="3") Relative (to spaCy)
-
-    +row
-        each column in ["System", "Tokenize", "Tag", "Parse", "Tokenize", "Tag", "Parse"]
-            th.c-table__head-cell.u-text-label=column
-
-    +row
-        +cell #[strong spaCy]
-        each data in [ "0.2ms", "1ms", "19ms"]
-            +cell #[strong=data]
-
-        each data in [ "1x", "1x", "1x" ]
-            +cell=data
-
-    +row
-        each data in [ "CoreNLP", "2ms", "10ms", "49ms", "10x", "10x", "2.6x"]
-            +cell=data
-    +row
-        each data in [ "ZPar", "1ms", "8ms", "850ms", "5x", "8x", "44.7x" ]
-            +cell=data
-    +row
-        each data in [ "NLTK", "4ms", "443ms", "n/a", "20x", "443x", "n/a" ]
-            +cell=data
-
-+h(3, "ner") Named entity comparison
-
-p
-    |  #[+a("https://aclweb.org/anthology/W/W16/W16-2703.pdf") Jiang et al. (2016)]
-    |  present several detailed comparisons of the named entity recognition
-    |  models provided by spaCy, CoreNLP, NLTK and LingPipe. Here we show their
-    |  evaluation of person, location and organization accuracy on Wikipedia.
-
-+aside("Methodology")
-    |  Making a meaningful comparison of different named entity recognition
-    |  systems is tricky.  Systems are often trained on different data, which
-    |  usually have slight differences in annotation style. For instance, some
-    |  corpora include titles as part of person names, while others don't.
-    |  These trivial differences in convention can distort comparisons
-    |  significantly. Jiang et al.'s #[em partial overlap] metric goes a long
-    |  way to solving this problem.
-
-+table([ "System", "Precision", "Recall", "F-measure" ])
-    +row
-        +cell spaCy
-        each data in [ 0.7240, 0.6514, 0.6858 ]
-            +cell=data
-
-    +row
-        +cell #[strong CoreNLP]
-        each data in [ 0.7914, 0.7327, 0.7609 ]
-            +cell #[strong=data]
-
-    +row
-        +cell NLTK
-        each data in [ 0.5136, 0.6532, 0.5750 ]
-            +cell=data
-
-    +row
-        +cell LingPipe
-        each data in [ 0.5412, 0.5357, 0.5384 ]
-            +cell=data
diff --git a/website/docs/api/language-models.jade b/website/docs/api/language-models.jade
deleted file mode 100644
index c6943b410..000000000
--- a/website/docs/api/language-models.jade
+++ /dev/null
@@ -1,93 +0,0 @@
-//- 💫 DOCS > API > LANGUAGE MODELS
-
-include ../../_includes/_mixins
-
-p
-    |  spaCy currently provides models for the following languages and
-    |  capabilities:
-
-
-+aside-code("Download language models", "bash").
-    spacy download en
-    spacy download de
-    spacy download fr
-
-+table([ "Language", "Token", "SBD", "Lemma", "POS", "NER", "Dep", "Vector", "Sentiment"])
-    +row
-        +cell English #[code en]
-        each icon in [ "pro", "pro", "pro", "pro", "pro", "pro", "pro", "con" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell German #[code de]
-        each icon in [ "pro", "pro", "con", "pro", "pro", "pro", "pro", "con" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell French #[code fr]
-        each icon in [ "pro", "con", "con", "pro", "con", "pro", "pro", "con" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell Spanish #[code es]
-        each icon in [ "pro", "pro", "con", "pro", "pro", "pro", "pro", "con" ]
-            +cell.u-text-center #[+procon(icon)]
-
-p
-    +button("/docs/usage/models", true, "primary") See available models
-
-+h(2, "alpha-support") Alpha tokenization support
-
-p
-    |  Work has started on the following languages. You can help by
-    |  #[+a("/docs/usage/adding-languages#language-data") improving the existing language data]
-    |  and extending the tokenization patterns.
-
-+aside("Usage note")
-    |  Note that the alpha languages don't yet come with a language model. In
-    |  order to use them, you have to import them directly:
-
-    +code.o-no-block.
-        from spacy.lang.fi import Finnish
-        nlp = Finnish()
-        doc = nlp(u'Ilmatyynyalukseni on täynnä ankeriaita')
-
-+infobox("Dependencies")
-    |  Some language tokenizers require external dependencies. To use #[strong Chinese],
-    |  you need to have #[+a("https://github.com/fxsjy/jieba") Jieba] installed.
-    |  The #[strong Japanese] tokenizer requires
-    |  #[+a("https://github.com/mocobeta/janome") Janome].
-
-+table([ "Language", "Code", "Source" ])
-    each language, code in { it: "Italian", pt: "Portuguese", nl: "Dutch", sv: "Swedish", fi: "Finnish", nb: "Norwegian Bokmål", da: "Danish", hu: "Hungarian", pl: "Polish", bn: "Bengali", he: "Hebrew", zh: "Chinese", ja: "Japanese" }
-        +row
-            +cell #{language}
-            +cell #[code=code]
-            +cell
-                +src(gh("spaCy", "spacy/lang/" + code)) lang/#{code}
-
-+h(2, "multi-language") Multi-language support
-    +tag-new(2)
-
-p
-    |  As of v2.0, spaCy supports models trained on more than one language. This
-    |  is especially useful for named entity recognition. The language ID used
-    |  for multi-language or language-neutral models is #[code xx]. The
-    |  language class, a generic subclass containing only the base language data,
-    |  can be found in #[+src(gh("spaCy", "spacy/lang/xx")) lang/xx].
-
-p
-    |  To load your model with the neutral, multi-language class, simply set
-    |  #[code "language": "xx"] in your
-    |  #[+a("/docs/usage/saving-loading#models-generating") model package]'s
-    |  meta.json. You can also import the class directly, or call
-    |  #[+api("util#get_lang_class") #[code util.get_lang_class()]] for
-    |  lazy-loading.
-
-+code("Standard import").
-    from spacy.lang.xx import MultiLanguage
-    nlp = MultiLanguage()
-
-+code("With lazy-loading").
-    from spacy.util import get_lang_class
-    nlp = get_lang_class('xx')
diff --git a/website/docs/api/tagger.jade b/website/docs/api/tagger.jade
deleted file mode 100644
index c41de6a4e..000000000
--- a/website/docs/api/tagger.jade
+++ /dev/null
@@ -1,93 +0,0 @@
-//- 💫 DOCS > API > TAGGER
-
-include ../../_includes/_mixins
-
-p Annotate part-of-speech tags on #[code Doc] objects.
-
-+under-construction
-
-+h(2, "init") Tagger.__init__
-    +tag method
-
-p Create a #[code Tagger].
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code vocab]
-        +cell #[code Vocab]
-        +cell The vocabulary. Must be shared with documents to be processed.
-
-    +row
-        +cell #[code model]
-        +cell #[thinc.linear.AveragedPerceptron]
-        +cell The statistical model.
-
-    +footrow
-        +cell returns
-        +cell #[code Tagger]
-        +cell The newly constructed object.
-
-+h(2, "call") Tagger.__call__
-    +tag method
-
-p Apply the tagger, setting the POS tags onto the #[code Doc] object.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The tokens to be tagged.
-
-    +footrow
-        +cell returns
-        +cell #[code None]
-        +cell -
-
-+h(2, "pipe") Tagger.pipe
-    +tag method
-
-p Tag a stream of documents.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code stream]
-        +cell -
-        +cell The sequence of documents to tag.
-
-    +row
-        +cell #[code batch_size]
-        +cell int
-        +cell The number of documents to accumulate into a working set.
-
-    +row
-        +cell #[code n_threads]
-        +cell int
-        +cell
-            |  The number of threads with which to work on the buffer in
-            |  parallel.
-
-    +footrow
-        +cell yields
-        +cell #[code Doc]
-        +cell Documents, in order.
-
-+h(2, "update") Tagger.update
-    +tag method
-
-p Update the statistical model, with tags supplied for the given document.
-
-+table(["Name", "Type", "Description"])
-    +row
-        +cell #[code doc]
-        +cell #[code Doc]
-        +cell The example document for the update.
-
-    +row
-        +cell #[code gold]
-        +cell #[code GoldParse]
-        +cell Manager for the gold-standard tags.
-
-    +footrow
-        +cell returns
-        +cell int
-        +cell Number of tags predicted correctly.
diff --git a/website/docs/api/tensorizer.jade b/website/docs/api/tensorizer.jade
deleted file mode 100644
index 9abd6793b..000000000
--- a/website/docs/api/tensorizer.jade
+++ /dev/null
@@ -1,7 +0,0 @@
-//- 💫 DOCS > API > TENSORIZER
-
-include ../../_includes/_mixins
-
-p Add a tensor with position-sensitive meaning representations to a #[code Doc].
-
-+under-construction
diff --git a/website/docs/api/textcategorizer.jade b/website/docs/api/textcategorizer.jade
deleted file mode 100644
index 926d957f7..000000000
--- a/website/docs/api/textcategorizer.jade
+++ /dev/null
@@ -1,21 +0,0 @@
-//- 💫 DOCS > API > TEXTCATEGORIZER
-
-include ../../_includes/_mixins
-
-p
-    |  Add text categorization models to spaCy pipelines. The model supports
-    |  classification with multiple, non-mutually exclusive labels.
-
-p
-    |  You can change the model architecture rather easily, but by default, the
-    |  #[code TextCategorizer] class uses a convolutional neural network to
-    |  assign position-sensitive vectors to each word in the document. This step
-    |  is similar to the #[+api("tensorizer") #[code Tensorizer]] component, but the
-    |  #[code TextCategorizer] uses its own CNN model, to avoid sharing weights
-    |  with the other pipeline components. The document tensor is then
-    |  summarized by concatenating max and mean pooling, and a multilayer
-    |  perceptron is used to predict an output vector of length #[code nr_class],
-    |  before a logistic activation is applied elementwise. The value of each
-    |  output neuron is the probability that some class is present.
-
-+under-construction
diff --git a/website/docs/api/vectors.jade b/website/docs/api/vectors.jade
deleted file mode 100644
index ef9aa2b52..000000000
--- a/website/docs/api/vectors.jade
+++ /dev/null
@@ -1,7 +0,0 @@
-//- 💫 DOCS > API > VECTORS
-
-include ../../_includes/_mixins
-
-p A container class for vector data keyed by string.
-
-+under-construction
diff --git a/website/usage/_models/_languages.jade b/website/usage/_models/_languages.jade
new file mode 100644
index 000000000..abdad01ad
--- /dev/null
+++ b/website/usage/_models/_languages.jade
@@ -0,0 +1,72 @@
+//- 💫 DOCS > USAGE > MODELS > LANGUAGE SUPPORT
+
+p spaCy currently provides models for the following languages:
+
++table(["Language", "Code", "Language data", "Models"])
+    for models, code in MODELS
+        - var count = Object.keys(models).length
+        +row
+            +cell=LANGUAGES[code]
+            +cell #[code=code]
+            +cell
+                +src(gh("spaCy", "spacy/lang/" + code)) #[code lang/#{code}]
+            +cell
+                +a("/models/" + code) #{count} #{(count == 1) ? "model" : "models"}
+
++h(3, "alpha-support") Alpha tokenization support
+
+p
+    |  Work has started on the following languages. You can help by
+    |  #[+a("/usage/adding-languages#language-data") improving the existing language data]
+    |  and extending the tokenization patterns.
+
++aside("Usage note")
+    |  Note that the alpha languages don't yet come with a language model. In
+    |  order to use them, you have to import them directly, or use
+    |  #[+api("spacy#blank") #[code spacy.blank]]:
+
+    +code.o-no-block.
+        from spacy.lang.fi import Finnish
+        nlp = Finnish()  # use directly
+        nlp = spacy.blank('fi')  # blank instance
+
++table(["Language", "Code", "Language data"])
+    for lang, code in LANGUAGES
+        if !Object.keys(MODELS).includes(code)
+            +row
+                +cell #{LANGUAGES[code]}
+                +cell #[code=code]
+                +cell
+                    +src(gh("spaCy", "spacy/lang/" + code)) #[code lang/#{code}]
+
++infobox("Dependencies")
+    |  Some language tokenizers require external dependencies. To use #[strong Chinese],
+    |  you need to have #[+a("https://github.com/fxsjy/jieba") Jieba] installed.
+    |  The #[strong Japanese] tokenizer requires
+    |  #[+a("https://github.com/mocobeta/janome") Janome].
+
++h(3, "multi-language") Multi-language support
+    +tag-new(2)
+
+p
+    |  As of v2.0, spaCy supports models trained on more than one language. This
+    |  is especially useful for named entity recognition. The language ID used
+    |  for multi-language or language-neutral models is #[code xx]. The
+    |  language class, a generic subclass containing only the base language data,
+    |  can be found in #[+src(gh("spaCy", "spacy/lang/xx")) #[code lang/xx]].
+
+p
+    |  To load your model with the neutral, multi-language class, simply set
+    |  #[code "language": "xx"] in your
+    |  #[+a("/usage/training#models-generating") model package]'s
+    |  meta.json. You can also import the class directly, or call
+    |  #[+api("util#get_lang_class") #[code util.get_lang_class()]] for
+    |  lazy-loading.
+
++code("Standard import").
+    from spacy.lang.xx import MultiLanguage
+    nlp = MultiLanguage()
+
++code("With lazy-loading").
+    from spacy.util import get_lang_class
+    nlp = get_lang_class('xx')

From 22dd929b65d62cdeb1fd65ceb9b304e15b7b90d9 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 14:28:03 +0200
Subject: [PATCH 167/213] Add models documentation

---
 .gitignore                |  2 +-
 website/models/_data.json | 95 +++++++++++++++++++++++++++++++++++++
 website/models/de.jade    |  6 +++
 website/models/en.jade    |  6 +++
 website/models/es.jade    |  6 +++
 website/models/fr.jade    |  6 +++
 website/models/index.jade | 98 +++++++++++++++++++++++++++++++++++++++
 website/models/xx.jade    |  6 +++
 8 files changed, 224 insertions(+), 1 deletion(-)
 create mode 100644 website/models/_data.json
 create mode 100644 website/models/de.jade
 create mode 100644 website/models/en.jade
 create mode 100644 website/models/es.jade
 create mode 100644 website/models/fr.jade
 create mode 100644 website/models/index.jade
 create mode 100644 website/models/xx.jade

diff --git a/.gitignore b/.gitignore
index cb0a8e84e..572eea92d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,7 @@
 # spaCy
 spacy/data/
 corpora/
-models/
+/models/
 keys/
 
 # Website
diff --git a/website/models/_data.json b/website/models/_data.json
new file mode 100644
index 000000000..cc26b9bc9
--- /dev/null
+++ b/website/models/_data.json
@@ -0,0 +1,95 @@
+{
+    "sidebar": {
+        "Models": {
+            "Overview": "./"
+        },
+
+        "Language models": {
+            "English": "en",
+            "German": "de",
+            "Spanish": "es",
+            "French": "fr",
+            "Multi-Language": "xx"
+        }
+    },
+
+    "index": {
+        "title": "Models Overview",
+        "teaser": "Downloadable statistical models for spaCy to predict and assign linguistic features.",
+        "quickstart": true,
+        "menu": {
+            "Quickstart": "quickstart",
+            "Installation": "install",
+            "Naming Conventions": "conventions"
+        }
+    },
+
+    "MODELS": {
+        "en": ["en_core_web_sm", "en_core_web_lg", "en_vectors_web_lg"],
+        "de": ["de_dep_news_sm"],
+        "es": ["es_core_web_sm"],
+        "fr": [],
+        "xx": ["xx_ent_wiki_sm"]
+    },
+
+    "MODEL_META": {
+        "core": "Vocabulary, syntax, entities, vectors",
+        "dep": "Vocabulary, syntax",
+        "ent": "Named entities",
+        "vectors": "Word vectors",
+        "web": "written text (blogs, news, comments)",
+        "news": "written text (news, media)",
+        "wiki": "Wikipedia",
+        "uas": "Unlabelled dependencies",
+        "las": "Labelled dependencies",
+        "tags_acc": "Part-of-speech tags",
+        "ents_f": "Entities (F-score)",
+        "pipeline": "Processing pipeline components in order",
+        "sources": "Sources of training data"
+    },
+
+    "MODEL_LICENSES": {
+        "CC BY-SA": "https://creativecommons.org/licenses/by-sa/3.0/",
+        "CC BY-SA 3.0": "https://creativecommons.org/licenses/by-sa/3.0/",
+        "CC BY-NC": "https://creativecommons.org/licenses/by-nc/3.0/",
+        "CC BY-NC 3.0": "https://creativecommons.org/licenses/by-nc/3.0/"
+    },
+
+    "MODEL_ACCURACY": {
+        "uas": "UAS",
+        "las": "LAS",
+        "tags_acc": "POS",
+        "ents_f": "NER F"
+    },
+
+    "LANGUAGES": {
+        "en": "English",
+        "de": "German",
+        "fr": "French",
+        "es": "Spanish",
+        "it": "Italian",
+        "pt": "Portuguese",
+        "nl": "Dutch",
+        "sv": "Swedish",
+        "fi": "Finnish",
+        "nb": "Norwegian Bokmål",
+        "da": "Danish",
+        "hu": "Hungarian",
+        "pl": "Polish",
+        "he": "Hebrew",
+        "bn": "Bengali",
+        "id": "Indonesian",
+        "th": "Thai",
+        "zh": "Chinese",
+        "ja": "Japanese",
+        "xx": "Multi-language"
+    },
+
+    "EXAMPLE_SENTENCES": {
+        "en": "This is a sentence.",
+        "de": "Dies ist ein Satz.",
+        "fr": "C'est une phrase.",
+        "es": "Esto es una frase.",
+        "xx": "This is a sentence about Facebook."
+    }
+}
diff --git a/website/models/de.jade b/website/models/de.jade
new file mode 100644
index 000000000..113290b7a
--- /dev/null
+++ b/website/models/de.jade
@@ -0,0 +1,6 @@
+//- 💫 DOCS > MODELS > DE
+
+include ../_includes/_mixins
+
+//- This is a placeholder. The page is rendered via the template at
+//- /_includes/_page-model.jade.
diff --git a/website/models/en.jade b/website/models/en.jade
new file mode 100644
index 000000000..4f400662b
--- /dev/null
+++ b/website/models/en.jade
@@ -0,0 +1,6 @@
+//- 💫 DOCS > MODELS > EN
+
+include ../_includes/_mixins
+
+//- This is a placeholder. The page is rendered via the template at
+//- /_includes/_page-model.jade.
diff --git a/website/models/es.jade b/website/models/es.jade
new file mode 100644
index 000000000..7aad72e81
--- /dev/null
+++ b/website/models/es.jade
@@ -0,0 +1,6 @@
+//- 💫 DOCS > MODELS > ES
+
+include ../_includes/_mixins
+
+//- This is a placeholder. The page is rendered via the template at
+//- /_includes/_page-model.jade.
diff --git a/website/models/fr.jade b/website/models/fr.jade
new file mode 100644
index 000000000..1b3cc3fde
--- /dev/null
+++ b/website/models/fr.jade
@@ -0,0 +1,6 @@
+//- 💫 DOCS > MODELS > FR
+
+include ../_includes/_mixins
+
+//- This is a placeholder. The page is rendered via the template at
+//- /_includes/_page-model.jade.
diff --git a/website/models/index.jade b/website/models/index.jade
new file mode 100644
index 000000000..8f9aae739
--- /dev/null
+++ b/website/models/index.jade
@@ -0,0 +1,98 @@
+//- 💫 DOCS > MODELS
+
+include ../_includes/_mixins
+
++section("quickstart")
+    p
+        |  spaCy v2.0 features new neural models for #[strong tagging],
+        |  #[strong parsing] and #[strong entity recognition]. The models have
+        |  been designed and implemented from scratch specifically for spaCy, to
+        |  give you an unmatched balance of speed, size and accuracy. A novel
+        |  bloom embedding strategy with subword features is used to support
+        |  huge vocabularies in tiny tables. Convolutional layers with residual
+        |  connections, layer normalization and maxout non-linearity are used,
+        |  giving much better efficiency than the standard BiLSTM solution. For
+        |  more details, see the notes on the
+        |  #[+a("/api/#nn-models") model architecture].
+
+    p
+        |  The parser and NER use an imitation learning objective to
+        |  deliver #[strong accuracy in-line with the latest research systems],
+        |  even when  evaluated from raw text. With these innovations, spaCy
+        |  v2.0's models are #[strong 10&times; smaller],
+        |  #[strong 20% more accurate], and #[strong just as fast] as the
+        |  previous generation.
+
+    include ../usage/_models/_quickstart
+
++section("install")
+    +h(2, "install") Installation & Usage
+
+    include ../usage/_models/_install-basics
+
+    +infobox
+        |  For more details on how to use models with spaCy, see the
+        |  #[+a("/usage/models") usage guide on models].
+
++section("conventions")
+    +h(2, "model-naming") Model naming conventions
+
+    p
+        |  In general, spaCy expects all model packages to follow the naming
+        |  convention of #[code [lang]_[name]]. For spaCy's models, we also
+        |  chose to divide the name into three components:
+
+    +table
+        +row
+            +cell #[+label Type]
+            +cell
+                |  Model capabilities (e.g. #[code core] for general-purpose
+                |  model with vocabulary, syntax, entities and word vectors, or
+                |  #[code depent] for only vocab, syntax and entities).
+        +row
+            +cell #[+label Genre]
+            +cell
+                |  Type of text the model is trained on, e.g. #[code web] or
+                |  #[code news].
+        +row
+            +cell #[+label Size]
+            +cell Model size indicator, #[code sm], #[code md] or #[code lg].
+
+    p
+        |  For example, #[code en_core_web_sm] is a small English model trained
+        |  on written web text (blogs, news, comments), that includes
+        |  vocabulary, vectors, syntax and entities.
+
+    +h(3, "model-versioning") Model versioning
+
+    p
+        |  Additionally, the model versioning reflects both the compatibility
+        |  with spaCy, as well as the major and minor model version. A model
+        |  version #[code a.b.c] translates to:
+
+    +table
+        +row
+            +cell #[code a]
+            +cell
+                |  #[strong spaCy major version]. For example, #[code 2] for
+                |  spaCy v2.x.
+        +row
+            +cell #[code b]
+            +cell
+                |  #[strong Model major version]. Models with a different major
+                |  version can't be loaded by the same code. For example,
+                |  changing the width of the model, adding hidden layers or
+                |  changing the activation changes the model major version.
+        +row
+            +cell #[code c]
+            +cell
+                |  #[strong Model minor version]. Same model structure, but
+                |  different parameter values, e.g. from being trained on
+                |  different data, for different numbers of iterations, etc.
+
+    p
+        |  For a detailed compatibility overview, see the
+        |  #[+a(gh("spacy-models", "compatibility.json")) #[code compatibility.json]]
+        |  in the models repository. This is also the source of spaCy's internal
+        |  compatibility check, performed when you run the
+        |  #[+api("cli#download") #[code download]] command.
diff --git a/website/models/xx.jade b/website/models/xx.jade
new file mode 100644
index 000000000..8967f38fa
--- /dev/null
+++ b/website/models/xx.jade
@@ -0,0 +1,6 @@
+//- 💫 DOCS > MODELS > XX
+
+include ../_includes/_mixins
+
+//- This is a placeholder. The page is rendered via the template at
+//- /_includes/_page-model.jade.

From 319fac14fe979375faf5fb93db5efbc3c6d0a64c Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 14:28:18 +0200
Subject: [PATCH 168/213] Update global config and landing page

---
 website/_data.json |  49 +++++++----
 website/_harp.json |  80 ++++--------------
 website/index.jade | 199 ++++++++++++++++++++++-----------------------
 3 files changed, 145 insertions(+), 183 deletions(-)

diff --git a/website/_data.json b/website/_data.json
index 525c70d80..53543b2d0 100644
--- a/website/_data.json
+++ b/website/_data.json
@@ -3,24 +3,22 @@
         "landing": true,
         "logos": [
             {
-                "quora": [ "https://www.quora.com", 150 ],
-                "chartbeat": [ "https://chartbeat.com", 200 ],
-                "duedil": [ "https://www.duedil.com", 150 ],
-                "stitchfix": [ "https://www.stitchfix.com", 190 ]
+                "airbnb": [ "https://www.airbnb.com", 150, 45],
+                "quora": [ "https://www.quora.com", 120, 34 ],
+                "retriever": [ "https://www.retriever.no", 150, 33 ],
+                "stitchfix": [ "https://www.stitchfix.com", 150, 18 ]
             },
             {
-                "wayblazer": [ "http://wayblazer.com", 200 ],
-                "indico": [ "https://indico.io", 150 ],
-                "chattermill": [ "https://chattermill.io", 175 ],
-                "turi": [ "https://turi.com", 150 ],
-                "kip": [ "http://kipthis.com", 70 ]
-            },
+                "chartbeat": [ "https://chartbeat.com", 180, 25 ],
+                "allenai": [ "https://allenai.org", 220, 37 ]
+            }
+        ],
+        "features": [
             {
-                "socrata": [ "https://www.socrata.com", 150 ],
-                "cytora": [ "http://www.cytora.com", 125 ],
-                "signaln": [ "http://signaln.com", 150 ],
-                "wonderflow": [ "http://www.wonderflow.co", 200 ],
-                "synapsify": [ "http://www.gosynapsify.com", 150 ]
+                "thoughtworks": ["https://www.thoughtworks.com/radar/tools", 150, 28],
+                "wapo": ["https://www.washingtonpost.com/news/wonk/wp/2016/05/18/googles-new-artificial-intelligence-cant-understand-these-sentences-can-you/", 100, 77],
+                "venturebeat": ["https://venturebeat.com/2017/01/27/4-ai-startups-that-analyze-customer-reviews/", 150, 19],
+                "microsoft": ["https://www.microsoft.com/developerblog/2016/09/13/training-a-classifier-for-relation-extraction-from-medical-literature/", 130, 28]
             }
         ]
     },
@@ -34,7 +32,24 @@
         "landing": true
     },
 
-    "announcement" : {
-        "title": "Important Announcement"
+    "styleguide": {
+        "title": "Styleguide",
+        "sidebar": {
+            "Styleguide": { "": "styleguide" },
+            "Resources": {
+                "Website Source": "https://github.com/explosion/spacy/tree/master/website",
+                "Contributing Guide": "https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md"
+            }
+        },
+        "menu": {
+            "Introduction": "intro",
+            "Logo": "logo",
+            "Colors": "colors",
+            "Typography": "typography",
+            "Elements": "elements",
+            "Components": "components",
+            "Embeds": "embeds",
+            "Markup Reference": "markup"
+        }
     }
 }
diff --git a/website/_harp.json b/website/_harp.json
index 1c27426f4..55035c32f 100644
--- a/website/_harp.json
+++ b/website/_harp.json
@@ -11,12 +11,9 @@
         "COMPANY": "Explosion AI",
         "COMPANY_URL": "https://explosion.ai",
         "DEMOS_URL": "https://demos.explosion.ai",
+        "MODELS_REPO": "explosion/spacy-models",
 
-        "SPACY_VERSION": "1.8",
-        "LATEST_NEWS": {
-            "url": "https://github.com/explosion/spaCy/releases/tag/v2.0.0-alpha",
-            "title": "Test spaCy v2.0.0 alpha!"
-        },
+        "SPACY_VERSION": "2.0",
 
         "SOCIAL": {
             "twitter": "spacy_io",
@@ -27,25 +24,23 @@
         },
 
         "NAVIGATION": {
-            "Home": "/",
-            "Usage": "/docs/usage",
-            "Reference": "/docs/api",
-            "Demos": "/docs/usage/showcase",
-            "Blog": "https://explosion.ai/blog"
+            "Usage": "/usage",
+            "Models": "/models",
+            "API": "/api"
         },
 
         "FOOTER": {
             "spaCy": {
-                "Usage": "/docs/usage",
-                "API Reference": "/docs/api",
-                "Tutorials": "/docs/usage/tutorials",
-                "Showcase": "/docs/usage/showcase"
+                "Usage": "/usage",
+                "Models": "/models",
+                "API Reference": "/api",
+                "Resources": "/usage/resources"
             },
             "Support": {
                 "Issue Tracker": "https://github.com/explosion/spaCy/issues",
                 "StackOverflow": "http://stackoverflow.com/questions/tagged/spacy",
-                "Reddit usergroup": "https://www.reddit.com/r/spacynlp/",
-                "Gitter chat": "https://gitter.im/explosion/spaCy"
+                "Reddit Usergroup": "https://www.reddit.com/r/spacynlp/",
+                "Gitter Chat": "https://gitter.im/explosion/spaCy"
             },
             "Connect": {
                 "Twitter": "https://twitter.com/spacy_io",
@@ -74,21 +69,11 @@
                 {"id": "venv", "title": "virtualenv", "help": "Use a virtual environment and install spaCy into a user directory" },
                 {"id": "gpu", "title": "GPU", "help": "Run spaCy on GPU to make it faster. Requires an NVDIA graphics card with CUDA 2+. See section below for more info."}]
             },
-            { "id": "model", "title": "Models", "multiple": true, "options": [
-                { "id": "en", "title": "English", "meta": "50MB" },
-                { "id": "de", "title": "German", "meta": "645MB" },
-                { "id": "fr", "title": "French", "meta": "1.33GB" },
-                { "id": "es", "title": "Spanish", "meta": "377MB"}]
-            }
+            { "id": "model", "title": "Models", "multiple": true }
         ],
 
         "QUICKSTART_MODELS": [
-            { "id": "lang", "title": "Language", "options": [
-                { "id": "en", "title": "English", "checked": true },
-                { "id": "de", "title": "German" },
-                { "id": "fr", "title": "French" },
-                { "id": "es", "title": "Spanish" }]
-            },
+            { "id": "lang", "title": "Language"},
             { "id": "load", "title": "Loading style", "options": [
                 { "id": "spacy", "title": "Use spacy.load()", "checked": true, "help": "Use spaCy's built-in loader to load the model by name." },
                 {  "id": "module", "title": "Import as module", "help": "Import the model explicitly as a Python module." }]
@@ -98,50 +83,15 @@
             }
         ],
 
-        "MODELS": {
-            "en": [
-                { "id": "en_core_web_sm", "lang": "English", "feats": [1, 1, 1, 1], "size": "50 MB", "license": "CC BY-SA", "def": true },
-                { "id": "en_core_web_md", "lang": "English", "feats": [1, 1, 1, 1], "size": "1 GB", "license": "CC BY-SA" },
-                { "id": "en_depent_web_md", "lang": "English", "feats": [1, 1, 1, 0], "size": "328 MB", "license": "CC BY-SA" },
-                { "id": "en_vectors_glove_md", "lang": "English", "feats": [1, 0, 0, 1], "size": "727 MB", "license": "CC BY-SA" }
-            ],
-            "de": [
-                { "id": "de_core_news_md", "lang": "German", "feats": [1, 1, 1, 1], "size": "645 MB", "license": "CC BY-SA" }
-            ],
-            "fr": [
-                { "id": "fr_depvec_web_lg", "lang": "French", "feats": [1, 1, 0, 1], "size": "1.33 GB", "license": "CC BY-NC" }
-            ],
-            "es": [
-                { "id": "es_core_web_md", "lang": "Spanish", "feats": [1, 1, 1, 1], "size": "377 MB", "license": "CC BY-SA"}
-            ]
-        },
-
-        "EXAMPLE_SENTENCES": {
-            "en": "This is a sentence.",
-            "de": "Dies ist ein Satz.",
-            "fr": "C'est une phrase.",
-            "es": "Esto es una frase."
-        },
-
         "ALPHA": true,
-        "V_CSS": "1.6",
-        "V_JS": "1.2",
+        "V_CSS": "2.0",
+        "V_JS": "2.0",
         "DEFAULT_SYNTAX": "python",
         "ANALYTICS": "UA-58931649-1",
         "MAILCHIMP": {
             "user": "spacy.us12",
             "id": "83b0498b1e7fa3c91ce68c3f1",
             "list": "89ad33e698"
-        },
-        "BADGES": {
-            "pipy": {
-                "badge": "https://img.shields.io/pypi/v/spacy.svg?style=flat-square",
-                "link": "https://pypi.python.org/pypi/spacy"
-            },
-            "conda": {
-                "badge": "https://anaconda.org/conda-forge/spacy/badges/version.svg",
-                "link": "https://anaconda.org/conda-forge/spacy"
-            }
         }
     }
 }
diff --git a/website/index.jade b/website/index.jade
index 9336d5c34..0155ab295 100644
--- a/website/index.jade
+++ b/website/index.jade
@@ -8,61 +8,48 @@ include _includes/_mixins
         | Natural Language#[br]
         | Processing
 
-    h2.c-landing__title.o-block.u-heading-1
-        | in Python
+    h2.c-landing__title.o-block.u-heading-3
+        span.u-text-label.u-text-label--light in Python
 
-    +landing-badge(gh("spaCy") + "/releases/tag/v2.0.0-alpha", "v2alpha", "Try spaCy v2.0.0 alpha!")
++grid.o-content.c-landing__blocks
+    +grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
+        +h(3) Fastest in the world
+        p
+            |  spaCy excels at large-scale information extraction tasks.
+            |  It's written from the ground up in carefully memory-managed
+            |  Cython. Independent research has confirmed that spaCy is
+            |  the fastest in the world.  If your application needs to
+            |  process entire web dumps, spaCy is the library you want to
+            |  be using.
 
-    +grid.o-content
-        +grid-col("third").o-card
-            +h(2) Fastest in the world
-            p
-                |  spaCy excels at large-scale information extraction tasks.
-                |  It's written from the ground up in carefully memory-managed
-                |  Cython. Independent research has confirmed that spaCy is
-                |  the fastest in the world.  If your application needs to
-                |  process entire web dumps, spaCy is the library you want to
-                |  be using.
+        +button("/usage/facts-figures", true, "primary")
+            |  Facts & figures
 
-            +button("/docs/api", true, "primary")
-                |  Facts & figures
+    +grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
+        +h(3) Get things done
+        p
+            |  spaCy is designed to help you do real work — to build real
+            |  products, or gather real insights. The library respects
+            |  your time, and tries to avoid wasting it. It's easy to
+            |  install, and its API is simple and productive. We like to
+            |  think of spaCy as the Ruby on Rails of Natural Language
+            |  Processing.
 
-        +grid-col("third").o-card
-            +h(2) Get things done
-            p
-                |  spaCy is designed to help you do real work — to build real
-                |  products, or gather real insights. The library respects
-                |  your time, and tries to avoid wasting it. It's easy to
-                |  install, and its API is simple and productive. I like to
-                |  think of spaCy as the Ruby on Rails of Natural Language
-                |  Processing.
+        +button("/usage", true, "primary")
+            |  Get started
 
-            +button("/docs/usage", true, "primary")
-                |  Get started
+    +grid-col("third").c-landing__card.o-card.o-grid.o-grid--space
+        +h(3) Deep learning
+        p
+            |  spaCy is the best way to prepare text for deep learning.
+            |  It interoperates seamlessly with TensorFlow, PyTorch,
+            |  scikit-learn, Gensim and the
+            |  rest of Python's awesome AI ecosystem. spaCy helps you
+            |  connect the statistical models trained by these libraries
+            |  to the rest of your application.
 
-        +grid-col("third").o-card
-            +h(2) Deep learning
-            p
-                |  spaCy is the best way to prepare text for deep learning.
-                |  It interoperates seamlessly with
-                |  #[+a("https://www.tensorflow.org") TensorFlow],
-                |  #[+a("https://keras.io") Keras],
-                |  #[+a("http://scikit-learn.org") Scikit-Learn],
-                |  #[+a("https://radimrehurek.com/gensim") Gensim] and the
-                |  rest of Python's awesome AI ecosystem. spaCy helps you
-                |  connect the statistical models trained by these libraries
-                |  to the rest of your application.
-
-            +button("/docs/usage/deep-learning", true, "primary")
-                |  Read more
-
-.o-inline-list.o-block.u-border-bottom.u-text-small.u-text-center.u-padding-small
-    +a(gh("spaCy") + "/releases")
-        strong.u-text-label.u-color-subtle #[+icon("code", 18)] Latest release:
-        |  v#{SPACY_VERSION}
-
-    if LATEST_NEWS
-        +a(LATEST_NEWS.url) #[+tag.o-icon New!] #{LATEST_NEWS.title}
+        +button("/usage/deep-learning", true, "primary")
+            |  Read more
 
 .o-content
     +grid
@@ -92,67 +79,77 @@ include _includes/_mixins
             +h(2) Features
             +list
                 +item Non-destructive #[strong tokenization]
-                +item Syntax-driven sentence segmentation
+                +item Support for #[strong #{LANG_COUNT}+ languages]
+                +item #[strong #{MODEL_COUNT} statistical models] for #{MODEL_LANG_COUNT} languages
                 +item Pre-trained #[strong word vectors]
+                +item Easy #[strong deep learning] integration
                 +item Part-of-speech tagging
                 +item #[strong Named entity] recognition
                 +item Labelled dependency parsing
+                +item Syntax-driven sentence segmentation
+                +item Built in #[strong visualizers] for syntax and NER
                 +item Convenient string-to-hash mapping
                 +item Export to numpy data arrays
-                +item GIL-free #[strong multi-threading]
                 +item Efficient binary serialization
-                +item Easy #[strong deep learning] integration
-                +item Statistical models for #[strong English] and #[strong German]
+                +item Easy #[strong model packaging] and deployment
                 +item State-of-the-art speed
                 +item Robust, rigorously evaluated accuracy
 
++landing-banner("Convolutional neural network models", "New in v2.0")
+    p
+        |  spaCy v2.0 features new neural models for #[strong tagging],
+        |  #[strong parsing] and #[strong entity recognition]. The models have
+        |  been designed and implemented from scratch specifically for spaCy, to
+        |  give you an unmatched balance of speed, size and accuracy. A novel
+        |  bloom embedding strategy with subword features is used to support
+        |  huge vocabularies in tiny tables. Convolutional layers with residual
+        |  connections, layer normalization and maxout non-linearity are used,
+        |  giving much better efficiency than the standard BiLSTM solution.
+        |  Finally, the parser and NER use an imitation learning objective to
+        |  deliver accuracy in-line with the latest research systems,
+        |  even when  evaluated from raw text. With these innovations, spaCy
+        |  v2.0's models are #[strong 10&times; smaller],
+        |  #[strong 20% more accurate], and #[strong just as fast] as the
+        |  previous generation.
+
+    .o-block-small.u-text-right
+        +button("/models", true, "secondary-light") Download models
+
++landing-logos("spaCy is trusted by", logos)
+    +button(gh("spacy") + "/stargazers", false, "secondary", "small")
+        |  and many more
+
++landing-logos("Featured on", features).o-block-small
+
++landing-banner("Prodigy: Radically efficient machine teaching", "From the makers of spaCy")
+    p
+        |  Prodigy is an #[strong annotation tool] so efficient that data scientists can
+        |  do the annotation themselves, enabling a new level of rapid
+        |  iteration. Whether you're working on entity recognition, intent
+        |  detection or image classification, Prodigy can help you
+        |  #[strong train and evaluate] your models faster. Stream in your own examples or
+        |  real-world data from live APIs, update your model in real-time and
+        |  chain models together to build more complex systems.
+
+    .o-block-small.u-text-right
+        +button("https://prodi.gy", true, "secondary-light") Try it out
+
+.o-content
+    +grid
+        +grid-col("half")
+            +h(2) Benchmarks
+
+            p
+                |  In 2015, independent researchers from Emory University and
+                |  Yahoo! Labs showed that spaCy offered the
+                |  #[strong fastest syntactic parser in the world] and that its
+                |  accuracy was #[strong within 1% of the best] available
+                |  (#[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") Choi et al., 2015]).
+                |  spaCy v2.0, released in 2017, is more accurate than any of
+                |  the systems Choi et al. evaluated.
+
             .o-inline-list
-                +button("/docs/usage/lightning-tour", true, "secondary")
-                    | See examples
+                +button("/usage/facts-figures#benchmarks", true, "secondary") See details
 
-    .o-block.u-text-center.u-padding
-        h3.u-text-label.u-color-subtle.o-block spaCy is trusted by
-
-        each row in logos
-            +grid("center").o-inline-list
-                each details, name in row
-                    +a(details[0])
-                        img(src="/assets/img/logos/#{name}.png" alt=name width=(details[1] || 150)).u-padding-small
-
-.u-pattern.u-padding
-    +grid.o-card.o-content
-        +grid-col("quarter")
-            img(src="/assets/img/profile_matt.png" width="280")
-
-        +grid-col("three-quarters")
-            +h(2) What's spaCy all about?
-
-            p
-                |  By 2014, I'd been publishing NLP research for about 10
-                |  years. During that time, I saw a huge gap open between the
-                |  technology that Google-sized companies could take to market,
-                |  and what was available to everyone else. This was especially
-                |  clear when companies started trying to use my research. Like
-                |  most researchers, my work was free to read, but expensive to
-                |  apply. You could run my code, but its requirements were
-                |  narrow. My code's mission in life was to print results
-                |  tables for my papers — it was good at this job, and bad at
-                |  all others.
-
-            p
-                |  spaCy's #[+a("/docs/api/philosophy") mission] is to make
-                |  cutting-edge NLP practical and commonly available.  That's
-                |  why I left academia in 2014, to build a production-quality
-                |  open-source NLP library. It's why
-                |  #[+a("https://twitter.com/_inesmontani") Ines] joined the
-                |  project in 2015, to build visualisations, demos and
-                |  annotation tools that make NLP technologies less abstract
-                |  and easier to use. Together, we've founded
-                |  #[+a(COMPANY_URL, true) Explosion AI], to develop data packs
-                |  you can drop into spaCy to extend its capabilities. If
-                |  you're processing Hindi insurance claims, you need a model
-                |  for that. We can build it for you.
-
-            .o-block
-                +a("https://twitter.com/honnibal")
-                    +svg("graphics", "matt-signature", 60, 45).u-color-theme
+        +grid-col("half")
+            include usage/_facts-figures/_benchmarks-choi-2015

From 23019d1daa9768f222cd2e94467451eb61eb885a Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 14:28:24 +0200
Subject: [PATCH 169/213] Add styleguide

---
 website/styleguide.jade | 623 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 623 insertions(+)
 create mode 100644 website/styleguide.jade

diff --git a/website/styleguide.jade b/website/styleguide.jade
new file mode 100644
index 000000000..107f7e2e6
--- /dev/null
+++ b/website/styleguide.jade
@@ -0,0 +1,623 @@
+//- 💫 STYLEGUIDE
+
+include _includes/_mixins
+
++section("intro")
+    p
+        |  This styleguide is loosely based on the concept and principles of
+        |  #[+a("http://bradfrost.com/blog/post/atomic-web-design/") Atomic Design].
+        |  The templates consist of small elements (atoms) which are combined
+        |  and connected to form larger molecules and full components. The site
+        |  is compiled using #[+a("http://harpjs.com/") Harp], a static web
+        |  server with built-in preprocessing. Templates are written entirely in
+        |  #[+a("http://jade-lang.com") Jade] (aka. Pug), a clean,
+        |  whitespace-sensitive templating  language that compiles to HTML.
+        |  CSS is written in #[+a("http://sass-lang.com") Sass] and preprocessed
+        |  via Harp, JavaScript is written in ES6 syntax and compiled using
+        |  #[+a("https://babeljs.io") Babel].
+
++section("logo")
+    +h(2, "logo", "website/assets/img/logo.svg") Logo
+
+    p
+        |  If you would like to use the spaCy logo on your site, please get in
+        |  touch and ask us first. However, if you want to show support and tell
+        |  others that your project is using spaCy, you can grab one of our
+        |  #[+a("/usage/spacy-101#faq-project-with-spacy") spaCy badges].
+
+    +grid
+        each color in [["#09a3d5", "#fff"], ["#fff", "#09a3d5"]]
+            +grid-col("half").o-box.u-text-center.u-padding-medium(style="background: #{color[1]}; color: #{color[0]}")
+                +icon("spacy", 338, 108)(style="max-width: 100%")
+
++section("colors")
+    +h(2, "colors", "website/assets/css/_variables.sass") Colors
+
+    +grid
+        each color, label in {"dark": "#1a1e23", "medium": "#45505e", "light": "#dddddd", "faint": "#f6f6f6", "blue": "#09a3d5", "dark blue": "#077ea4", "green": "#05b083", "dark green": "#047e5e"}
+            +grid-col("quarter").u-text-small.o-card
+                div(style="height: 75px; background: #{color}; border-top-left-radius: 6px; border-top-right-radius: 6px")
+                .u-text-center.u-padding-medium
+                    +label=label
+                    code=color
+
+        each pattern in ["blue", "green"]
+            +grid-col("half").u-text-small.o-card
+                div(style="background: url('/assets/img/pattern_#{pattern}.jpg') center/100% repeat; height: 125px; border-top-left-radius: 6px; border-top-right-radius: 6px")
+                .u-text-center.u-padding-medium
+                    +label #{pattern} pattern
+                    .u-text-tiny.u-color-subtle by #[+a("https://dribbble.com/kemal").u-color-dark Kemal Şanlı]
+
++section("typography")
+    +h(2, "typography") Typography
+
+    +aside-code("Usage", "jade").
+        +h(2) Headline two
+        +h(3, "some-id") Headline three
+
+    p
+        |  Headlines are set in
+        |  #[+a("http://cargocollective.com/hanken/HK-Grotesk-Open-Source-Font") HK Grotesk]
+        |  by Hanken Design. All other body text and code uses the best-matching
+        |  default system font to provide a "native" reading experience.
+
+    each heading in [0, 1, 2, 3, 4, 5]
+        .o-block-small(class="u-heading-" + heading) Heading #{heading}
+    +label Label
+
++section("elements")
+    +h(2, "elements", "website/_includes/_mixins.jade") Elements
+
+    p
+        |  The site comes with a collection of simple content elements,
+        |  implemented as mixins. These elements can be used individually, or as
+        |  part of larger components.
+
+    +h(3, "text-links") Special text & links
+
+    +aside-code("Usage", "jade").
+        +api("token") &#35;[code Token]
+        +src("https://github.com") GitHub source
+        +help("Help text here")
+        +fn(1, "bibliography")
+
+    p
+        |  Special link styles are implemented as mixins and can be used to
+        |  mark links to the API documentation, or links to source code.
+        |  Additionally a "help" icon can be added to provide more information
+        |  via a tooltip.
+
+    p.o-inline-list
+        +a("#") Link
+        code Inline Code
+        +api("token") #[code Token]
+        +src(gh("spacy")) Source
+        span.u-color-dark.u-nowrap Help #[+help("Help text here")]
+        span Footnote#[+fn(1, "", "This marks a footnote and can link to a section")]
+
+    +h(3, "buttons") Buttons
+
+    +aside-code("Usage", "jade").
+        +button("https://spacy.io", true, "secondary")
+        +button("https://spacy.io", true, "primary", "small")
+
+    p
+        |  Link buttons come in two variants, #[code primary] and
+        |  #[code secondary] and two sizes, with an optional #[code small] size
+        |  modifier.Since they're mostly used as enhanced links, the buttons are
+        |  implemented as styled links instead of native button elements.
+
+    p.o-inline-list
+        +button("#", false, "primary") Primary
+        +button("#", false, "secondary") Secondary
+        +button("#", false, "primary", "small") Primary small
+        +button("#", false, "secondary", "small") Secondary small
+
+    +h(3, "tags") Tags
+
+    +aside-code("Usage", "jade").
+        +tag I'm a tag
+        +tag-new(2)
+        +tag-model("Named entities")
+
+    p
+        |  Tags can be used together with headlines, or next to properties
+        |  across the documentation, and combined with tooltips to provide
+        |  additional information. The #[code +tag-new] mixin takes a version
+        |  number and can mark new features. Using the mixin, visibility of this
+        |  tag can be toggled once the feature isn't considered new anymore.
+        |  The #[code +tag-model] mixin takes a description of model
+        |  capabilities and can be used to mark features that require a
+        |  respective model to be installed.
+
+    p.o-inline-list
+        +tag I'm a tag
+        +tag-new(2)
+        +tag-model("Named entities")
+
+    +h(3, "icons", "website/_includes/_svg.jade") Icons
+
+    +aside-code("Usage", "jade").
+        +icon("github", 18)
+
+    p
+        |  Icons are implemented via a SVG sprite and can be included as a
+        |  mixin, using their name and an optional size value in #[code px].
+
+    +infobox.u-text-center
+        each icon in ["code", "arrow-right", "book", "circle", "chat", "star", "help", "accept", "reject", "markdown", "course", "github", "jupyter"]
+            .u-inline-block.u-padding-small.u-color-dark(data-tooltip=icon data-tooltip-style="code" aria-label=icon)
+                +icon(icon, 20)
+
++section("components")
+    +h(2, "components", "website/_includes/_mixins.jade") Components
+
+    p
+        |  The site uses a collection of Jade mixins to make it easy to use
+        |  complex content elements across templates and blog posts. To read
+        |  more about the concept of modular markup components, check out our
+        |  #[+a("https://explosion.ai/blog/modular-markup", true) blog post] on
+        |  the subject.
+
+    +h(3, "grid") Grid
+
+    +aside-code("Usage", "jade").
+        +grid
+            +grid-col("half") Half
+            +grid-col("half") Half
+
+    p
+        |  For now, the grid is still implemented as a standard #[code flexbox]
+        |  grid, although it may be refactored to use CSS #[code grid] going
+        |  forward. The grid supports up to four columns and collapses on
+        |  small screens.
+
+    +grid
+        each count, label in {"full": 1, "half": 2, "third": 3, "quarter": 4}
+            each _ in Array(count)
+                +grid-col(label).o-box.u-text-center.u-text-label.u-color-dark=label
+
+    +h(3, "table") Table
+
+    +aside-code("Usage", "jade").
+        +table(["Header 1", "Header 2"])
+            +row
+                +cell Cell
+                +cell Cell
+
+    p
+        |  Tables are used to present data and API documentation. If a list of
+        |  headings is specified, those will be rendered as the table header.
+        |  An optional #[code +row("foot")] can be used to mark a footer row
+        |  with a distinct style, for example to visualise the return values
+        |  of a documented function.
+
+    - var table_cols = ["Header 1", "Header 2", "Header 3"]
+    +table(table_cols)
+        each row, i in Array(4)
+            +row((i == 3) ? "foot" : null)
+                each col, j in table_cols
+                    +cell
+                        if i == 3 && j == 0
+                            |  Footer
+
+                        else
+                            |  Row #{i + 1}, cell #{j + 1}
+
+    +h(3, "list") List
+
+    +aside-code("Usage", "jade").
+        +list("numbers", 3)
+            +item List item
+            +item List item
+
+    p
+        |  Lists are available as bulleted, numbered, lettered and lower roman.
+        |  Optionally, a start index can be defined as the second argument
+        |  on ordered lists.
+
+    +grid
+        +list
+            +item I am a bulleted list
+            +item I have nice bullets
+            +item Lorem ipsum dolor
+            +item consectetur adipiscing elit
+
+        +list("numbers")
+            +item I am an ordered list
+            +item I have nice numbers
+            +item Lorem ipsum dolor
+            +item consectetur adipiscing elit
+
+        +list("numbers", 10)
+            +item I am an numbered list
+            +item with a custom start number
+            +item Lorem ipsum dolor
+            +item consectetur adipiscing elit
+
+        +list("letters")
+            +item I am an ordered list
+            +item I have uppercase letters
+            +item Lorem ipsum dolor
+            +item consectetur adipiscing elit
+
+        +list("letters", 18)
+            +item I am an ordered list
+            +item with a custom start letter
+            +item Lorem ipsum dolor
+            +item consectetur adipiscing elit
+
+        +list("roman")
+            +item I am an ordered list
+            +item I have roman numerals
+            +item Lorem ipsum dolor
+            +item consectetur adipiscing elit
+
+    +h(3, "code") Code
+
+    +aside-code("Usage", "jade").
+        +code("Label", "python").
+            import spacy
+            nlp = spacy.load('en')
+            doc = nlp(u"This is a sentence.")
+
+    p
+        |  Code blocks use the #[+a("http://prismjs.com/") Prism] syntax
+        |  highlighter with a custom theme. The language can be set individually
+        |  on each block, and defaults to Python. An optional label can be
+        |  added as the first argument, which is displayed above the block.
+        |  When using the #[code +code] mixin, don't forget to append a period
+        |  #[code &period;] to the mixin call. This tells Jade to interpret the
+        |  indented block as plain text and preserve whitespace.
+
+    +code("Using spaCy").
+        import spacy
+        nlp = spacy.load('en')
+        doc = nlp(u"This is a sentence.")
+
+    +h(3, "aside") Aside
+
+    +aside-code("Usage", "jade").
+        +aside("Title") This is an aside
+        +aside-code("Title", "python").
+            nlp = spacy.load('en')
+
+    p
+        |  Asides can be used to display additional notes and content in the
+        |  right-hand column. Two mixins are available: #[code +aside] for
+        |  regular text with an optional title, #[code +aside-code], which
+        |  roughly mimicks the #[code +code] component. Visually, asides are
+        |  moved to the side on the X-axis, and displayed at the same level
+        |  they were inserted. On small screens, they collapse and are rendered
+        |  in their original position, in between the text.
+
+    +h(3, "infobox") Infobox
+
+    +aside-code("Usage", "jade").
+        +infobox("Label") This is text.
+        +infobox("Label", "⚠️") This is text.
+
+    p
+        |  Infoboxes can be used to add notes, updates, warnings or additional
+        |  information to a page or section. Semantically, they're implemented
+        |  and interpreted as an #[code aside] element. Since infobox titles
+        |  are especially nice with emoji, an emoji can be specified as the
+        |  second argument for optimal rendering and spacing.
+
+    +infobox("Infobox label") Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque enim ante, pretium a orci eget, varius dignissim augue. Nam eu dictum mauris, id tincidunt nisi. Integer commodo pellentesque tincidunt.
+
+    +infobox("Infobox label with emoji", "⚠️") Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque enim ante, pretium a orci eget, varius dignissim augue. Nam eu dictum mauris, id tincidunt nisi. Integer commodo pellentesque tincidunt.
+
+    +h(3, "card") Card
+
+    +aside-code("Usage", "jade").
+        +grid
+            +card("Title", "https://", "Author", "github")
+                |  Card content goes here
+    p
+        |  Cards can be used to present external content and links, like GitHub
+        |  projects, websites, books or articles. They can take an optional
+        |  value for the content author and icon, which is displayed in the
+        |  corner. The content supplied via an indented block can also include
+        |  formatting or other elements like images. Under the hood, cards are
+        |  styled grid columns and should therefore always be used as children
+        |  of #[code +grid].
+
+    +grid
+        +card("spaCy", "https://github.com/explosion/spaCy", "Explosion AI", "github")
+            |  An open-source library for industrial-strength Natural Language
+            |  Processing in Python.
+
+        +card("Prodigy", "https://prodi.gy", "Explosion AI", "star")
+            |  A new annotation tool for radically efficient machine teaching,
+            |  powered by active learning.
+
++section("embeds")
+    +h(2, "embeds") Embeds
+
+    p
+        |  The framework also allows embedding content from selected sites via
+        |  mixins, usually styled wrappers for the respective embed codes.
+
+    +h(3, "codepen") CodePen
+
+    p
+        |  #[+a("https://codepen.io") CodePen] is a platform to share and
+        |  collaborate on front-end code. It comes with a powerful live editor,
+        |  and is mostly used on this site to present visualizations created by
+        |  spaCy's built-in visualizers. Embeds use a
+        |  #[+a("https://blog.codepen.io/documentation/pro-features/unlimited-embed-themes/") custom theme]
+        |  and are included using a mixin that takes the pen ID, and an optional
+        |  height to prevent content reflow on load.
+
+    +aside-code("Usage", "jade").
+        +codepen("2f2ad1408ff79fc6a326ea3aedbb353b", 160)
+
+    +codepen("2f2ad1408ff79fc6a326ea3aedbb353b", 160)
+
+    +h(3, "github") GitHub
+
+    p
+        |  GitHub only allows native embedding of Gists, but Gists are only
+        |  available for users, not organisations. So in order to be able to
+        |  embed examples from spaCy's #[+src(gh("spacy", "examples")) examples],
+        |  we ended up developing our own micro library. A #[code data-gh-embed]
+        |  attribute on the code block, set via the mixin, specifies the file
+        |  to load. The script then fetches the raw text via the GitHub API and
+        |  renders it in the container. This way, the example previews on the
+        |  site are always in sync with the examples in the repository.
+
+    +aside-code("Usage", "jade").
+        +github("spacy", "examples/training/train_textcat.py")
+
+    +github("spacy", "examples/training/train_textcat.py")
+
++section("markup")
+    +h(2, "markup") Markup reference
+
+    p
+        |  The spaCy website is implemented
+        |  in #[+a("https://www.jade-lang.org") Jade (aka Pug)], and is built or
+        |  served by #[+a("(https://harpjs.com") Harp]. Jade is an extensible
+        |  templating language with a readable syntax, that compiles to HTML.
+        |  The website source makes extensive use of Jade mixins, so that the
+        |  design system is abstracted away from the content you're writing. You
+        |  can read more about our approach in our blog post,
+        |  #[+a("https://explosion.ai/blog/modular-markup", true) "Rebuilding a Website with Modular Markup"].
+
+    +code("Viewing the site locally", "bash").
+        sudo npm install --global harp
+        git clone #{gh("spacy")}
+        cd spacy/website
+        harp server --port 9000
+
+    +h(3, "jade") Jade conventions
+
+    p
+        |  Jade/Pug is a whitespace-sensitive markup language that compiles to
+        |  HTML. Indentation is used to nest elements, and for template logic,
+        |  like #[code if], #[code else] or #[code for], mainly used to iterate
+        |  over objects and arrays in the meta data. It also allows inline
+        |  JavaScript expressions.
+
+    +grid.o-no-block
+        +grid-col("half")
+            +code("Input", "jade").
+                ul#some-id
+                    for item in ['a', 'b', 'c']
+                        li.test=item.toUpperCase()
+                            if item == 'a'
+                                |  🎉
+
+        +grid-col("half")
+            +code("Output", "markup").
+                &lt;ul id="some-id"&gt;
+                    &lt;li class="test"&gt;A 🎉&lt;li&gt;
+                    &lt;li class="test"&gt;B&lt;li&gt;
+                    &lt;li class="test"&gt;C&lt;li&gt;
+                &lt;/ul&gt;
+
+    p
+        |  For an overview of Harp and Jade, see
+        |  #[+a("https://ines.io/blog/the-ultimate-guide-static-websites-harp-jade") this blog post].
+        |  For more info on the Jade/Pug syntax, check out their
+        |  #[+a("https://pugjs.org") documentation]. In the spacy.io source, we
+        |  use 4 spaces to indent and hard-wrap at 80 characters.
+
+    +code(false, "jade").
+        p This is a very short paragraph. It stays inline.
+
+        p
+            |  This is a much longer paragraph. It's hard-wrapped at 80 characters to
+            |  make it easier to read on GitHub and in editors that do not have soft
+            |  wrapping enabled. To prevent Jade from interpreting each line as a new
+            |  element, it's prefixed with a pipe and two spaces. This ensures that no
+            |  spaces are dropped – for example, if your editor strips out trailing
+            |  whitespace by default. Inline links are added using the inline syntax,
+            |  like this: &#35;[+a("https://google.com") Google].
+
+    +aside("Plain HTML elements used")
+        +list.o-no-block
+            +item #[code p]: Regular paragraph.
+            +item #[code code]: Inline #[code code].
+            +item #[code em]: #[em Italicized] text.
+            +item #[code strong]: #[strong Bold] text.
+
+    p
+        |  Note that for external links, #[code &plus;a("...")] is used instead
+        |  of #[code a(href="...")] – it's a mixin that takes care of adding all
+        |  required attributes. If possible, always use a mixin instead of
+        |  regular HTML elements. With a few exceptions for practical reasons,
+        |  class names and other HTML attributes should
+        |  #[strong only live in mixins] and not in the site content.
+
+    +infobox("Mixins documentation")
+        |  For a more detailed overview and API documentation of the available
+        |  mixins and their arguments, see the source of the
+        |  #[+src(gh("spacy", "website/_includes/_mixins.jade")) #[code _includes/_mixins.jade]]
+        |  file.
+
+    +h(3, "directory-structure") Directory structure
+
+    p
+        |  Each section is represented by its own subdirectory, containing a
+        |  #[code _data.json] to store its meta information. All #[code .jade]
+        |  files that are not prefixed with an underscore are later converted to
+        |  #[code .html]. Site assets like images, styles, fonts and scripts are
+        |  loaded from a directory #[code assets]. Global variables like titles,
+        |  navigations, URLs and other settings are defined in the global
+        |  #[code _harp.json].
+
+    +code("website", "yaml").
+        ├── _includes         # layout partials, shared mixins, functions
+        ├── api
+        |   ├── _data.json    # meta data for API section
+        |   └── ...           # other pages and partials
+        ├── assets
+        |   ├── css           # Sass styles, will be converted to CSS
+        |   ├── fonts         # web fonts
+        |   ├── img           # images and icons
+        |   └── js            # scripts, custom and third-party
+        ├── models
+        |   ├── _data.json    # model meta data and meta for models section
+        |   └── ...           # other pages and partials
+        ├── usage
+        |   ├── _data.json    # meta data for usage section
+        |   └── ...           # other pages and partials
+        ├── _data.json        # meta data for pages in the root
+        ├── _harp.json        # global site configuration and variables
+        ├── _layout.jade      # global layout
+        ├── 404.jade          # 404 page
+        └── index.jade        # landing page
+
+    +h(3, "data-structure") Data structure
+
+    p
+        |  While all page content lives in the #[code .jade] files, article meta
+        |  (page titles, sidebars etc.) is stored as JSON. Each folder contains
+        |  a #[code _data.json] with all required meta for its files. Meta
+        |  information is keyed by the page's filename or slug, and becomes
+        |  available to the templates as variables. The #[code menu] specifies
+        |  the sub-navigation in the sidebar and maps titles to section IDs.
+
+    +code(false, "json").
+        "resources": {
+            "title": "Resources",
+            "teaser": "Libraries, demos, books, courses and research systems featuring spaCy.",
+            "menu": {
+                "Third-party libraries": "libraries",
+                "Demos & Visualizations": "demos",
+                "Books & Courses": "books",
+                "Jupyter Notebooks": "notebooks",
+                "Research": "research"
+            }
+        }
+
+    p
+        |  Long pages with multiple sections are often split into separate
+        |  partials that live in their own subdirectory. Those partials can be
+        |  included on the page, and if needed, across the site to avoid content
+        |  duplication. Partials and partial directories are prefixed with an
+        |  underscore #[code _] to prevent Harp from building them as separate
+        |  files.
+
+    +code("spacy-101.jade", "jade").
+        +section("architecture")
+            +h(2, "architecture") Architecture
+            include _spacy-101/_architecture
+
+    +h(3, "model-data", "website/models/_data.json") Model data
+
+    p
+        |  The new #[+a("/models") models directory] uses the GitHub API to
+        |  fetch meta information from the latest
+        |  #[+a(gh("spacy-models") + "/releases") model releases]. This ensures
+        |  that the website is always up to date. However, some details, like
+        |  human-readable descriptions and the list of available models and
+        |  languages, is stored in the static CMS and used across the site.
+        |  This info only lives in one place, #[code models/_data.json].
+        |  Wherever possible, the model info is generated dynamically – for
+        |  example, in installation examples, quickstart widgets and even in the
+        |  total model and language count on the landing page.
+
+    p
+        |  The following data is stored and made available in the global scope:
+
+    +table(["Variable", "Description", "Example"])
+        +row
+            +cell #[code LANGUAGES]
+            +cell All languages supported by spaCy, code mapped to name.
+            +cell
+                +code(false, "json").o-no-block "en": "English"
+
+        +row
+            +cell #[code MODELS]
+            +cell Model names (without version). Language codes mapped to list of names.
+            +cell
+                +code(false, "json").o-no-block "xx": ["xx_ent_wiki_sm"]
+
+        +row
+            +cell #[code MODEL_META]
+            +cell Description for model name components and meta data, ID mapped to string.
+            +cell
+                +code(false, "json").o-no-block "vectors": "Word vectors",
+
+        +row
+            +cell #[code MODEL_LICENSES]
+            +cell License types mapped to license URL.
+            +cell
+                +code(false, "json").o-no-block "CC BY-SA 3.0": "http://..."
+
+        +row
+            +cell #[code MODEL_ACCURACY]
+            +cell Display labels for accuracy keys.
+            +cell
+                +code(false, "json").o-no-block "ents_f": "NER F"
+
+        +row
+            +cell #[code EXAMPLE_SENTENCES]
+            +cell Example sentences for different languages.
+            +cell
+                +code(false, "json").o-no-block "es": "Esto es una frase."
+
+    +h(3, "functions", "website/_includes/_functions.jade") Template functions
+
+    p
+        |  Jade allows you to implement any custom logic as inline JavaScript
+        |  expressions. Reusable functions are organised in a
+        |  #[code _functions.jade], which is included via the mixins file and
+        |  makes them accessible on each page. However, most functions deal
+        |  with internals only, e.g. prefixing class names in mixins or
+        |  converting paths and links.
+
+    +h(4, "gh") gh
+        +tag function
+
+    p
+        |  Since GitHub links can be long and tricky, this function takes care
+        |  generating them automatically for spaCy and all repositories owned
+        |  by the #[+a(gh())=SOCIAL.github] organisation.
+
+    +aside-code("Example", "jade").
+        +a(gh("spacy", "spacy/language.py")) This is a link
+
+    +table(["Name", "Type", "Description"])
+        +row
+            +cell #[code repo]
+            +cell String
+            +cell Name of the repository, e.g. #[code "spacy"].
+
+        +row
+            +cell #[code filepath]
+            +cell String
+            +cell Logical path to the file, relative to the repository root.
+
+        +row
+            +cell #[code branch]
+            +cell String
+            +cell Optional branch. Defaults to #[code "master"].
+
+        +row("foot")
+            +cell returns
+            +cell String
+            +cell The full GitHub link to the file.

From b24fbd8aad5075af611a3f5dc624da57ba4d6983 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 14:54:33 +0200
Subject: [PATCH 170/213] Fix titles for social cards

---
 website/_layout.jade | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/website/_layout.jade b/website/_layout.jade
index 31c6ce6c3..49a025d96 100644
--- a/website/_layout.jade
+++ b/website/_layout.jade
@@ -3,6 +3,7 @@
 include _includes/_mixins
 
 - title = IS_MODELS ? LANGUAGES[current.source] || title : title
+- social_title = (SECTION == "index") ? SITENAME + " - " + SLOGAN : title + " - " + SITENAME
 - social_img = SITE_URL + "/assets/img/social/preview_" + (preview || ALPHA ? "alpha" : "default") + ".jpg"
 
 doctype html
@@ -26,13 +27,13 @@ html(lang="en")
     meta(property="og:type" content="website")
     meta(property="og:site_name" content=sitename)
     meta(property="og:url" content="#{SITE_URL}/#{current.path.join('/')}")
-    meta(property="og:title" content="#{title} - spaCy")
+    meta(property="og:title" content=social_title)
     meta(property="og:description" content=description)
     meta(property="og:image" content=social_img)
 
     meta(name="twitter:card" content="summary_large_image")
     meta(name="twitter:site" content="@" + SOCIAL.twitter)
-    meta(name="twitter:title" content="#{title} - spaCy")
+    meta(name="twitter:title" content=social_title)
     meta(name="twitter:description" content=description)
     meta(name="twitter:image" content=social_img)
 

From b0dfa059db69ecbbd5134779f86de1c72c2580d4 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 15:19:55 +0200
Subject: [PATCH 171/213] Update docs link in about.py

---
 spacy/about.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/about.py b/spacy/about.py
index 0ae019946..b8b1dbbf6 100644
--- a/spacy/about.py
+++ b/spacy/about.py
@@ -11,7 +11,7 @@ __email__ = 'contact@explosion.ai'
 __license__ = 'MIT'
 __release__ = False
 
-__docs_models__ = 'https://spacy.io/docs/usage/models'
+__docs_models__ = 'https://alpha.spacy.io/usage/models'
 __download_url__ = 'https://github.com/explosion/spacy-models/releases/download'
 __compatibility__ = 'https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json'
 __shortcuts__ = 'https://raw.githubusercontent.com/explosion/spacy-models/master/shortcuts.json'

From e49cd7aeaf81ed12490d82b8a65ca93088ec916e Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 15:22:19 +0200
Subject: [PATCH 172/213] Move import into load to avoid circular imports

---
 spacy/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/__init__.py b/spacy/__init__.py
index 1cb7c0cbd..9acc566ad 100644
--- a/spacy/__init__.py
+++ b/spacy/__init__.py
@@ -3,12 +3,12 @@ from __future__ import unicode_literals
 
 from .cli.info import info as cli_info
 from .glossary import explain
-from .deprecated import resolve_load_name
 from .about import __version__
 from . import util
 
 
 def load(name, **overrides):
+    from .deprecated import resolve_load_name
     name = resolve_load_name(name, **overrides)
     return util.load_model(name, **overrides)
 

From 5fb057b5754d1c4aa41c2d0c82f37bf89edd99ed Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 15:45:07 +0200
Subject: [PATCH 173/213] Fix secondary font stack

---
 website/assets/css/_variables.sass | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/assets/css/_variables.sass b/website/assets/css/_variables.sass
index 4fafbfca5..fbceb5a6f 100644
--- a/website/assets/css/_variables.sass
+++ b/website/assets/css/_variables.sass
@@ -21,7 +21,7 @@ $headings: (1: 4.4, 2: 3.4, 3: 2.6, 4: 2.2, 5: 1.8)
 //  Fonts
 
 $font-primary: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol" !default
-$font-secondary: "HK Grotesk" !default
+$font-secondary: "HK Grotesk", Roboto, Helvetica, Arial, sans-serif !default
 $font-code: Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace !default
 
 // Colors

From 02586a52431865a165439098bff8482cae96397a Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 09:14:34 -0500
Subject: [PATCH 174/213] Add timing to spacy evaluate command

---
 spacy/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/__init__.py b/spacy/__init__.py
index 1cb7c0cbd..58a2f10a6 100644
--- a/spacy/__init__.py
+++ b/spacy/__init__.py
@@ -4,7 +4,7 @@ from __future__ import unicode_literals
 from .cli.info import info as cli_info
 from .glossary import explain
 from .deprecated import resolve_load_name
-from .about import __version__
+#from .about import __version__
 from . import util
 
 

From 96da86b3e5d3a515f0f8db57ef1704750233ff38 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 09:14:57 -0500
Subject: [PATCH 175/213] Add support for verbose flag to Language

---
 spacy/language.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/spacy/language.py b/spacy/language.py
index 701b5c140..c49c64b1d 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -388,7 +388,7 @@ class Language(object):
         self._optimizer.device = device
         return self._optimizer
 
-    def evaluate(self, docs_golds):
+    def evaluate(self, docs_golds, verbose=False):
         scorer = Scorer()
         docs, golds = zip(*docs_golds)
         docs = list(docs)
@@ -401,7 +401,9 @@ class Language(object):
                 docs = list(pipe.pipe(docs))
         assert len(docs) == len(golds)
         for doc, gold in zip(docs, golds):
-            scorer.score(doc, gold)
+            if verbose:
+                print(doc)
+            scorer.score(doc, gold, verbose=verbose)
         return scorer
 
     @contextmanager

From a44c4c3a5b91dcf85681df57865942a888485a65 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 09:15:35 -0500
Subject: [PATCH 176/213] Add timer to evaluate

---
 spacy/cli/evaluate.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/spacy/cli/evaluate.py b/spacy/cli/evaluate.py
index 209660529..f409821b1 100644
--- a/spacy/cli/evaluate.py
+++ b/spacy/cli/evaluate.py
@@ -32,18 +32,25 @@ numpy.random.seed(0)
     model=("Model name or path", "positional", None, str),
     data_path=("Location of JSON-formatted evaluation data", "positional", None, str),
     gold_preproc=("Use gold preprocessing", "flag", "G", bool),
+    gpu_id=("Use GPU", "option", "g", int),
 )
-def evaluate(cmd, model, data_path, gold_preproc=False):
+def evaluate(cmd, model, data_path, gpu_id=-1, gold_preproc=False):
     """
     Train a model. Expects data in spaCy's JSON format.
     """
+    util.use_gpu(gpu_id)
     util.set_env_log(True)
     data_path = util.ensure_path(data_path)
     if not data_path.exists():
         prints(data_path, title="Evaluation data not found", exits=1)
     corpus = GoldCorpus(data_path, data_path)
     nlp = util.load_model(model)
-    scorer = nlp.evaluate(list(corpus.dev_docs(nlp, gold_preproc=gold_preproc)))
+    dev_docs = list(corpus.dev_docs(nlp, gold_preproc=gold_preproc))
+    begin = timer()
+    scorer = nlp.evaluate(dev_docs, verbose=False)
+    end = timer()
+    nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
+    print('Time', end-begin, 'words', nwords, 'w.p.s', nwords/(end-begin))
     print_results(scorer)
 
 

From 338e1fda0effda0b749926c38dec6f19a2dd6b6f Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 09:41:05 -0500
Subject: [PATCH 177/213] Unbreak merge artefact

---
 spacy/__init__.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/spacy/__init__.py b/spacy/__init__.py
index 25af17361..ba2479106 100644
--- a/spacy/__init__.py
+++ b/spacy/__init__.py
@@ -3,7 +3,6 @@ from __future__ import unicode_literals
 
 from .cli.info import info as cli_info
 from .glossary import explain
-<<<<<<< HEAD
 from .deprecated import resolve_load_name
 #from .about import __version__
 from .about import __version__

From e514d6aa0a0fe82a2ebc9cf4d867532769dcb26a Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 18:39:57 +0200
Subject: [PATCH 178/213] Import thinc modules more explicitly, to avoid cycles

---
 spacy/_ml.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 62fc7543f..77d6e0615 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -1,10 +1,12 @@
 import ujson
 from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
-from thinc.neural import Model, Maxout, Softmax, Affine
+from thinc.neural._classes.model import Model
+from thinc.neural._classes.maxout import Maxout
+from thinc.neural._classes.softmax import Softmax
+from thinc.neural._classes.affine import Affine
 from thinc.neural._classes.hash_embed import HashEmbed
 from thinc.neural.ops import NumpyOps, CupyOps
 from thinc.neural.util import get_array_module
-import thinc.extra.load_nlp
 import random
 import cytoolz
 
@@ -13,7 +15,7 @@ from thinc.neural._classes.static_vectors import StaticVectors
 from thinc.neural._classes.batchnorm import BatchNorm as BN
 from thinc.neural._classes.layernorm import LayerNorm as LN
 from thinc.neural._classes.resnet import Residual
-from thinc.neural import ReLu
+from thinc.neural._classes.relu import ReLu
 from thinc.neural._classes.selu import SELU
 from thinc import describe
 from thinc.describe import Dimension, Synapses, Biases, Gradient
@@ -23,6 +25,7 @@ from thinc.neural.pooling import Pooling, max_pool, mean_pool, sum_pool
 from thinc.neural._classes.attention import ParametricAttention
 from thinc.linear.linear import LinearModel
 from thinc.api import uniqued, wrap, flatten_add_lengths, noop
+import thinc.extra.load_nlp
 
 
 from .attrs import ID, ORTH, LOWER, NORM, PREFIX, SUFFIX, SHAPE, TAG, DEP, CLUSTER

From cbb1fbef80a15fe2f8415cd698d9f8b78c48ef04 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 18:38:55 +0200
Subject: [PATCH 179/213] Update train_ner_standalone example

---
 examples/training/train_ner_standalone.py | 19 +++++++------------
 1 file changed, 7 insertions(+), 12 deletions(-)

diff --git a/examples/training/train_ner_standalone.py b/examples/training/train_ner_standalone.py
index 6cca56c69..e4fb1d1e8 100644
--- a/examples/training/train_ner_standalone.py
+++ b/examples/training/train_ner_standalone.py
@@ -20,9 +20,10 @@ import plac
 from pathlib import Path
 import random
 import json
+import tqdm
+
 from thinc.neural.optimizers import Adam
 from thinc.neural.ops import NumpyOps
-import tqdm
 
 from spacy.vocab import Vocab
 from spacy.pipeline import TokenVectorEncoder, NeuralEntityRecognizer
@@ -35,6 +36,7 @@ from spacy.gold import minibatch
 from spacy.scorer import Scorer
 import spacy.util
 
+
 try:
     unicode
 except NameError:
@@ -55,20 +57,17 @@ def init_vocab():
 
 
 class Pipeline(object):
-    def __init__(self, vocab=None, tokenizer=None, tensorizer=None, entity=None):
+    def __init__(self, vocab=None, tokenizer=None, entity=None):
         if vocab is None:
             vocab = init_vocab()
         if tokenizer is None:
             tokenizer = Tokenizer(vocab, {}, None, None, None)
-        if tensorizer is None:
-            tensorizer = TokenVectorEncoder(vocab)
         if entity is None:
             entity = NeuralEntityRecognizer(vocab)
         self.vocab = vocab
         self.tokenizer = tokenizer
-        self.tensorizer = tensorizer
         self.entity = entity
-        self.pipeline = [tensorizer, self.entity]
+        self.pipeline = [self.entity]
 
     def begin_training(self):
         for model in self.pipeline:
@@ -102,10 +101,8 @@ class Pipeline(object):
         golds = [self.make_gold(input_, annot) for input_, annot in
                  zip(inputs, annots)]
 
-        tensors, bp_tensors = self.tensorizer.update(docs, golds, drop=drop)
-        d_tensors = self.entity.update((docs, tensors), golds, drop=drop,
-                                      sgd=sgd, losses=losses)
-        bp_tensors(d_tensors, sgd=sgd)
+        self.entity.update(docs, golds, drop=drop,
+                           sgd=sgd, losses=losses)
         return losses
 
     def evaluate(self, examples):
@@ -123,7 +120,6 @@ class Pipeline(object):
         elif not path.is_dir():
             raise IOError("Can't save pipeline to %s\nNot a directory" % path)
         self.vocab.to_disk(path / 'vocab')
-        self.tensorizer.to_disk(path / 'tensorizer')
         self.entity.to_disk(path / 'ner')
 
     def from_disk(self, path):
@@ -133,7 +129,6 @@ class Pipeline(object):
         if not path.is_dir():
             raise IOError("Cannot load pipeline from %s\nNot a directory" % path)
         self.vocab = self.vocab.from_disk(path / 'vocab')
-        self.tensorizer = self.tensorizer.from_disk(path / 'tensorizer')
         self.entity = self.entity.from_disk(path / 'ner')
 
 

From 4a59f6358cfb1926f363b5c094ce5d9d47608928 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 19:21:26 +0200
Subject: [PATCH 180/213] Fix thinc imports

---
 spacy/pipeline.pyx         | 6 ++++--
 spacy/syntax/nn_parser.pyx | 5 ++++-
 spacy/tests/test_misc.py   | 3 ++-
 3 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index 1a12107b7..f6ee257d8 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -4,7 +4,6 @@
 from __future__ import unicode_literals
 
 from thinc.api import chain, layerize, with_getitem
-from thinc.neural import Model, Softmax
 import numpy
 cimport numpy as np
 import cytoolz
@@ -14,7 +13,10 @@ import ujson
 import msgpack
 
 from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
-from thinc.neural import Model, Maxout, Softmax, Affine
+from thinc.neural._classes.model import Model
+from thinc.neural._classes.maxout import Maxout
+from thinc.neural._classes.softmax import Softmax
+from thinc.neural._classes.affine import Affine
 from thinc.neural._classes.hash_embed import HashEmbed
 from thinc.neural.util import to_categorical
 
diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 1efdc4474..4043d6dd3 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -38,7 +38,10 @@ from preshed.maps cimport MapStruct
 from preshed.maps cimport map_get
 
 from thinc.api import layerize, chain, noop, clone, with_flatten
-from thinc.neural import Model, Affine, ReLu, Maxout
+from thinc.neural._classes.model import Model
+from thinc.neural._classes.affine import Affine
+from thinc.neural._classes.relu import ReLu
+from thinc.neural._classes.maxout import Maxout
 from thinc.neural._classes.batchnorm import BatchNorm as BN
 from thinc.neural._classes.selu import SELU
 from thinc.neural._classes.layernorm import LayerNorm
diff --git a/spacy/tests/test_misc.py b/spacy/tests/test_misc.py
index 80b859c70..762ea4c08 100644
--- a/spacy/tests/test_misc.py
+++ b/spacy/tests/test_misc.py
@@ -9,7 +9,8 @@ from .util import get_doc
 
 from pathlib import Path
 import pytest
-from thinc.neural import Maxout, Softmax
+from thinc.neural._classes.maxout import Maxout
+from thinc.neural._classes.softmax import Softmax
 from thinc.api import chain
 
 

From 80a2fb619316b205d3688c608e8027f69bb387a2 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Tue, 3 Oct 2017 19:40:39 +0200
Subject: [PATCH 181/213] Update visualizers docs and add submenu

---
 website/usage/_data.json                 |  10 +-
 website/usage/_visualizers/_dep.jade     |  62 ++++
 website/usage/_visualizers/_ent.jade     |  80 +++++
 website/usage/_visualizers/_html.jade    | 162 +++++++++
 website/usage/_visualizers/_jupyter.jade |  36 ++
 website/usage/visualizers.jade           | 424 +++--------------------
 6 files changed, 393 insertions(+), 381 deletions(-)
 create mode 100644 website/usage/_visualizers/_dep.jade
 create mode 100644 website/usage/_visualizers/_ent.jade
 create mode 100644 website/usage/_visualizers/_html.jade
 create mode 100644 website/usage/_visualizers/_jupyter.jade

diff --git a/website/usage/_data.json b/website/usage/_data.json
index 3c37ee4d1..b34304ed6 100644
--- a/website/usage/_data.json
+++ b/website/usage/_data.json
@@ -167,7 +167,15 @@
 
     "visualizers": {
         "title": "Visualizers",
-        "next": "resources"
+        "tag_new": 2,
+        "teaser": "Visualize dependencies and entities in your browser and notebook, or export HTML.",
+        "next": "resources",
+        "menu": {
+            "Dependencies": "dep",
+            "Entities": "ent",
+            "Jupyter Notebooks": "jupyter",
+            "Rendering HTML": "html"
+        }
     },
 
     "resources": {
diff --git a/website/usage/_visualizers/_dep.jade b/website/usage/_visualizers/_dep.jade
new file mode 100644
index 000000000..b028ba4cf
--- /dev/null
+++ b/website/usage/_visualizers/_dep.jade
@@ -0,0 +1,62 @@
+//- 💫 DOCS > USAGE > VISUALIZERS > DEPENDENCIES
+
+p
+    |  The dependency visualizer, #[code dep], shows part-of-speech tags
+    |  and syntactic dependencies.
+
++code("Dependency example").
+    import spacy
+    from spacy import displacy
+
+    nlp = spacy.load('en')
+    doc = nlp(u'This is a sentence.')
+    displacy.serve(doc, style='dep')
+
++codepen("f0e85b64d469d6617251d8241716d55f", 370)
+
+p
+    |  The argument #[code options] lets you specify a dictionary of settings
+    |  to customise the layout, for example:
+
++aside("Important note")
+    |  There's currently a known issue with the #[code compact] mode for
+    |  sentences with short arrows and long dependency labels, that causes labels
+    |  longer than the arrow to wrap. So if you come across this problem,
+    |  especially when using custom labels, you'll have to increase the
+    |  #[code distance] setting in the #[code options] to allow longer arcs.
+
++table(["Name", "Type", "Description", "Default"])
+    +row
+        +cell #[code compact]
+        +cell bool
+        +cell "Compact mode" with square arrows that takes up less space.
+        +cell #[code False]
+
+    +row
+        +cell #[code color]
+        +cell unicode
+        +cell Text color (HEX, RGB or color names).
+        +cell #[code '#000000']
+
+    +row
+        +cell #[code bg]
+        +cell unicode
+        +cell Background color (HEX, RGB or color names).
+        +cell #[code '#ffffff']
+
+    +row
+        +cell #[code font]
+        +cell unicode
+        +cell Font name or font family for all text.
+        +cell #[code 'Arial']
+
+p
+    |  For a list of all available options, see the
+    |  #[+api("displacy#options") #[code displacy] API documentation].
+
++aside-code("Options example").
+    options = {'compact': True, 'bg': '&#35;09a3d5',
+               'color': 'white', 'font': 'Source Sans Pro'}
+    displacy.serve(doc, style='dep', options=options)
+
++codepen("39c02c893a84794353de77a605d817fd", 360)
diff --git a/website/usage/_visualizers/_ent.jade b/website/usage/_visualizers/_ent.jade
new file mode 100644
index 000000000..e9174cc55
--- /dev/null
+++ b/website/usage/_visualizers/_ent.jade
@@ -0,0 +1,80 @@
+//- 💫 DOCS > USAGE > VISUALIZERS > ENTITIES
+
+p
+    |  The entity visualizer, #[code ent], highlights named entities and
+    |  their labels in a text.
+
++code("Named Entity example").
+    import spacy
+    from spacy import displacy
+
+    text = """But Google is starting from behind. The company made a late push
+    into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa
+    software, which runs on its Echo and Dot devices, have clear leads in
+    consumer adoption."""
+
+    nlp = spacy.load('custom_ner_model')
+    doc = nlp(text)
+    displacy.serve(doc, style='ent')
+
++codepen("a73f8b68f9af3157855962b283b364e4", 345)
+
+p The entity visualizer lets you customise the following #[code options]:
+
++table(["Name", "Type", "Description", "Default"])
+    +row
+        +cell #[code ents]
+        +cell list
+        +cell
+            |  Entity types to highlight (#[code None] for all types).
+        +cell #[code None]
+
+    +row
+        +cell #[code colors]
+        +cell dict
+        +cell
+            |  Color overrides. Entity types in lowercase should be mapped to
+            |  color names or values.
+        +cell #[code {}]
+
+p
+    |  If you specify a list of #[code ents], only those entity types will be
+    |  rendered – for example, you can choose to display #[code PERSON] entities.
+    |  Internally, the visualizer knows nothing about available entity types and
+    |  will render whichever spans and labels it receives. This makes it
+    |  especially easy to work with custom entity types. By default, displaCy
+    |  comes with colours for all
+    |  #[+a("/api/annotation#named-entities") entity types supported by spaCy].
+    |  If you're using custom entity types, you can use the #[code colors]
+    |  setting to add your own colours for them.
+
++aside-code("Options example").
+    colors = {'ORG': 'linear-gradient(90deg, #aa9cfc, #fc9ce7)'}
+    options = {'ents': ['ORG'], 'colors': colors}
+    displacy.serve(doc, style='ent', options=options)
+
++codepen("f42ec690762b6f007022a7acd6d0c7d4", 300)
+
+p
+    |  The above example uses a little trick: Since the background colour values
+    |  are added as the #[code background] style attribute, you can use any
+    |  #[+a("https://tympanus.net/codrops/css_reference/background/") valid background value]
+    |  or shorthand — including gradients and even images!
+
++h(3, "ent-titles") Adding titles to documents
+
+p
+    |  Rendering several large documents on one page can easily become confusing.
+    |  To add a headline to each visualization, you can add a #[code title] to
+    |  its #[code user_data]. User data is never touched or modified by spaCy.
+
++code.
+    doc = nlp(u'This is a sentence about Google.')
+    doc.user_data['title'] = 'This is a title'
+    displacy.serve(doc, style='ent')
+
+p
+    |  This feature is espeically handy if you're using displaCy to compare
+    |  performance at different stages of a process, e.g. during training. Here
+    |  you could use the title for a brief description of the text example and
+    |  the number of iterations.
diff --git a/website/usage/_visualizers/_html.jade b/website/usage/_visualizers/_html.jade
new file mode 100644
index 000000000..701d4b683
--- /dev/null
+++ b/website/usage/_visualizers/_html.jade
@@ -0,0 +1,162 @@
+//- 💫 DOCS > USAGE > VISUALIZERS > HTML
+
+p
+    |  If you don't need the web server and just want to generate the markup
+    |  – for example, to export it to a file or serve it in a custom
+    |  way – you can use #[+api("displacy#render") #[code displacy.render]].
+    |  It works the same way, but returns a string containing the markup.
+
++code("Example").
+    import spacy
+    from spacy import displacy
+
+    nlp = spacy.load('en')
+    doc1 = nlp(u'This is a sentence.')
+    doc2 = nlp(u'This is another sentence.')
+    html = displacy.render([doc1, doc2], style='dep', page=True)
+
+p
+    |  #[code page=True] renders the markup wrapped as a full HTML page.
+    |  For minified and more compact HTML markup, you can set #[code minify=True].
+    |  If you're rendering a dependency parse, you can also export it as an
+    |  #[code .svg] file.
+
++aside("What's SVG?")
+    |  Unlike other image formats, the SVG (Scalable Vector Graphics) uses XML
+    |  markup that's easy to manipulate
+    |  #[+a("https://www.smashingmagazine.com/2014/11/styling-and-animating-svgs-with-css/") using CSS] or
+    |  #[+a("https://css-tricks.com/smil-is-dead-long-live-smil-a-guide-to-alternatives-to-smil-features/") JavaScript].
+    |  Essentially, SVG lets you design with code, which makes it a perfect fit
+    |  for visualizing dependency trees. SVGs can be embedded online in an
+    |  #[code &lt;img&gt;] tag, or inlined in an HTML document. They're also
+    |  pretty easy to #[+a("https://convertio.co/image-converter/") convert].
+
++code.
+    svg = displacy.render(doc, style='dep')
+    output_path = Path('/images/sentence.svg')
+    output_path.open('w', encoding='utf-8').write(svg)
+
++infobox("Important note")
+    |  Since each visualization is generated as a separate SVG, exporting
+    |  #[code .svg] files only works if you're rendering #[strong one single doc]
+    |  at a time. (This makes sense – after all, each visualization should be
+    |  a standalone graphic.) So instead of rendering all #[code Doc]s at one,
+    |  loop over them and export them separately.
+
+
++h(3, "examples-export-svg") Example: Export SVG graphics of dependency parses
+
++code("Example").
+    import spacy
+    from spacy import displacy
+    from pathlib import Path
+
+    nlp = spacy.load('en')
+    sentences = ["This is an example.", "This is another one."]
+    for sent in sentences:
+        doc = nlp(sentence)
+        svg = displacy.render(doc, style='dep')
+        file_name = '-'.join([w.text for w in doc if not w.is_punct]) + '.svg'
+        output_path = Path('/images/' + file_name)
+        output_path.open('w', encoding='utf-8').write(svg)
+
+p
+    |  The above code will generate the dependency visualizations and them to
+    |  two files, #[code This-is-an-example.svg] and #[code This-is-another-one.svg].
+
+
++h(3, "manual-usage") Rendering data manually
+
+p
+    |  You can also use displaCy to manually render data. This can be useful if
+    |  you want to visualize output from other libraries, like
+    |  #[+a("http://www.nltk.org") NLTK] or
+    |  #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet].
+    |  Simply convert the dependency parse or recognised entities to displaCy's
+    |  format and set #[code manual=True] on either #[code render()] or
+    |  #[code serve()].
+
++aside-code("Example").
+    ex = [{'text': 'But Google is starting from behind.',
+           'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
+           'title': None}]
+    html = displacy.render(ex, style='ent', manual=True)
+
++code("DEP input").
+    {
+        'words': [
+            {'text': 'This', 'tag': 'DT'},
+            {'text': 'is', 'tag': 'VBZ'},
+            {'text': 'a', 'tag': 'DT'},
+            {'text': 'sentence', 'tag': 'NN'}],
+        'arcs': [
+            {'start': 0, 'end': 1, 'label': 'nsubj', 'dir': 'left'},
+            {'start': 2, 'end': 3, 'label': 'det', 'dir': 'left'},
+            {'start': 1, 'end': 3, 'label': 'attr', 'dir': 'right'}]
+    }
+
++code("ENT input").
+    {
+        'text': 'But Google is starting from behind.',
+        'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
+        'title': None
+    }
+
++h(3, "webapp") Using displaCy in a web application
+
+p
+    |  If you want to use the visualizers as part of a web application, for
+    |  example to create something like our
+    |  #[+a(DEMOS_URL + "/displacy") online demo], it's not recommended to
+    |  simply wrap and serve the displaCy renderer. Instead, you should only
+    |  rely on the server to perform spaCy's processing capabilities, and use
+    |  #[+a(gh("displacy")) displaCy.js] to render the JSON-formatted output.
+
++aside("Why not return the HTML by the server?")
+    |  It's certainly possible to just have your server return the markup.
+    |  But outputting raw, unsanitised HTML is risky and makes your app vulnerable to
+    |  #[+a("https://en.wikipedia.org/wiki/Cross-site_scripting") cross-site scripting]
+    |  (XSS). All your user needs to do is find a way to make spaCy return text
+    |  like #[code &lt;script src="malicious-code.js"&gt;&lt;script&gt;], which
+    |  is pretty easy in NER mode. Instead of relying on the server to render
+    |  and sanitise HTML, you can do this on the client in JavaScript.
+    |  displaCy.js creates the markup as DOM nodes and will never insert raw
+    |  HTML.
+
+p
+    |  The #[code parse_deps] function takes a #[code Doc] object and returns
+    |  a dictionary in a format that can be rendered by displaCy.
+
++code("Example").
+    import spacy
+    from spacy import displacy
+
+    nlp = spacy.load('en')
+
+    def displacy_service(text):
+        doc = nlp(text)
+        return displacy.parse_deps(doc)
+
+p
+    |  Using a library like #[+a("https://falconframework.org/") Falcon] or
+    |  #[+a("http://www.hug.rest/") Hug], you can easily turn the above code
+    |  into a simple REST API that receives a text and returns a JSON-formatted
+    |  parse. In your front-end, include #[+a(gh("displacy")) displacy.js] and
+    |  initialise it with the API URL and the ID or query selector of the
+    |  container to render the visualisation in, e.g. #[code '#displacy'] for
+    |  #[code &lt;div id="displacy"&gt;].
+
++code("script.js", "javascript").
+    var displacy = new displaCy('http://localhost:8080', {
+        container: '#displacy'
+    })
+
+    function parse(text) {
+        displacy.parse(text);
+    }
+
+p
+    |  When you call #[code parse()], it will make a request to your API,
+    |  receive the JSON-formatted parse and render it in your container. To
+    |  create an interactive experience, you could trigger this function by
+    |  a button and read the text from an #[code &lt;input&gt;] field.
diff --git a/website/usage/_visualizers/_jupyter.jade b/website/usage/_visualizers/_jupyter.jade
new file mode 100644
index 000000000..f7227e4d1
--- /dev/null
+++ b/website/usage/_visualizers/_jupyter.jade
@@ -0,0 +1,36 @@
+//- 💫 DOCS > USAGE > VISUALIZERS > JUPYTER
+
+p
+    |  displaCy is able to detect whether you're working in a
+    |  #[+a("https://jupyter.org") Jupyter] notebook, and will return markup
+    |  that can be rendered in a cell straight away. When you export your
+    |  notebook, the visualizations will be included as HTML.
+
++code("Jupyter Example").
+    # don't forget to install a model, e.g.: spacy download en
+    import spacy
+    from spacy import displacy
+
+    doc = nlp(u'Rats are various medium-sized, long-tailed rodents.')
+    displacy.render(doc, style='dep')
+
+    doc2 = nlp(LONG_NEWS_ARTICLE)
+    displacy.render(doc2, style='ent')
+
++aside("Enabling or disabling Jupyter mode")
+    |  To explicitly enable or disable "Jupyter mode", you can use the
+    |  #[code jupyter] keyword argument – e.g. to return raw HTML in a notebook,
+    |  or to force Jupyter rendering if auto-detection fails.
+
++image("/assets/img/displacy_jupyter.jpg", 700, false, "Example of using the displaCy dependency and named entity visualizer in a Jupyter notebook")
+
+p
+    |  Internally, displaCy imports #[code display] and #[code HTML] from
+    |  #[code IPython.core.display] and returns a Jupyter HTML object. If you
+    |  were doing it manually, it'd look like this:
+
++code.
+    from IPython.core.display import display, HTML
+
+    html = displacy.render(doc, style='dep')
+    return display(HTML(html))
diff --git a/website/usage/visualizers.jade b/website/usage/visualizers.jade
index 39d34aea6..a092404ac 100644
--- a/website/usage/visualizers.jade
+++ b/website/usage/visualizers.jade
@@ -2,383 +2,47 @@
 
 include ../_includes/_mixins
 
-p
-    |  As of v2.0, our popular visualizers, #[+a(DEMOS_URL + "/displacy") displaCy]
-    |  and #[+a(DEMOS_URL + "/displacy-ent") displaCy #[sup ENT]] are finally an
-    |  official part of the library. Visualizing a dependency parse or named
-    |  entities in a text is not only a fun NLP demo – it can also be incredibly
-    |  helpful in speeding up development and debugging your code and training
-    |  process. Instead of printing a list of dependency labels or entity spans,
-    |  you can simply pass your #[code Doc] objects to #[code displacy] and view
-    |  the visualizations in your browser, or export them as HTML files or
-    |  vector graphics.
-
-p
-    |  If you're running a #[+a("https://jupyter.org") Jupyter] notebook,
-    |  displaCy will detect this and return the markup in a format
-    |  #[+a("#jupyter") ready to be rendered and exported].
-
-+aside("What about the old visualizers?")
-    |  Our JavaScript-based visualizers #[+src(gh("displacy")) #[code displacy.js]] and
-    |  #[+src(gh("displacy-ent")) #[code displacy-ent.js]] will still be available on
-    |  GitHub. If you're looking to implement web-based visualizations, we
-    |  generally recommend using those instead of spaCy's built-in
-    |  #[code displacy] module. It'll allow your application to perform all
-    |  rendering on the client and only rely on the server for the text
-    |  processing. The generated markup is also more compatible with modern web
-    |  standards.
-
-+h(2, "getting-started") Getting started
-    +tag-new(2)
-
-p
-    |  The quickest way visualize  #[code Doc] is to use
-    |  #[+api("displacy#serve") #[code displacy.serve]]. This will spin up a
-    |  simple web server and let you view the result straight from your browser.
-    |  displaCy can either take a single #[code Doc] or a list of #[code Doc]
-    |  objects as its first argument. This lets you construct them however you
-    |  like – using any model or modifications you like.
-
-+h(3, "dep") Visualizing the dependency parse
-
-p
-    |  The dependency visualizer, #[code dep], shows part-of-speech tags
-    |  and syntactic dependencies.
-
-+code("Dependency example").
-    import spacy
-    from spacy import displacy
-
-    nlp = spacy.load('en')
-    doc = nlp(u'This is a sentence.')
-    displacy.serve(doc, style='dep')
-
-+codepen("f0e85b64d469d6617251d8241716d55f", 370)
-
-p
-    |  The argument #[code options] lets you specify a dictionary of settings
-    |  to customise the layout, for example:
-
-+aside("Important note")
-    |  There's currently a known issue with the #[code compact] mode for
-    |  sentences with short arrows and long dependency labels, that causes labels
-    |  longer than the arrow to wrap. So if you come across this problem,
-    |  especially when using custom labels, you'll have to increase the
-    |  #[code distance] setting in the #[code options] to allow longer arcs.
-
-+table(["Name", "Type", "Description", "Default"])
-    +row
-        +cell #[code compact]
-        +cell bool
-        +cell "Compact mode" with square arrows that takes up less space.
-        +cell #[code False]
-
-    +row
-        +cell #[code color]
-        +cell unicode
-        +cell Text color (HEX, RGB or color names).
-        +cell #[code '#000000']
-
-    +row
-        +cell #[code bg]
-        +cell unicode
-        +cell Background color (HEX, RGB or color names).
-        +cell #[code '#ffffff']
-
-    +row
-        +cell #[code font]
-        +cell unicode
-        +cell Font name or font family for all text.
-        +cell #[code 'Arial']
-
-p
-    |  For a list of all available options, see the
-    |  #[+api("displacy#options") #[code displacy] API documentation].
-
-+aside-code("Options example").
-    options = {'compact': True, 'bg': '&#35;09a3d5',
-               'color': 'white', 'font': 'Source Sans Pro'}
-    displacy.serve(doc, style='dep', options=options)
-
-+codepen("39c02c893a84794353de77a605d817fd", 360)
-
-+h(3, "ent") Visualizing the entity recognizer
-
-p
-    |  The entity visualizer, #[code ent], highlights named entities and
-    |  their labels in a text.
-
-+code("Named Entity example").
-    import spacy
-    from spacy import displacy
-
-    text = """But Google is starting from behind. The company made a late push
-    into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa
-    software, which runs on its Echo and Dot devices, have clear leads in
-    consumer adoption."""
-
-    nlp = spacy.load('custom_ner_model')
-    doc = nlp(text)
-    displacy.serve(doc, style='ent')
-
-+codepen("a73f8b68f9af3157855962b283b364e4", 345)
-
-p The entity visualizer lets you customise the following #[code options]:
-
-+table(["Name", "Type", "Description", "Default"])
-    +row
-        +cell #[code ents]
-        +cell list
-        +cell
-            |  Entity types to highlight (#[code None] for all types).
-        +cell #[code None]
-
-    +row
-        +cell #[code colors]
-        +cell dict
-        +cell
-            |  Color overrides. Entity types in lowercase should be mapped to
-            |  color names or values.
-        +cell #[code {}]
-
-p
-    |  If you specify a list of #[code ents], only those entity types will be
-    |  rendered – for example, you can choose to display #[code PERSON] entities.
-    |  Internally, the visualizer knows nothing about available entity types and
-    |  will render whichever spans and labels it receives. This makes it
-    |  especially easy to work with custom entity types. By default, displaCy
-    |  comes with colours for all
-    |  #[+a("/api/annotation#named-entities") entity types supported by spaCy].
-    |  If you're using custom entity types, you can use the #[code colors]
-    |  setting to add your own colours for them.
-
-+aside-code("Options example").
-    colors = {'ORG': 'linear-gradient(90deg, #aa9cfc, #fc9ce7)'}
-    options = {'ents': ['ORG'], 'colors': colors}
-    displacy.serve(doc, style='ent', options=options)
-
-+codepen("f42ec690762b6f007022a7acd6d0c7d4", 300)
-
-p
-    |  The above example uses a little trick: Since the background colour values
-    |  are added as the #[code background] style attribute, you can use any
-    |  #[+a("https://tympanus.net/codrops/css_reference/background/") valid background value]
-    |  or shorthand — including gradients and even images!
-
-+h(3, "ent-titles") Adding titles to documents
-
-p
-    |  Rendering several large documents on one page can easily become confusing.
-    |  To add a headline to each visualization, you can add a #[code title] to
-    |  its #[code user_data]. User data is never touched or modified by spaCy.
-
-+code.
-    doc = nlp(u'This is a sentence about Google.')
-    doc.user_data['title'] = 'This is a title'
-    displacy.serve(doc, style='ent')
-
-p
-    |  This feature is espeically handy if you're using displaCy to compare
-    |  performance at different stages of a process, e.g. during training. Here
-    |  you could use the title for a brief description of the text example and
-    |  the number of iterations.
-
-+h(2, "render") Rendering visualizations
-
-p
-    |  If you don't need the web server and just want to generate the markup
-    |  – for example, to export it to a file or serve it in a custom
-    |  way – you can use #[+api("displacy#render") #[code displacy.render]].
-    |  It works the same way, but returns a string containing the markup.
-
-+code("Example").
-    import spacy
-    from spacy import displacy
-
-    nlp = spacy.load('en')
-    doc1 = nlp(u'This is a sentence.')
-    doc2 = nlp(u'This is another sentence.')
-    html = displacy.render([doc1, doc2], style='dep', page=True)
-
-p
-    |  #[code page=True] renders the markup wrapped as a full HTML page.
-    |  For minified and more compact HTML markup, you can set #[code minify=True].
-    |  If you're rendering a dependency parse, you can also export it as an
-    |  #[code .svg] file.
-
-+aside("What's SVG?")
-    |  Unlike other image formats, the SVG (Scalable Vector Graphics) uses XML
-    |  markup that's easy to manipulate
-    |  #[+a("https://www.smashingmagazine.com/2014/11/styling-and-animating-svgs-with-css/") using CSS] or
-    |  #[+a("https://css-tricks.com/smil-is-dead-long-live-smil-a-guide-to-alternatives-to-smil-features/") JavaScript].
-    |  Essentially, SVG lets you design with code, which makes it a perfect fit
-    |  for visualizing dependency trees. SVGs can be embedded online in an
-    |  #[code &lt;img&gt;] tag, or inlined in an HTML document. They're also
-    |  pretty easy to #[+a("https://convertio.co/image-converter/") convert].
-
-+code.
-    svg = displacy.render(doc, style='dep')
-    output_path = Path('/images/sentence.svg')
-    output_path.open('w', encoding='utf-8').write(svg)
-
-+infobox("Important note")
-    |  Since each visualization is generated as a separate SVG, exporting
-    |  #[code .svg] files only works if you're rendering #[strong one single doc]
-    |  at a time. (This makes sense – after all, each visualization should be
-    |  a standalone graphic.) So instead of rendering all #[code Doc]s at one,
-    |  loop over them and export them separately.
-
-
-+h(3, "examples-export-svg") Example: Export SVG graphics of dependency parses
-
-+code("Example").
-    import spacy
-    from spacy import displacy
-    from pathlib import Path
-
-    nlp = spacy.load('en')
-    sentences = ["This is an example.", "This is another one."]
-    for sent in sentences:
-        doc = nlp(sentence)
-        svg = displacy.render(doc, style='dep')
-        file_name = '-'.join([w.text for w in doc if not w.is_punct]) + '.svg'
-        output_path = Path('/images/' + file_name)
-        output_path.open('w', encoding='utf-8').write(svg)
-
-p
-    |  The above code will generate the dependency visualizations and them to
-    |  two files, #[code This-is-an-example.svg] and #[code This-is-another-one.svg].
-
-
-+h(2, "jupyter") Using displaCy in Jupyter notebooks
-
-p
-    |  displaCy is able to detect whether you're working in a
-    |  #[+a("https://jupyter.org") Jupyter] notebook, and will return markup
-    |  that can be rendered in a cell straight away. When you export your
-    |  notebook, the visualizations will be included as HTML.
-
-+code("Jupyter Example").
-    # don't forget to install a model, e.g.: spacy download en
-    import spacy
-    from spacy import displacy
-
-    doc = nlp(u'Rats are various medium-sized, long-tailed rodents.')
-    displacy.render(doc, style='dep')
-
-    doc2 = nlp(LONG_NEWS_ARTICLE)
-    displacy.render(doc2, style='ent')
-
-+aside("Enabling or disabling Jupyter mode")
-    |  To explicitly enable or disable "Jupyter mode", you can use the
-    |  #[code jupyter] keyword argument – e.g. to return raw HTML in a notebook,
-    |  or to force Jupyter rendering if auto-detection fails.
-
-+image("/assets/img/displacy_jupyter.jpg", 700, false, "Example of using the displaCy dependency and named entity visualizer in a Jupyter notebook")
-
-p
-    |  Internally, displaCy imports #[code display] and #[code HTML] from
-    |  #[code IPython.core.display] and returns a Jupyter HTML object. If you
-    |  were doing it manually, it'd look like this:
-
-+code.
-    from IPython.core.display import display, HTML
-
-    html = displacy.render(doc, style='dep')
-    return display(HTML(html))
-
-+h(2, "manual-usage") Rendering data manually
-
-p
-    |  You can also use displaCy to manually render data. This can be useful if
-    |  you want to visualize output from other libraries, like
-    |  #[+a("http://www.nltk.org") NLTK] or
-    |  #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet].
-    |  Simply convert the dependency parse or recognised entities to displaCy's
-    |  format and set #[code manual=True] on either #[code render()] or
-    |  #[code serve()].
-
-+aside-code("Example").
-    ex = [{'text': 'But Google is starting from behind.',
-           'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
-           'title': None}]
-    html = displacy.render(ex, style='ent', manual=True)
-
-+code("DEP input").
-    {
-        'words': [
-            {'text': 'This', 'tag': 'DT'},
-            {'text': 'is', 'tag': 'VBZ'},
-            {'text': 'a', 'tag': 'DT'},
-            {'text': 'sentence', 'tag': 'NN'}],
-        'arcs': [
-            {'start': 0, 'end': 1, 'label': 'nsubj', 'dir': 'left'},
-            {'start': 2, 'end': 3, 'label': 'det', 'dir': 'left'},
-            {'start': 1, 'end': 3, 'label': 'attr', 'dir': 'right'}]
-    }
-
-+code("ENT input").
-    {
-        'text': 'But Google is starting from behind.',
-        'ents': [{'start': 4, 'end': 10, 'label': 'ORG'}],
-        'title': None
-    }
-
-+h(2, "webapp") Using displaCy in a web application
-
-p
-    |  If you want to use the visualizers as part of a web application, for
-    |  example to create something like our
-    |  #[+a(DEMOS_URL + "/displacy") online demo], it's not recommended to
-    |  simply wrap and serve the displaCy renderer. Instead, you should only
-    |  rely on the server to perform spaCy's processing capabilities, and use
-    |  #[+a(gh("displacy")) displaCy.js] to render the JSON-formatted output.
-
-+aside("Why not return the HTML by the server?")
-    |  It's certainly possible to just have your server return the markup.
-    |  But outputting raw, unsanitised HTML is risky and makes your app vulnerable to
-    |  #[+a("https://en.wikipedia.org/wiki/Cross-site_scripting") cross-site scripting]
-    |  (XSS). All your user needs to do is find a way to make spaCy return text
-    |  like #[code &lt;script src="malicious-code.js"&gt;&lt;script&gt;], which
-    |  is pretty easy in NER mode. Instead of relying on the server to render
-    |  and sanitise HTML, you can do this on the client in JavaScript.
-    |  displaCy.js creates the markup as DOM nodes and will never insert raw
-    |  HTML.
-
-p
-    |  The #[code parse_deps] function takes a #[code Doc] object and returns
-    |  a dictionary in a format that can be rendered by displaCy.
-
-+code("Example").
-    import spacy
-    from spacy import displacy
-
-    nlp = spacy.load('en')
-
-    def displacy_service(text):
-        doc = nlp(text)
-        return displacy.parse_deps(doc)
-
-p
-    |  Using a library like #[+a("https://falconframework.org/") Falcon] or
-    |  #[+a("http://www.hug.rest/") Hug], you can easily turn the above code
-    |  into a simple REST API that receives a text and returns a JSON-formatted
-    |  parse. In your front-end, include #[+a(gh("displacy")) displacy.js] and
-    |  initialise it with the API URL and the ID or query selector of the
-    |  container to render the visualisation in, e.g. #[code '#displacy'] for
-    |  #[code &lt;div id="displacy"&gt;].
-
-+code("script.js", "javascript").
-    var displacy = new displaCy('http://localhost:8080', {
-        container: '#displacy'
-    })
-
-    function parse(text) {
-        displacy.parse(text);
-    }
-
-p
-    |  When you call #[code parse()], it will make a request to your API,
-    |  receive the JSON-formatted parse and render it in your container. To
-    |  create an interactive experience, you could trigger this function by
-    |  a button and read the text from an #[code &lt;input&gt;] field.
++section
+    p
+        |  As of v2.0, our popular visualizers, #[+a(DEMOS_URL + "/displacy") displaCy]
+        |  and #[+a(DEMOS_URL + "/displacy-ent") displaCy #[sup ENT]] are finally an
+        |  official part of the library. Visualizing a dependency parse or named
+        |  entities in a text is not only a fun NLP demo – it can also be incredibly
+        |  helpful in speeding up development and debugging your code and training
+        |  process. If you're running a #[+a("https://jupyter.org") Jupyter] notebook,
+        |  displaCy will detect this and return the markup in a format
+        |  #[+a("#jupyter") ready to be rendered and exported].
+
+    +aside("What about the old visualizers?")
+        |  Our JavaScript-based visualizers #[+src(gh("displacy")) #[code displacy.js]] and
+        |  #[+src(gh("displacy-ent")) #[code displacy-ent.js]] will still be available on
+        |  GitHub. If you're looking to implement web-based visualizations, we
+        |  generally recommend using those instead of spaCy's built-in
+        |  #[code displacy] module. It'll allow your application to perform all
+        |  rendering on the client and only rely on the server for the text
+        |  processing. The generated markup is also more compatible with modern web
+        |  standards.
+
+    p
+        |  The quickest way visualize  #[code Doc] is to use
+        |  #[+api("displacy#serve") #[code displacy.serve]]. This will spin up a
+        |  simple web server and let you view the result straight from your browser.
+        |  displaCy can either take a single #[code Doc] or a list of #[code Doc]
+        |  objects as its first argument. This lets you construct them however you
+        |  like – using any model or modifications you like.
+
++section("dep")
+    +h(2, "dep") Visualizing the dependency parse
+    include _visualizers/_dep
+
++section("ent")
+    +h(2, "ent") Visualizing the entity recognizer
+    include _visualizers/_ent
+
++section("jupyter")
+    +h(2, "jupyter") Using displaCy in Jupyter notebooks
+    include _visualizers/_jupyter
+
++section("html")
+    +h(2, "html") Rendering HTML
+    include _visualizers/_html

From 5454b20cd7dbb41da577578e55274e556db00a4c Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 20:07:17 +0200
Subject: [PATCH 182/213] Update thinc imports for 6.9

---
 spacy/_ml.py               | 31 +++++++++++++------------------
 spacy/pipeline.pyx         | 18 ++++++++----------
 spacy/syntax/nn_parser.pyx | 10 +++-------
 3 files changed, 24 insertions(+), 35 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 77d6e0615..47f5c545e 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -1,33 +1,28 @@
 import ujson
+from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu, SELU
+from thinc.i2v import HashEmbed, StaticVectors
+from thinc.t2t import ExtractWindow, ParametricAttention
+from thinc.t2v import Pooling, max_pool, mean_pool, sum_pool
+from thinc.misc import Residual
+from thinc.misc import BatchNorm as BN
+from thinc.misc import LayerNorm as LN
+
 from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
-from thinc.neural._classes.model import Model
-from thinc.neural._classes.maxout import Maxout
-from thinc.neural._classes.softmax import Softmax
-from thinc.neural._classes.affine import Affine
-from thinc.neural._classes.hash_embed import HashEmbed
+from thinc.api import FeatureExtracter, with_getitem
+from thinc.api import uniqued, wrap, flatten_add_lengths, noop
+
+from thinc.linear.linear import LinearModel
 from thinc.neural.ops import NumpyOps, CupyOps
 from thinc.neural.util import get_array_module
+
 import random
 import cytoolz
 
-from thinc.neural._classes.convolution import ExtractWindow
-from thinc.neural._classes.static_vectors import StaticVectors
-from thinc.neural._classes.batchnorm import BatchNorm as BN
-from thinc.neural._classes.layernorm import LayerNorm as LN
-from thinc.neural._classes.resnet import Residual
-from thinc.neural._classes.relu import ReLu
-from thinc.neural._classes.selu import SELU
 from thinc import describe
 from thinc.describe import Dimension, Synapses, Biases, Gradient
 from thinc.neural._classes.affine import _set_dimensions_if_needed
-from thinc.api import FeatureExtracter, with_getitem
-from thinc.neural.pooling import Pooling, max_pool, mean_pool, sum_pool
-from thinc.neural._classes.attention import ParametricAttention
-from thinc.linear.linear import LinearModel
-from thinc.api import uniqued, wrap, flatten_add_lengths, noop
 import thinc.extra.load_nlp
 
-
 from .attrs import ID, ORTH, LOWER, NORM, PREFIX, SUFFIX, SHAPE, TAG, DEP, CLUSTER
 from .tokens.doc import Doc
 from . import util
diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index f6ee257d8..8d935335c 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -13,20 +13,18 @@ import ujson
 import msgpack
 
 from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
-from thinc.neural._classes.model import Model
-from thinc.neural._classes.maxout import Maxout
-from thinc.neural._classes.softmax import Softmax
-from thinc.neural._classes.affine import Affine
-from thinc.neural._classes.hash_embed import HashEmbed
+from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu, SELU
+from thinc.i2v import HashEmbed
+from thinc.t2v import Pooling, max_pool, mean_pool, sum_pool
+from thinc.t2t import ExtractWindow, ParametricAttention
+from thinc.misc import Residual
+from thinc.misc import BatchNorm as BN
+from thinc.misc import LayerNorm as LN
+
 from thinc.neural.util import to_categorical
 
-from thinc.neural.pooling import Pooling, max_pool, mean_pool
 from thinc.neural._classes.difference import Siamese, CauchySimilarity
 
-from thinc.neural._classes.convolution import ExtractWindow
-from thinc.neural._classes.resnet import Residual
-from thinc.neural._classes.batchnorm import BatchNorm as BN
-
 from .tokens.doc cimport Doc
 from .syntax.parser cimport Parser as LinearParser
 from .syntax.nn_parser cimport Parser as NeuralParser
diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 4043d6dd3..459c94463 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -38,13 +38,9 @@ from preshed.maps cimport MapStruct
 from preshed.maps cimport map_get
 
 from thinc.api import layerize, chain, noop, clone, with_flatten
-from thinc.neural._classes.model import Model
-from thinc.neural._classes.affine import Affine
-from thinc.neural._classes.relu import ReLu
-from thinc.neural._classes.maxout import Maxout
-from thinc.neural._classes.batchnorm import BatchNorm as BN
-from thinc.neural._classes.selu import SELU
-from thinc.neural._classes.layernorm import LayerNorm
+from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu, SELU
+from thinc.misc import LayerNorm
+
 from thinc.neural.ops import NumpyOps, CupyOps
 from thinc.neural.util import get_array_module
 

From 5cbefcba1743701a4d895123178a885454cf6c45 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 20:29:58 +0200
Subject: [PATCH 183/213] Set backwards compatibility flag

---
 spacy/_ml.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 47f5c545e..3b96a69b5 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -30,6 +30,10 @@ from . import util
 import numpy
 import io
 
+# TODO: Unset this once we don't want to support models previous models.
+import thinc.neural._classes.layernorm
+thinc.neural._classes.layernorm.set_compat_six_eight(True)
+
 VECTORS_KEY = 'spacy_pretrained_vectors'
 
 @layerize

From 252299ca2a518ea2d6e1e04208bce516e9d1ef59 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 21:29:43 +0200
Subject: [PATCH 184/213] Add sdist command

---
 fabfile.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/fabfile.py b/fabfile.py
index cfa80ead5..02a2110d9 100644
--- a/fabfile.py
+++ b/fabfile.py
@@ -32,6 +32,10 @@ def make():
             local('pip install -r requirements.txt')
             local('python setup.py build_ext --inplace')
 
+def sdist():
+    with virtualenv(VENV_DIR):
+        with lcd(path.dirname(__file__)):
+            local('python setup.py sdist')
 
 def clean():
     with lcd(path.dirname(__file__)):

From c69b0836a0f8a10f9bc56517ffb0d8abe1918b10 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 21:31:41 +0200
Subject: [PATCH 185/213] Fix fabfile

---
 fabfile.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/fabfile.py b/fabfile.py
index 02a2110d9..2894fe477 100644
--- a/fabfile.py
+++ b/fabfile.py
@@ -14,6 +14,7 @@ VENV_DIR = path.join(PWD, ENV)
 def env(lang='python2.7'):
     if path.exists(VENV_DIR):
         local('rm -rf {env}'.format(env=VENV_DIR))
+    local('pip install virtualenv')
     local('python -m virtualenv -p {lang} {env}'.format(lang=lang, env=VENV_DIR))
 
 

From 2eb0fe4957f4b827e85e41b811f832c1970567d6 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 21:40:04 +0200
Subject: [PATCH 186/213] Fix setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 8943d7a2e..23b4f9581 100755
--- a/setup.py
+++ b/setup.py
@@ -195,7 +195,7 @@ def setup_package():
                 'murmurhash>=0.28,<0.29',
                 'cymem>=1.30,<1.32',
                 'preshed>=1.0.0,<2.0.0',
-                'thinc>=6.8.2,<6.9.0',
+                'thinc>=6.9.0,<6.10.0',
                 'plac<1.0.0,>=0.9.6',
                 'six',
                 'pathlib',

From 32b9f3d1a671f2a8aeb6f07cf897ec069cf4e06a Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 22:17:31 +0200
Subject: [PATCH 187/213] Require new thinc

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 7fa5d72d3..0b46b38d5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,7 +3,7 @@ pathlib
 numpy>=1.7
 cymem>=1.30,<1.32
 preshed>=1.0.0,<2.0.0
-thinc>=6.8.2,<6.9.0
+thinc>=6.9.0,<6.10.0
 murmurhash>=0.28,<0.29
 plac<1.0.0,>=0.9.6
 six

From f24c2e3a8af785dd11b8d0a994732174290d688b Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 22:47:31 +0200
Subject: [PATCH 188/213] Fix evaluate for non-GPU

---
 spacy/cli/evaluate.py | 2 +-
 spacy/util.py         | 5 ++++-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/spacy/cli/evaluate.py b/spacy/cli/evaluate.py
index f409821b1..d9be95fae 100644
--- a/spacy/cli/evaluate.py
+++ b/spacy/cli/evaluate.py
@@ -39,7 +39,7 @@ def evaluate(cmd, model, data_path, gpu_id=-1, gold_preproc=False):
     Train a model. Expects data in spaCy's JSON format.
     """
     util.use_gpu(gpu_id)
-    util.set_env_log(True)
+    util.set_env_log(False)
     data_path = util.ensure_path(data_path)
     if not data_path.exists():
         prints(data_path, title="Evaluation data not found", exits=1)
diff --git a/spacy/util.py b/spacy/util.py
index 911970831..e1a721a12 100644
--- a/spacy/util.py
+++ b/spacy/util.py
@@ -563,7 +563,10 @@ def minify_html(html):
 
 
 def use_gpu(gpu_id):
-    import cupy.cuda.device
+    try:
+        import cupy.cuda.device
+    except ImportError:
+        return None
     from thinc.neural.ops import CupyOps
     device = cupy.cuda.device.Device(gpu_id)
     device.use()

From 73ac0aa0b560e3b719ebafe3b5dbbdf27eb18616 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Wed, 4 Oct 2017 00:03:15 +0200
Subject: [PATCH 189/213] Update spacy evaluate and add displaCy option

---
 spacy/cli/evaluate.py | 45 ++++++++++++++++++++++++++++++-------------
 1 file changed, 32 insertions(+), 13 deletions(-)

diff --git a/spacy/cli/evaluate.py b/spacy/cli/evaluate.py
index d9be95fae..42e077dc2 100644
--- a/spacy/cli/evaluate.py
+++ b/spacy/cli/evaluate.py
@@ -33,16 +33,23 @@ numpy.random.seed(0)
     data_path=("Location of JSON-formatted evaluation data", "positional", None, str),
     gold_preproc=("Use gold preprocessing", "flag", "G", bool),
     gpu_id=("Use GPU", "option", "g", int),
+    displacy_path=("Directory to output rendered parses as HTML", "option", "dp", str),
+    displacy_limit=("Limit of parses to render as HTML", "option", "dl", int)
 )
-def evaluate(cmd, model, data_path, gpu_id=-1, gold_preproc=False):
+def evaluate(cmd, model, data_path, gpu_id=-1, gold_preproc=False,
+             displacy_path=None, displacy_limit=25):
     """
-    Train a model. Expects data in spaCy's JSON format.
+    Evaluate a model. To render a sample of parses in a HTML file, set an output
+    directory as the displacy_path argument.
     """
     util.use_gpu(gpu_id)
     util.set_env_log(False)
     data_path = util.ensure_path(data_path)
+    displacy_path = util.ensure_path(displacy_path)
     if not data_path.exists():
         prints(data_path, title="Evaluation data not found", exits=1)
+    if displacy_path and not displacy_path.exists():
+        prints(displacy_path, title="Visualization output directory not found", exits=1)
     corpus = GoldCorpus(data_path, data_path)
     nlp = util.load_model(model)
     dev_docs = list(corpus.dev_docs(nlp, gold_preproc=gold_preproc))
@@ -50,18 +57,27 @@ def evaluate(cmd, model, data_path, gpu_id=-1, gold_preproc=False):
     scorer = nlp.evaluate(dev_docs, verbose=False)
     end = timer()
     nwords = sum(len(doc_gold[0]) for doc_gold in dev_docs)
-    print('Time', end-begin, 'words', nwords, 'w.p.s', nwords/(end-begin))
-    print_results(scorer)
+    print_results(scorer, time=end - begin, words=nwords,
+                  wps=nwords / (end - begin))
+    if displacy_path:
+        docs, golds = zip(*dev_docs)
+        render_deps = 'parser' in nlp.meta.get('pipeline', [])
+        render_ents = 'ner' in nlp.meta.get('pipeline', [])
+        render_parses(docs, displacy_path, model_name=model, limit=displacy_limit,
+                      deps=render_deps, ents=render_ents)
+        prints(displacy_path, title="Generated %s parses as HTML" % displacy_limit)
 
 
-def _render_parses(i, to_render):
-    to_render[0].user_data['title'] = "Batch %d" % i
-    with Path('/tmp/entities.html').open('w') as file_:
-        html = displacy.render(to_render[:5], style='ent', page=True)
-        file_.write(html)
-    with Path('/tmp/parses.html').open('w') as file_:
-        html = displacy.render(to_render[:5], style='dep', page=True)
-        file_.write(html)
+def render_parses(docs, output_path, model_name='', limit=250, deps=True, ents=True):
+    docs[0].user_data['title'] = model_name
+    if ents:
+        with (output_path / 'entities.html').open('w') as file_:
+            html = displacy.render(docs[:limit], style='ent', page=True)
+            file_.write(html)
+    if deps:
+        with (output_path / 'parses.html').open('w') as file_:
+            html = displacy.render(docs[:limit], style='dep', page=True, options={'compact': True})
+            file_.write(html)
 
 
 def print_progress(itn, losses, dev_scores, wps=0.0):
@@ -88,8 +104,11 @@ def print_progress(itn, losses, dev_scores, wps=0.0):
     print(tpl.format(itn, **scores))
 
 
-def print_results(scorer):
+def print_results(scorer, time, words, wps):
     results = {
+        'Time': '%.2f s' % time,
+        'Words': words,
+        'Words/s': '%.0f' % wps,
         'TOK': '%.2f' % scorer.token_acc,
         'POS': '%.2f' % scorer.tags_acc,
         'UAS': '%.2f' % scorer.uas,

From bfb512f45a14b6f436cace5dab536871171e4ba8 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Wed, 4 Oct 2017 00:18:41 +0200
Subject: [PATCH 190/213] Add website package.json and fix gitignore

---
 .gitignore           |  2 --
 website/package.json | 20 ++++++++++++++++++++
 2 files changed, 20 insertions(+), 2 deletions(-)
 create mode 100644 website/package.json

diff --git a/.gitignore b/.gitignore
index 572eea92d..14097dfcd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,8 +7,6 @@ keys/
 # Website
 website/www/
 website/_deploy.sh
-website/package.json
-website/announcement.jade
 website/.gitignore
 
 # Cython / C extensions
diff --git a/website/package.json b/website/package.json
new file mode 100644
index 000000000..c86aca222
--- /dev/null
+++ b/website/package.json
@@ -0,0 +1,20 @@
+{
+  "name": "spacy.io",
+  "private": true,
+  "version": "2.0.0",
+  "description": "spacy.io website",
+  "author": "Explosion AI",
+  "license": "MIT",
+  "devDependencies": {
+    "babel-cli": "^6.14.0",
+    "harp": "^0.24.0",
+    "uglify-js": "^2.7.3"
+  },
+  "dependencies": {},
+  "scripts": {
+    "compile": "NODE_ENV=deploy harp compile",
+    "compile_js": "babel www/assets/js/main.js --out-file www/assets/js/main.js --presets=es2015",
+    "uglify": "uglifyjs www/assets/js/main.js --output www/assets/js/main.js",
+    "build": "npm run compile && npm run compile_js && npm run uglify"
+  }
+}

From 464f14019d11ff3417fc9a522b810080ee406fe2 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Wed, 4 Oct 2017 00:18:47 +0200
Subject: [PATCH 191/213] Fix typos

---
 website/styleguide.jade | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/website/styleguide.jade b/website/styleguide.jade
index 107f7e2e6..42e70ed73 100644
--- a/website/styleguide.jade
+++ b/website/styleguide.jade
@@ -141,7 +141,7 @@ include _includes/_mixins
         +icon("github", 18)
 
     p
-        |  Icons are implemented via a SVG sprite and can be included as a
+        |  Icons are implemented via an SVG sprite and can be included as a
         |  mixin, using their name and an optional size value in #[code px].
 
     +infobox.u-text-center
@@ -560,7 +560,7 @@ include _includes/_mixins
             +cell #[code MODEL_META]
             +cell Description for model name components and meta data, ID mapped to string.
             +cell
-                +code(false, "json").o-no-block "vectors": "Word vectors",
+                +code(false, "json").o-no-block "vectors": "Word vectors"
 
         +row
             +cell #[code MODEL_LICENSES]

From 15ec7ddd0984f2180d0d4bfe7c2b3f4dfcfc772c Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Wed, 4 Oct 2017 00:19:03 +0200
Subject: [PATCH 192/213] Add docs for new spacy evaluate command

---
 website/api/_top-level/_cli.jade | 53 ++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)

diff --git a/website/api/_top-level/_cli.jade b/website/api/_top-level/_cli.jade
index 52884988e..f59d5afdd 100644
--- a/website/api/_top-level/_cli.jade
+++ b/website/api/_top-level/_cli.jade
@@ -344,6 +344,59 @@ p
         +cell Gradient L2 norm constraint.
         +cell #[code 1.0]
 
++h(3, "evaluate") Evaluate
+    +tag-new(2)
+
+p
+    |  Evaluate a model's accuracy and speed on JSON-formatted annotated data.
+    |  Will print the results and optionally export
+    |  #[+a("/usage/visualizers") displaCy visualizations] of a sample set of
+    |  parses to #[code .html] files. Visualizations for the dependency parse
+    |  and NER will be exported as separate files if the respective component
+    |  is present in the model's pipeline.
+
++code(false, "bash", "$", false, false, true).
+    spacy evaluate [model] [data_path] [--displacy-path] [--displacy-limit] [--gpu-id] [--gold-preproc]
+
++table(["Argument", "Type", "Description"])
+    +row
+        +cell #[code model]
+        +cell positional
+        +cell
+            |  Model to evaluate. Can be a package or shortcut link name, or a
+            |  path to a model data directory.
+
+    +row
+        +cell #[code data_path]
+        +cell positional
+        +cell Location of JSON-formatted evaluation data.
+
+    +row
+        +cell #[code --displacy-path], #[code -dp]
+        +cell option
+        +cell
+            |  Directory to output rendered parses as HTML. If not set, no
+            |  visualizations will be generated.
+
+    +row
+        +cell #[code --displacy-limit], #[code -dl]
+        +cell option
+        +cell
+            |  Number of parses to generate per file. Defaults to #[code 25].
+            |  Keep in mind that a significantly higher number might cause the
+            |  #[code .html] files to render slowly.
+
+    +row
+        +cell #[code --gpu-id], #[code -g]
+        +cell option
+        +cell GPU to use, if any. Defaults to #[code -1] for CPU.
+
+    +row
+        +cell #[code --gold-preproc], #[code -G]
+        +cell flag
+        +cell Use gold preprocessing.
+
+
 +h(3, "package") Package
 
 p

From 36ff525ff59239e5bf81c569622c918811d4266a Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Wed, 4 Oct 2017 00:37:15 +0200
Subject: [PATCH 193/213] Add NER P and NER R scores to model overview

---
 website/_includes/_page_models.jade | 2 +-
 website/models/_data.json           | 6 +++++-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/website/_includes/_page_models.jade b/website/_includes/_page_models.jade
index 6370f1b94..c5bd799f0 100644
--- a/website/_includes/_page_models.jade
+++ b/website/_includes/_page_models.jade
@@ -60,7 +60,7 @@ for id in CURRENT_MODELS
             +grid.o-no-block
                 +grid-col("third")
                     +h(4) Accuracy
-                    +table.o-no-block
+                    +table.o-block-small
                         for label, field in MODEL_ACCURACY
                             +row(style="display: none")
                                 +cell.u-nowrap
diff --git a/website/models/_data.json b/website/models/_data.json
index cc26b9bc9..b2898be8a 100644
--- a/website/models/_data.json
+++ b/website/models/_data.json
@@ -44,6 +44,8 @@
         "las": "Labelled dependencies",
         "tags_acc": "Part-of-speech tags",
         "ents_f": "Entities (F-score)",
+        "ents_p": "Entities (precision)",
+        "ents_r": "Entities (recall)",
         "pipeline": "Processing pipeline components in order",
         "sources": "Sources of training data"
     },
@@ -59,7 +61,9 @@
         "uas": "UAS",
         "las": "LAS",
         "tags_acc": "POS",
-        "ents_f": "NER F"
+        "ents_f": "NER F",
+        "ents_p": "NER P",
+        "ents_r": "NER R"
     },
 
     "LANGUAGES": {

From af75b742083798347c279a840abb0535643c920d Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Tue, 3 Oct 2017 20:47:10 -0500
Subject: [PATCH 194/213] Unset LayerNorm backwards compat hack

---
 spacy/_ml.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index 3b96a69b5..dc458d6ac 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -31,8 +31,8 @@ import numpy
 import io
 
 # TODO: Unset this once we don't want to support models previous models.
-import thinc.neural._classes.layernorm
-thinc.neural._classes.layernorm.set_compat_six_eight(True)
+#import thinc.neural._classes.layernorm
+#thinc.neural._classes.layernorm.set_compat_six_eight(True)
 
 VECTORS_KEY = 'spacy_pretrained_vectors'
 

From 33cf9cecdd047282682c3ff91bd495ca79f89b32 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Wed, 4 Oct 2017 13:34:03 +0200
Subject: [PATCH 195/213] Port over changes from #1386

---
 website/usage/_facts-figures/_benchmarks.jade         | 2 +-
 website/usage/_facts-figures/_feature-comparison.jade | 2 +-
 website/usage/_visualizers/_html.jade                 | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/usage/_facts-figures/_benchmarks.jade b/website/usage/_facts-figures/_benchmarks.jade
index fa0e26763..f69eb5406 100644
--- a/website/usage/_facts-figures/_benchmarks.jade
+++ b/website/usage/_facts-figures/_benchmarks.jade
@@ -74,7 +74,7 @@ p
 
     +row
         +cell
-            +a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet Parsey McParseface
+            +a("https://github.com/tensorflow/models/tree/master/research/syntaxnet") SyntaxNet Parsey McParseface
         +cell 2016
         +cell neural
         +cell.u-text-right 94.15
diff --git a/website/usage/_facts-figures/_feature-comparison.jade b/website/usage/_facts-figures/_feature-comparison.jade
index 92ac69050..c8fa5ffbe 100644
--- a/website/usage/_facts-figures/_feature-comparison.jade
+++ b/website/usage/_facts-figures/_feature-comparison.jade
@@ -2,7 +2,7 @@
 
 p
     |  Here's a quick comparison of the functionalities offered by spaCy,
-    |  #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet],
+    |  #[+a("https://github.com/tensorflow/models/tree/master/research/syntaxnet") SyntaxNet],
     |  #[+a("http://www.nltk.org/py-modindex.html") NLTK] and
     |  #[+a("http://stanfordnlp.github.io/CoreNLP/") CoreNLP].
 
diff --git a/website/usage/_visualizers/_html.jade b/website/usage/_visualizers/_html.jade
index 701d4b683..595192442 100644
--- a/website/usage/_visualizers/_html.jade
+++ b/website/usage/_visualizers/_html.jade
@@ -71,7 +71,7 @@ p
     |  You can also use displaCy to manually render data. This can be useful if
     |  you want to visualize output from other libraries, like
     |  #[+a("http://www.nltk.org") NLTK] or
-    |  #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet].
+    |  #[+a("https://github.com/tensorflow/models/tree/master/research/syntaxnet") SyntaxNet].
     |  Simply convert the dependency parse or recognised entities to displaCy's
     |  format and set #[code manual=True] on either #[code render()] or
     |  #[code serve()].

From 774f5732bdab210381fffce8c06adca9fc0152e5 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 14:55:15 +0200
Subject: [PATCH 196/213] Fix dimensionality of textcat when no vectors
 available

---
 spacy/_ml.py | 25 ++++++++++++++++---------
 1 file changed, 16 insertions(+), 9 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index dc458d6ac..b02bd27d9 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -570,6 +570,7 @@ def foreach(layer, drop_factor=1.0):
 
 def build_text_classifier(nr_class, width=64, **cfg):
     nr_vector = cfg.get('nr_vector', 5000)
+    pretrained_dims = cfg.get('pretrained_dims', 0)
     with Model.define_operators({'>>': chain, '+': add, '|': concatenate,
                                  '**': clone}):
         if cfg.get('low_data'):
@@ -577,7 +578,7 @@ def build_text_classifier(nr_class, width=64, **cfg):
                 SpacyVectors
                 >> flatten_add_lengths
                 >> with_getitem(0,
-                    Affine(width, 300)
+                    Affine(width, pretrained_dims)
                 )
                 >> ParametricAttention(width)
                 >> Pooling(sum_pool)
@@ -604,16 +605,22 @@ def build_text_classifier(nr_class, width=64, **cfg):
             )
         )
 
-        static_vectors = (
-            SpacyVectors
-            >> with_flatten(Affine(width, 300))
-        )
-
-        cnn_model = (
+        if pretrained_dims:
+            static_vectors = (
+                SpacyVectors
+                >> with_flatten(Affine(width, pretrained_dims))
+            )
             # TODO Make concatenate support lists
-            concatenate_lists(trained_vectors, static_vectors)
+            vectors = concatenate_lists(trained_vectors, static_vectors)
+            vectors_width = width*2
+        else:
+            vectors = trained_vectors
+            vectors_width = width
+            static_vectors = None
+        cnn_model = (
+            vectors
             >> with_flatten(
-                LN(Maxout(width, width*2))
+                LN(Maxout(width, vectors_width))
                 >> Residual(
                     (ExtractWindow(nW=1) >> zero_init(Maxout(width, width*3)))
                 ) ** 2, pad=2

From 79a94bc166bfdd73a11b4e10a4717df22d171025 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 14:55:30 +0200
Subject: [PATCH 197/213] Update textcat exampe

---
 examples/training/train_textcat.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/examples/training/train_textcat.py b/examples/training/train_textcat.py
index eefae111f..7eb356100 100644
--- a/examples/training/train_textcat.py
+++ b/examples/training/train_textcat.py
@@ -1,3 +1,7 @@
+'''Train a multi-label convolutional neural network text classifier,
+using the spacy.pipeline.TextCategorizer component. The model is then added
+to spacy.pipeline, and predictions are available at `doc.cats`.
+'''
 from __future__ import unicode_literals
 import plac
 import random
@@ -31,7 +35,7 @@ def train_textcat(tokenizer, textcat,
         train_data = tqdm.tqdm(train_data, leave=False) # Progress bar
         for batch in minibatch(train_data, size=batch_sizes):
             docs, golds = zip(*batch)
-            textcat.update((docs, None), golds, sgd=optimizer, drop=0.2,
+            textcat.update(docs, golds, sgd=optimizer, drop=0.2,
                 losses=losses)
         with textcat.model.use_params(optimizer.averages):
             scores = evaluate(tokenizer, textcat, dev_texts, dev_cats)

From db05d4d5823c05208880eda92d99e7ba2de3ddd4 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 14:56:16 +0200
Subject: [PATCH 198/213] Add test for #1380. Passes without fix?

---
 spacy/tests/regression/test_issue1380.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 spacy/tests/regression/test_issue1380.py

diff --git a/spacy/tests/regression/test_issue1380.py b/spacy/tests/regression/test_issue1380.py
new file mode 100644
index 000000000..d9cfe1bd2
--- /dev/null
+++ b/spacy/tests/regression/test_issue1380.py
@@ -0,0 +1,13 @@
+import pytest
+
+from ...language import Language
+
+def test_issue1380_empty_string():
+    nlp = Language()
+    doc = nlp('')
+    assert len(doc) == 0
+
+@pytest.mark.models('en')
+def test_issue1380_en(EN):
+    doc = EN('')
+    assert len(doc) == 0

From 39798b0172eab604873280aa8a429c3bf78c0c78 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 15:12:09 +0200
Subject: [PATCH 199/213] Uncomment layernorm adjustment hack

---
 spacy/_ml.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index b02bd27d9..bbd3e2b3c 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -31,8 +31,8 @@ import numpy
 import io
 
 # TODO: Unset this once we don't want to support models previous models.
-#import thinc.neural._classes.layernorm
-#thinc.neural._classes.layernorm.set_compat_six_eight(True)
+import thinc.neural._classes.layernorm
+thinc.neural._classes.layernorm.set_compat_six_eight(True)
 
 VECTORS_KEY = 'spacy_pretrained_vectors'
 

From f1b86dff8cef8802f5493bad4331d23e016dca7c Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 15:12:28 +0200
Subject: [PATCH 200/213] Update textcat example

---
 examples/training/train_textcat.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/examples/training/train_textcat.py b/examples/training/train_textcat.py
index 7eb356100..6018827a4 100644
--- a/examples/training/train_textcat.py
+++ b/examples/training/train_textcat.py
@@ -16,6 +16,11 @@ from spacy.gold import GoldParse, minibatch
 from spacy.util import compounding
 from spacy.pipeline import TextCategorizer
 
+# TODO: Remove this once we're not supporting models trained with thinc <6.9.0
+import thinc.neural._classes.layernorm
+thinc.neural._classes.layernorm.set_compat_six_eight(False)
+
+
 
 def train_textcat(tokenizer, textcat,
                   train_texts, train_cats, dev_texts, dev_cats,
@@ -28,12 +33,13 @@ def train_textcat(tokenizer, textcat,
     train_docs = [tokenizer(text) for text in train_texts]
     train_gold = [GoldParse(doc, cats=cats) for doc, cats in
                   zip(train_docs, train_cats)]
-    train_data = zip(train_docs, train_gold)
+    train_data = list(zip(train_docs, train_gold))
     batch_sizes = compounding(4., 128., 1.001)
     for i in range(n_iter):
         losses = {}
-        train_data = tqdm.tqdm(train_data, leave=False) # Progress bar
-        for batch in minibatch(train_data, size=batch_sizes):
+        # Progress bar and minibatching
+        batches = minibatch(tqdm.tqdm(train_data, leave=False), size=batch_sizes)
+        for batch in batches:
             docs, golds = zip(*batch)
             textcat.update(docs, golds, sgd=optimizer, drop=0.2,
                 losses=losses)
@@ -65,12 +71,13 @@ def evaluate(tokenizer, textcat, texts, cats):
     return {'textcat_p': precis, 'textcat_r': recall, 'textcat_f': fscore}  
 
 
-def load_data():
+def load_data(limit=0):
     # Partition off part of the train data --- avoid running experiments
     # against test.
     train_data, _ = thinc.extra.datasets.imdb()
 
     random.shuffle(train_data)
+    train_data = train_data[-limit:]
 
     texts, labels = zip(*train_data)
     cats = [(['POSITIVE'] if y else []) for y in labels]
@@ -90,7 +97,7 @@ def main(model_loc=None):
     textcat = TextCategorizer(tokenizer.vocab, labels=['POSITIVE'])
 
     print("Load IMDB data")
-    (train_texts, train_cats), (dev_texts, dev_cats) = load_data()
+    (train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=1000)
 
     print("Itn.\tLoss\tP\tR\tF")
     progress = '{i:d} {loss:.3f} {textcat_p:.3f} {textcat_r:.3f} {textcat_f:.3f}'

From f8a0614527a656a50100fa784be23c698f706673 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 15:15:53 +0200
Subject: [PATCH 201/213] Improve textcat model slightly

---
 spacy/_ml.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index bbd3e2b3c..e882c954e 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -622,7 +622,7 @@ def build_text_classifier(nr_class, width=64, **cfg):
             >> with_flatten(
                 LN(Maxout(width, vectors_width))
                 >> Residual(
-                    (ExtractWindow(nW=1) >> zero_init(Maxout(width, width*3)))
+                    (ExtractWindow(nW=1) >> LN(Maxout(width, width*3)))
                 ) ** 2, pad=2
             )
             >> flatten_add_lengths

From bd8e84998a7bf4706c18f9b3099a84d4a2ba6a52 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 16:07:30 +0200
Subject: [PATCH 202/213] Add nO attribute to TextCategorizer model

---
 spacy/_ml.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spacy/_ml.py b/spacy/_ml.py
index e882c954e..4a41339aa 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -642,7 +642,7 @@ def build_text_classifier(nr_class, width=64, **cfg):
             >> zero_init(Affine(nr_class, nr_class*2, drop_factor=0.0))
             >> logistic
         )
-
+    model.nO = nr_class
     model.lsuv = False
     return model
 

From bb13aa4bf3b7cb10ac93cc36486e8d0d1fddc43c Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Wed, 4 Oct 2017 16:12:06 +0200
Subject: [PATCH 203/213] Fix typos in PhraseMatcher docs

---
 website/api/phrasematcher.jade | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/api/phrasematcher.jade b/website/api/phrasematcher.jade
index 5c49a03d5..0f0959f28 100644
--- a/website/api/phrasematcher.jade
+++ b/website/api/phrasematcher.jade
@@ -15,7 +15,7 @@ p Create the rule-based #[code PhraseMatcher].
 
 +aside-code("Example").
     from spacy.matcher import PhraseMatcher
-    matcher = Matcher(nlp.vocab, max_length=6)
+    matcher = PhraseMatcher(nlp.vocab, max_length=6)
 
 +table(["Name", "Type", "Description"])
     +row
@@ -41,9 +41,9 @@ p Create the rule-based #[code PhraseMatcher].
 p Find all token sequences matching the supplied patterns on the #[code Doc].
 
 +aside-code("Example").
-    from spacy.matcher import Matcher
+    from spacy.matcher import PhraseMatcher
 
-    matcher = Matcher(nlp.vocab)
+    matcher = PhraseMatcher(nlp.vocab)
     matcher.add('OBAMA', None, nlp(u"Barack Obama"))
     doc = nlp(u"Barack Obama lifts America one last time in emotional farewell")
     matches = matcher(doc)

From 40edb65ee751dfe4cf6e04ee59891266d8b14f30 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 16:36:50 +0200
Subject: [PATCH 204/213] Make test work for Python 2.7

---
 spacy/tests/regression/test_issue1380.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/spacy/tests/regression/test_issue1380.py b/spacy/tests/regression/test_issue1380.py
index d9cfe1bd2..b2d610954 100644
--- a/spacy/tests/regression/test_issue1380.py
+++ b/spacy/tests/regression/test_issue1380.py
@@ -1,3 +1,4 @@
+from __future__ import unicode_literals
 import pytest
 
 from ...language import Language

From d90398643933999d734a09bc3637a8723d5de2c3 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 17:14:26 +0200
Subject: [PATCH 205/213] Increment version

---
 spacy/about.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spacy/about.py b/spacy/about.py
index b8b1dbbf6..a8880d7ca 100644
--- a/spacy/about.py
+++ b/spacy/about.py
@@ -3,13 +3,13 @@
 # https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
 
 __title__ = 'spacy-nightly'
-__version__ = '2.0.0a15'
+__version__ = '2.0.0a16'
 __summary__ = 'Industrial-strength Natural Language Processing (NLP) with Python and Cython'
 __uri__ = 'https://spacy.io'
 __author__ = 'Explosion AI'
 __email__ = 'contact@explosion.ai'
 __license__ = 'MIT'
-__release__ = False
+__release__ = True
 
 __docs_models__ = 'https://alpha.spacy.io/usage/models'
 __download_url__ = 'https://github.com/explosion/spacy-models/releases/download'

From cc29e8b49717c76299e494ad98bf0e80d3363e2a Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 18:00:37 +0200
Subject: [PATCH 206/213] Add buildkite.yml for making sdists

---
 .buildkite/sdist.yml | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 .buildkite/sdist.yml

diff --git a/.buildkite/sdist.yml b/.buildkite/sdist.yml
new file mode 100644
index 000000000..a3d8b2b68
--- /dev/null
+++ b/.buildkite/sdist.yml
@@ -0,0 +1,11 @@
+steps:
+  -
+    command:
+      - "$HOME/.local/bin/fab env clean make test"
+    label: ":dizzy: :python:"
+    artifact_paths: "dist/*.tar.gz"
+  - wait
+  - trigger "spacy-sdist-against-models"
+    label: ":spacy: :build:"
+    env:
+      SPACY_VERSION: "{$SPACY_VERSION}"

From ff24b6d04a48bf0aa218052fa5e6cc28156e8fe1 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 18:05:45 +0200
Subject: [PATCH 207/213] Fix yml

---
 .buildkite/sdist.yml | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/.buildkite/sdist.yml b/.buildkite/sdist.yml
index a3d8b2b68..01dc6f024 100644
--- a/.buildkite/sdist.yml
+++ b/.buildkite/sdist.yml
@@ -1,7 +1,5 @@
 steps:
-  -
-    command:
-      - "$HOME/.local/bin/fab env clean make test"
+  - command: "$HOME/.local/bin/fab env clean make test"
     label: ":dizzy: :python:"
     artifact_paths: "dist/*.tar.gz"
   - wait

From 6304c5e14636b754f7bf6b7764d759d2bafb7cf7 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 18:08:34 +0200
Subject: [PATCH 208/213] Fix yml

---
 .buildkite/sdist.yml | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/.buildkite/sdist.yml b/.buildkite/sdist.yml
index 01dc6f024..2cbbc54c4 100644
--- a/.buildkite/sdist.yml
+++ b/.buildkite/sdist.yml
@@ -1,9 +1,10 @@
 steps:
-  - command: "$HOME/.local/bin/fab env clean make test"
-    label: ":dizzy: :python:"
-    artifact_paths: "dist/*.tar.gz"
+  -
+    command: "$HOME/.local/bin/fab env clean make test"
+      label: ":dizzy: :python:"
+      artifact_paths: "dist/*.tar.gz"
   - wait
-  - trigger "spacy-sdist-against-models"
+  - trigger: "spacy-sdist-against-models"
     label: ":spacy: :build:"
     env:
       SPACY_VERSION: "{$SPACY_VERSION}"

From 71825f9737dd00b461e0fdefd0b38925389da5c0 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 18:12:16 +0200
Subject: [PATCH 209/213] Fix yml

---
 .buildkite/sdist.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.buildkite/sdist.yml b/.buildkite/sdist.yml
index 2cbbc54c4..e43153802 100644
--- a/.buildkite/sdist.yml
+++ b/.buildkite/sdist.yml
@@ -1,8 +1,8 @@
 steps:
   -
     command: "$HOME/.local/bin/fab env clean make test"
-      label: ":dizzy: :python:"
-      artifact_paths: "dist/*.tar.gz"
+    label: ":dizzy: :python:"
+    artifact_paths: "dist/*.tar.gz"
   - wait
   - trigger: "spacy-sdist-against-models"
     label: ":spacy: :build:"

From c4c7def9cef280b20818409846200c49f33afac2 Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 18:14:33 +0200
Subject: [PATCH 210/213] Fix yml

---
 .buildkite/sdist.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.buildkite/sdist.yml b/.buildkite/sdist.yml
index e43153802..0f80fd9c8 100644
--- a/.buildkite/sdist.yml
+++ b/.buildkite/sdist.yml
@@ -6,5 +6,6 @@ steps:
   - wait
   - trigger: "spacy-sdist-against-models"
     label: ":spacy: :build:"
-    env:
-      SPACY_VERSION: "{$SPACY_VERSION}"
+    build:
+      env:
+        SPACY_VERSION: "{$SPACY_VERSION}"

From e3c93f87a4c17c1a9db9bf85fd77255d2a5ddc6f Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 18:18:07 +0200
Subject: [PATCH 211/213] Update sdist

---
 .buildkite/sdist.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.buildkite/sdist.yml b/.buildkite/sdist.yml
index 0f80fd9c8..7776429c5 100644
--- a/.buildkite/sdist.yml
+++ b/.buildkite/sdist.yml
@@ -1,6 +1,6 @@
 steps:
   -
-    command: "$HOME/.local/bin/fab env clean make test"
+    command: "fab env clean make test"
     label: ":dizzy: :python:"
     artifact_paths: "dist/*.tar.gz"
   - wait

From 5560c46a593363b6d8c51bbbf575a6b2e562ed4d Mon Sep 17 00:00:00 2001
From: Matthew Honnibal <honnibal+gh@gmail.com>
Date: Wed, 4 Oct 2017 18:29:41 +0200
Subject: [PATCH 212/213] Update buildkite

---
 .buildkite/sdist.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.buildkite/sdist.yml b/.buildkite/sdist.yml
index 7776429c5..f5e0fca35 100644
--- a/.buildkite/sdist.yml
+++ b/.buildkite/sdist.yml
@@ -1,6 +1,6 @@
 steps:
   -
-    command: "fab env clean make test"
+    command: "fab env clean make test sdist"
     label: ":dizzy: :python:"
     artifact_paths: "dist/*.tar.gz"
   - wait

From b621a2e9646f54b396f2c6439022e40e00e074e6 Mon Sep 17 00:00:00 2001
From: ines <ines@ines.io>
Date: Wed, 4 Oct 2017 18:37:27 +0200
Subject: [PATCH 213/213] Fix build emoji

---
 .buildkite/sdist.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.buildkite/sdist.yml b/.buildkite/sdist.yml
index f5e0fca35..9b94e3752 100644
--- a/.buildkite/sdist.yml
+++ b/.buildkite/sdist.yml
@@ -5,7 +5,7 @@ steps:
     artifact_paths: "dist/*.tar.gz"
   - wait
   - trigger: "spacy-sdist-against-models"
-    label: ":spacy: :build:"
+    label: ":dizzy: :hammer:"
     build:
       env:
         SPACY_VERSION: "{$SPACY_VERSION}"