Merge branch 'master' into tokenizer_exceptions

.github/contributors/wallinm1.md

@@ -0,0 +1,106 @@
+# spaCy contributor agreement
+
+This spaCy Contributor Agreement (**"SCA"**) is based on the
+[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
+The SCA applies to any contribution that you make to any product or project
+managed by us (the **"project"**), and sets out the intellectual property rights
+you grant to us in the contributed materials. The term **"us"** shall mean
+[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
+**"you"** shall mean the person or entity identified below.
+
+If you agree to be bound by these terms, fill in the information requested
+below and include the filled-in version with your first pull request, under the
+folder [`.github/contributors/`](/.github/contributors/). The name of the file
+should be your GitHub username, with the extension `.md`. For example, the user
+example_user would create the file `.github/contributors/example_user.md`.
+
+Read this agreement carefully before signing. These terms and conditions
+constitute a binding legal agreement.
+
+## Contributor Agreement
+
+1. The term "contribution" or "contributed materials" means any source code,
+object code, patch, tool, sample, graphic, specification, manual,
+documentation, or any other material posted or submitted by you to the project.
+
+2. With respect to any worldwide copyrights, or copyright applications and
+registrations, in your contribution:
+
+    * you hereby assign to us joint ownership, and to the extent that such
+    assignment is or becomes invalid, ineffective or unenforceable, you hereby
+    grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
+    royalty-free, unrestricted license to exercise all rights under those
+    copyrights. This includes, at our option, the right to sublicense these same
+    rights to third parties through multiple levels of sublicensees or other
+    licensing arrangements;
+
+    * you agree that each of us can do all things in relation to your
+    contribution as if each of us were the sole owners, and if one of us makes
+    a derivative work of your contribution, the one who makes the derivative
+    work (or has it made will be the sole owner of that derivative work;
+
+    * you agree that you will not assert any moral rights in your contribution
+    against us, our licensees or transferees;
+
+    * you agree that we may register a copyright in your contribution and
+    exercise all ownership rights associated with it; and
+
+    * you agree that neither of us has any duty to consult with, obtain the
+    consent of, pay or render an accounting to the other for any use or
+    distribution of your contribution.
+
+3. With respect to any patents you own, or that you can license without payment
+to any third party, you hereby grant to us a perpetual, irrevocable,
+non-exclusive, worldwide, no-charge, royalty-free license to:
+
+    * make, have made, use, sell, offer to sell, import, and otherwise transfer
+    your contribution in whole or in part, alone or in combination with or
+    included in any product, work or materials arising out of the project to
+    which your contribution was submitted, and
+
+    * at our option, to sublicense these same rights to third parties through
+    multiple levels of sublicensees or other licensing arrangements.
+
+4. Except as set out above, you keep all right, title, and interest in your
+contribution. The rights that you grant to us under these terms are effective
+on the date you first submitted a contribution to us, even if your submission
+took place before the date you sign these terms.
+
+5. You covenant, represent, warrant and agree that:
+
+    * Each contribution that you submit is and shall be an original work of
+    authorship and you can legally grant the rights set out in this SCA;
+
+    * to the best of your knowledge, each contribution will not violate any
+    third party's copyrights, trademarks, patents, or other intellectual
+    property rights; and
+
+    * each contribution shall be in compliance with U.S. export control laws and
+    other applicable export and import laws. You agree to notify us if you
+    become aware of any circumstance which would make any of the foregoing
+    representations inaccurate in any respect. We may publicly disclose your
+    participation in the project, including the fact that you have signed the SCA.
+
+6. This SCA is governed by the laws of the State of California and applicable
+U.S. Federal law. Any choice of law rules will not apply.
+
+7. Please place an “x” on one of the applicable statement below. Please do NOT
+mark both statements:
+
+    * [x] I am signing on behalf of myself as an individual and no other person
+    or entity, including my employer, has or will have rights with respect my
+    contributions.
+
+    * [ ] I am signing on behalf of my employer or a legal entity and I have the
+    actual authority to contractually bind that entity.
+
+## Contributor Details
+
+| Field                          | Entry                            |
+|------------------------------- | -------------------------------- |
+| Name                           |  Michael Wallin                  |
+| Company name (if applicable)   |                                  |
+| Title or role (if applicable)  |                                  |
+| Date                           |  2017-02-04                      |
+| GitHub username                |  wallinm1                        |
+| Website (optional)             |                                  |

CONTRIBUTING.md

@@ -19,7 +19,7 @@ First, [do a quick search](https://github.com/issues?q=+is%3Aissue+user%3Aexplos
 
 If you're looking for help with your code, consider posting a question on [StackOverflow](http://stackoverflow.com/questions/tagged/spacy) instead. If you tag it `spacy` and `python`, more people will see it and hopefully be able to help.
 
-When opening an issue, use a descriptive title and include your environment (operating system, Python version, spaCy version). Our [issue template](https://github.com/explosion/spaCy/issues/new) helps you remember the most important details to include.
+When opening an issue, use a descriptive title and include your environment (operating system, Python version, spaCy version). Our [issue template](https://github.com/explosion/spaCy/issues/new) helps you remember the most important details to include. **Pro tip:** If you need to share long blocks of code or logs, you can wrap them in `<details>` and `</details>`. This [collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details) so it only becomes visible on click, making the issue easier to read and follow.
 
 If you've discovered a bug, you can also submit a [regression test](#fixing-bugs) straight away. When you're opening an issue to report the bug, simply refer to your pull request in the issue body.
 

CONTRIBUTORS.md

@@ -22,12 +22,14 @@ This is a list of everyone who has made significant contributions to spaCy, in a
 * Mark Amery, [@ExplodingCabbage](https://github.com/ExplodingCabbage)
 * Matthew Honnibal, [@honnibal](https://github.com/honnibal)
 * Maxim Samsonov, [@maxirmx](https://github.com/maxirmx)
+* Michael Wallin, [@wallinm1](https://github.com/wallinm1)
 * Oleg Zd, [@olegzd](https://github.com/olegzd)
 * Pokey Rule, [@pokey](https://github.com/pokey)
 * Raphaël Bournhonesque, [@raphael0202](https://github.com/raphael0202)
 * Rob van Nieuwpoort, [@RvanNieuwpoort](https://github.com/RvanNieuwpoort)
 * Sam Bozek, [@sambozek](https://github.com/sambozek)
 * Sasho Savkov [@savkov](https://github.com/savkov)
+* Thomas Tanon, [@Tpt](https://github.com/Tpt)
 * Tiago Rodrigues, [@TiagoMRodrigues](https://github.com/TiagoMRodrigues)
 * Vsevolod Solovyov, [@vsolovyov](https://github.com/vsolovyov)
 * Wah Loon Keng, [@kengz](https://github.com/kengz)

README.rst

@@ -5,8 +5,8 @@ spaCy is a library for advanced natural language processing in Python and
 Cython. spaCy is built on  the very latest research, but it isn't researchware.  
 It was designed from day one to be used in real products. spaCy currently supports 
 English and German,  as well as tokenization for Chinese, Spanish, Italian, French, 
-Portuguese, Dutch, Swedish and Hungarian. It's commercial  open-source software, 
+Portuguese, Dutch, Swedish, Finnish and Hungarian. It's commercial  open-source 
-released under the MIT license.
+software, released under the MIT license.
 
 💫 **Version 1.6 out now!** `Read the release notes here. <https://github.com/explosion/spaCy/releases/>`_
 

examples/keras_parikh_entailment/__main__.py

@@ -12,17 +12,23 @@ from spacy_hook import create_similarity_pipeline
 
 from keras_decomposable_attention import build_model
 
+try:
+    import cPickle as pickle
+except ImportError:
+    import pickle
+
 
 def train(model_dir, train_loc, dev_loc, shape, settings):
     train_texts1, train_texts2, train_labels = read_snli(train_loc)
     dev_texts1, dev_texts2, dev_labels = read_snli(dev_loc)
-    
+
     print("Loading spaCy")
     nlp = spacy.load('en')
+    assert nlp.path is not None
     print("Compiling network")
     model = build_model(get_embeddings(nlp.vocab), shape, settings)
     print("Processing texts...")
-    Xs = []    
+    Xs = []
     for texts in (train_texts1, train_texts2, dev_texts1, dev_texts2):
         Xs.append(get_word_ids(list(nlp.pipe(texts, n_threads=20, batch_size=20000)),
                          max_length=shape[0],
@@ -36,35 +42,41 @@ def train(model_dir, train_loc, dev_loc, shape, settings):
         validation_data=([dev_X1, dev_X2], dev_labels),
         nb_epoch=settings['nr_epoch'],
         batch_size=settings['batch_size'])
+    if not (nlp.path / 'similarity').exists():
+        (nlp.path / 'similarity').mkdir()
+    print("Saving to", model_dir / 'similarity')
+    weights = model.get_weights()
+    with (nlp.path / 'similarity' / 'model').open('wb') as file_:
+        pickle.dump(weights[1:], file_)
+    with (nlp.path / 'similarity' / 'config.json').open('wb') as file_:
+        file_.write(model.to_json())
 
 
 def evaluate(model_dir, dev_loc):
-    nlp = spacy.load('en', path=model_dir,
+    dev_texts1, dev_texts2, dev_labels = read_snli(dev_loc)
-            tagger=False, parser=False, entity=False, matcher=False,
+    nlp = spacy.load('en',
             create_pipeline=create_similarity_pipeline)
-    n = 0
+    total = 0.
-    correct = 0
+    correct = 0.
-    for (text1, text2), label in zip(dev_texts, dev_labels):
+    for text1, text2, label in zip(dev_texts1, dev_texts2, dev_labels):
         doc1 = nlp(text1)
         doc2 = nlp(text2)
         sim = doc1.similarity(doc2)
-        if bool(sim >= 0.5) == label:
+        if sim.argmax() == label.argmax():
             correct += 1
-        n += 1
+        total += 1
     return correct, total
 
 
 def demo(model_dir):
     nlp = spacy.load('en', path=model_dir,
-            tagger=False, parser=False, entity=False, matcher=False,
             create_pipeline=create_similarity_pipeline)
-    doc1 = nlp(u'Worst fries ever! Greasy and horrible...')
+    doc1 = nlp(u'What were the best crime fiction books in 2016?')
-    doc2 = nlp(u'The milkshakes are good. The fries are bad.')
+    doc2 = nlp(
-    print('doc1.similarity(doc2)', doc1.similarity(doc2))
+        u'What should I read that was published last year? I like crime stories.')
-    sent1a, sent1b = doc1.sents
+    print(doc1)
-    print('sent1a.similarity(sent1b)', sent1a.similarity(sent1b))
+    print(doc2)
-    print('sent1a.similarity(doc2)', sent1a.similarity(doc2))
+    print("Similarity", doc1.similarity(doc2))
-    print('sent1b.similarity(doc2)', sent1b.similarity(doc2))
 
 
 LABELS = {'entailment': 0, 'contradiction': 1, 'neutral': 2}
@@ -119,7 +131,8 @@ def main(mode, model_dir, train_loc, dev_loc,
     if mode == 'train':
         train(model_dir, train_loc, dev_loc, shape, settings)
     elif mode == 'evaluate':
-        evaluate(model_dir, dev_loc)
+        correct, total = evaluate(model_dir, dev_loc)
+        print(correct, '/', total, correct / total)
     else:
         demo(model_dir)
 

examples/keras_parikh_entailment/keras_decomposable_attention.py

@@ -12,6 +12,8 @@ from keras.models import Sequential, Model, model_from_json
 from keras.regularizers import l2
 from keras.optimizers import Adam
 from keras.layers.normalization import BatchNormalization
+from keras.layers.pooling import GlobalAveragePooling1D, GlobalMaxPooling1D
+from keras.layers import Merge
 
 
 def build_model(vectors, shape, settings):
@@ -29,11 +31,11 @@ def build_model(vectors, shape, settings):
     align = _SoftAlignment(max_length, nr_hidden)
     compare = _Comparison(max_length, nr_hidden, dropout=settings['dropout'])
     entail = _Entailment(nr_hidden, nr_class, dropout=settings['dropout'])
-    
+
     # Declare the model as a computational graph.
     sent1 = embed(ids1) # Shape: (i, n)
     sent2 = embed(ids2) # Shape: (j, n)
-    
+
     if settings['gru_encode']:
         sent1 = encode(sent1)
         sent2 = encode(sent2)
@@ -42,12 +44,12 @@ def build_model(vectors, shape, settings):
 
     align1 = align(sent2, attention)
     align2 = align(sent1, attention, transpose=True)
-    
+
     feats1 = compare(sent1, align1)
     feats2 = compare(sent2, align2)
-    
+
     scores = entail(feats1, feats2)
-    
+
     # Now that we have the input/output, we can construct the Model object...
     model = Model(input=[ids1, ids2], output=[scores])
 
@@ -93,7 +95,7 @@ class _StaticEmbedding(object):
         def get_output_shape(shapes):
             print(shapes)
             return shapes[0]
-        mod_sent = self.mod_ids(sentence) 
+        mod_sent = self.mod_ids(sentence)
         tuning = self.tune(mod_sent)
         #tuning = merge([tuning, mod_sent],
         #    mode=lambda AB: AB[0] * (K.clip(K.cast(AB[1], 'float32'), 0, 1)),
@@ -129,7 +131,7 @@ class _Attention(object):
         self.model.add(Dense(nr_hidden, name='attend2',
             init='he_normal', W_regularizer=l2(L2), activation='relu'))
         self.model = TimeDistributed(self.model)
-    
+
     def __call__(self, sent1, sent2):
         def _outer(AB):
             att_ji = K.batch_dot(AB[1], K.permute_dimensions(AB[0], (0, 2, 1)))
@@ -158,7 +160,7 @@ class _SoftAlignment(object):
             return K.batch_dot(sm_att, mat)
         return merge([attention, sentence], mode=_normalize_attention,
                       output_shape=(self.max_length, self.nr_hidden)) # Shape: (i, n)
- 
+
 
 class _Comparison(object):
     def __init__(self, words, nr_hidden, L2=0.0, dropout=0.0):
@@ -176,10 +178,12 @@ class _Comparison(object):
 
     def __call__(self, sent, align, **kwargs):
         result = self.model(merge([sent, align], mode='concat')) # Shape: (i, n)
-        result = _GlobalSumPooling1D()(result, mask=self.words)
+        avged = GlobalAveragePooling1D()(result, mask=self.words)
-        result = BatchNormalization()(result)
+        maxed = GlobalMaxPooling1D()(result, mask=self.words)
+        merged = merge([avged, maxed])
+        result = BatchNormalization()(merged)
         return result
- 
+
 
 class _Entailment(object):
     def __init__(self, nr_hidden, nr_out, dropout=0.0, L2=0.0):
@@ -251,7 +255,7 @@ def test_fit_model():
     shape = (10, 16, 3)
     settings = {'lr': 0.001, 'dropout': 0.2, 'gru_encode':True}
     model = build_model(vectors, shape, settings)
-    
+
     train_X = _generate_X(20, shape[0], vectors.shape[1])
     train_Y = _generate_Y(20, shape[2])
     dev_X = _generate_X(15, shape[0], vectors.shape[1])
@@ -261,6 +265,4 @@ def test_fit_model():
               batch_size=4)
 
 
-
-
 __all__ = [build_model]

examples/keras_parikh_entailment/spacy_hook.py

@@ -1,33 +1,40 @@
 from keras.models import model_from_json
 import numpy
 import numpy.random
+import json
+from spacy.tokens.span import Span
+
+try:
+    import cPickle as pickle
+except ImportError:
+    import pickle
 
 
 class KerasSimilarityShim(object):
     @classmethod
-    def load(cls, path, nlp, get_features=None):
+    def load(cls, path, nlp, get_features=None, max_length=100):
         if get_features is None:
-            get_features = doc2ids
+            get_features = get_word_ids
         with (path / 'config.json').open() as file_:
-            config = json.load(file_)
+            model = model_from_json(file_.read())
-        model = model_from_json(config['model'])
         with (path / 'model').open('rb') as file_:
             weights = pickle.load(file_)
         embeddings = get_embeddings(nlp.vocab)
         model.set_weights([embeddings] + weights)
-        return cls(model, get_features=get_features)
+        return cls(model, get_features=get_features, max_length=max_length)
 
-    def __init__(self, model, get_features=None):
+    def __init__(self, model, get_features=None, max_length=100):
         self.model = model
         self.get_features = get_features
+        self.max_length = max_length
 
     def __call__(self, doc):
         doc.user_hooks['similarity'] = self.predict
         doc.user_span_hooks['similarity'] = self.predict
-    
+
     def predict(self, doc1, doc2):
-        x1 = self.get_features(doc1)
+        x1 = self.get_features([doc1], max_length=self.max_length, tree_truncate=True)
-        x2 = self.get_features(doc2)
+        x2 = self.get_features([doc2], max_length=self.max_length, tree_truncate=True)
         scores = self.model.predict([x1, x2])
         return scores[0]
 
@@ -45,7 +52,10 @@ def get_word_ids(docs, rnn_encode=False, tree_truncate=False, max_length=100, nr
     Xs = numpy.zeros((len(docs), max_length), dtype='int32')
     for i, doc in enumerate(docs):
         if tree_truncate:
-            queue = [sent.root for sent in doc.sents]
+            if isinstance(doc, Span):
+                queue = [doc.root]
+            else:
+                queue = [sent.root for sent in doc.sents]
         else:
             queue = list(doc)
         words = []
@@ -71,7 +81,9 @@ def get_word_ids(docs, rnn_encode=False, tree_truncate=False, max_length=100, nr
 
 
 def create_similarity_pipeline(nlp):
-    return [SimilarityModel.load(
+    return [
-                nlp.path / 'similarity',
+        nlp.tagger,
-                nlp,
+        nlp.entity,
-                feature_extracter=get_features)]
+        nlp.parser,
+        KerasSimilarityShim.load(nlp.path / 'similarity', nlp, max_length=10)
+    ]

setup.py

@@ -31,6 +31,7 @@ PACKAGES = [
     'spacy.pt',
     'spacy.nl',
     'spacy.sv',
+    'spacy.fi',
     'spacy.language_data',
     'spacy.serialize',
     'spacy.syntax',

spacy/__init__.py

@@ -13,7 +13,7 @@ from . import fr
 from . import pt
 from . import nl
 from . import sv
-
+from . import fi
 
 try:
     basestring
@@ -31,6 +31,8 @@ set_lang_class(hu.Hungarian.lang, hu.Hungarian)
 set_lang_class(zh.Chinese.lang, zh.Chinese)
 set_lang_class(nl.Dutch.lang, nl.Dutch)
 set_lang_class(sv.Swedish.lang, sv.Swedish)
+set_lang_class(fi.Finnish.lang, fi.Finnish)
+
 
 
 def load(name, **overrides):

spacy/fi/__init__.py

@@ -0,0 +1,17 @@
+# encoding: utf8
+from __future__ import unicode_literals, print_function
+
+from ..language import Language
+from ..attrs import LANG
+from .language_data import *
+
+
+class Finnish(Language):
+    lang = 'fi'
+
+    class Defaults(Language.Defaults):
+        lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
+        lex_attr_getters[LANG] = lambda text: 'fi'
+
+        tokenizer_exceptions = TOKENIZER_EXCEPTIONS
+        stop_words = STOP_WORDS

spacy/fi/language_data.py

@@ -0,0 +1,17 @@
+# encoding: utf8
+from __future__ import unicode_literals
+
+from .. import language_data as base
+from ..language_data import update_exc, strings_to_exc
+
+from .stop_words import STOP_WORDS
+from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
+
+
+STOP_WORDS = set(STOP_WORDS)
+
+TOKENIZER_EXCEPTIONS = dict(TOKENIZER_EXCEPTIONS)
+update_exc(TOKENIZER_EXCEPTIONS, strings_to_exc(base.EMOTICONS))
+
+
+__all__ = ["TOKENIZER_EXCEPTIONS", "STOP_WORDS"]

spacy/fi/stop_words.py

@@ -0,0 +1,114 @@
+# encoding: utf8
+from __future__ import unicode_literals
+
+# Source https://github.com/stopwords-iso/stopwords-fi/blob/master/stopwords-fi.txt
+# Reformatted with some minor corrections
+
+STOP_WORDS = set("""
+
+aiemmin aika aikaa aikaan aikaisemmin aikaisin aikana aikoina aikoo aikovat
+aina ainakaan ainakin ainoa ainoat aiomme aion aiotte aivan ajan alas alemmas
+alkuisin alkuun alla alle aloitamme aloitan aloitat aloitatte aloitattivat
+aloitettava aloitettavaksi aloitettu aloitimme aloitin aloitit aloititte
+aloittaa aloittamatta aloitti aloittivat alta aluksi alussa alusta annettavaksi
+annettava annettu ansiosta antaa antamatta antoi apu asia asiaa asian asiasta
+asiat asioiden asioihin asioita asti avuksi avulla avun avutta
+
+edelle edelleen edellä edeltä edemmäs edes edessä edestä ehkä ei eikä eilen
+eivät eli ellei elleivät ellemme ellen ellet ellette emme en enemmän eniten
+ennen ensi ensimmäinen ensimmäiseksi ensimmäisen ensimmäisenä ensimmäiset
+ensimmäisiksi ensimmäisinä ensimmäisiä ensimmäistä ensin entinen entisen
+entisiä entisten entistä enää eri erittäin erityisesti eräiden eräs eräät esi
+esiin esillä esimerkiksi et eteen etenkin ette ettei että
+
+halua haluaa haluamatta haluamme haluan haluat haluatte haluavat halunnut
+halusi halusimme halusin halusit halusitte halusivat halutessa haluton he hei
+heidän heidät heihin heille heillä heiltä heissä heistä heitä helposti heti
+hetkellä hieman hitaasti huolimatta huomenna hyvien hyviin hyviksi hyville
+hyviltä hyvin hyvinä hyvissä hyvistä hyviä hyvä hyvät hyvää hän häneen hänelle
+hänellä häneltä hänen hänessä hänestä hänet häntä
+
+ihan ilman ilmeisesti itse itsensä itseään
+
+ja jo johon joiden joihin joiksi joilla joille joilta joina joissa joista joita
+joka jokainen jokin joko joksi joku jolla jolle jolloin jolta jompikumpi jona
+jonka jonkin jonne joo jopa jos joskus jossa josta jota jotain joten jotenkin
+jotenkuten jotka jotta jouduimme jouduin jouduit jouduitte joudumme joudun
+joudutte joukkoon joukossa joukosta joutua joutui joutuivat joutumaan joutuu
+joutuvat juuri jälkeen jälleen jää
+
+kahdeksan kahdeksannen kahdella kahdelle kahdelta kahden kahdessa kahdesta
+kahta kahteen kai kaiken kaikille kaikilta kaikkea kaikki kaikkia kaikkiaan
+kaikkialla kaikkialle kaikkialta kaikkien kaikkiin kaksi kannalta kannattaa
+kanssa kanssaan kanssamme kanssani kanssanne kanssasi kauan kauemmas kaukana
+kautta kehen keiden keihin keiksi keille keillä keiltä keinä keissä keistä
+keitten keittä keitä keneen keneksi kenelle kenellä keneltä kenen kenenä
+kenessä kenestä kenet kenettä kenties kerran kerta kertaa keskellä kesken
+keskimäärin ketkä ketä kiitos kohti koko kokonaan kolmas kolme kolmen kolmesti
+koska koskaan kovin kuin kuinka kuinkaan kuitenkaan kuitenkin kuka kukaan kukin
+kumpainen kumpainenkaan kumpi kumpikaan kumpikin kun kuten kuuden kuusi kuutta
+kylliksi kyllä kymmenen kyse
+
+liian liki lisäksi lisää lla luo luona lähekkäin lähelle lähellä läheltä
+lähemmäs lähes lähinnä lähtien läpi
+
+mahdollisimman mahdollista me meidän meidät meihin meille meillä meiltä meissä
+meistä meitä melkein melko menee menemme menen menet menette menevät meni
+menimme menin menit menivät mennessä mennyt menossa mihin miksi mikä mikäli
+mikään mille milloin milloinkan millä miltä minkä minne minua minulla minulle
+minulta minun minussa minusta minut minuun minä missä mistä miten mitkä mitä
+mitään moi molemmat mones monesti monet moni moniaalla moniaalle moniaalta
+monta muassa muiden muita muka mukaan mukaansa mukana mutta muu muualla muualle
+muualta muuanne muulloin muun muut muuta muutama muutaman muuten myöhemmin myös
+myöskin myöskään myötä
+
+ne neljä neljän neljää niiden niihin niiksi niille niillä niiltä niin niinä
+niissä niistä niitä noiden noihin noiksi noilla noille noilta noin noina noissa
+noista noita nopeammin nopeasti nopeiten nro nuo nyt näiden näihin näiksi
+näille näillä näiltä näin näinä näissä näistä näitä nämä
+
+ohi oikea oikealla oikein ole olemme olen olet olette oleva olevan olevat oli
+olimme olin olisi olisimme olisin olisit olisitte olisivat olit olitte olivat
+olla olleet ollut oma omaa omaan omaksi omalle omalta oman omassa omat omia
+omien omiin omiksi omille omilta omissa omista on onkin onko ovat
+
+paikoittain paitsi pakosti paljon paremmin parempi parhaillaan parhaiten
+perusteella peräti pian pieneen pieneksi pienelle pienellä pieneltä pienempi
+pienestä pieni pienin poikki puolesta puolestaan päälle
+
+runsaasti
+
+saakka sama samaa samaan samalla saman samat samoin sata sataa satojen se
+seitsemän sekä sen seuraavat siellä sieltä siihen siinä siis siitä sijaan siksi
+sille silloin sillä silti siltä sinne sinua sinulla sinulle sinulta sinun
+sinussa sinusta sinut sinuun sinä sisäkkäin sisällä siten sitten sitä ssa sta
+suoraan suuntaan suuren suuret suuri suuria suurin suurten
+
+taa taas taemmas tahansa tai takaa takaisin takana takia tallä tapauksessa
+tarpeeksi tavalla tavoitteena te teidän teidät teihin teille teillä teiltä
+teissä teistä teitä tietysti todella toinen toisaalla toisaalle toisaalta
+toiseen toiseksi toisella toiselle toiselta toisemme toisen toisensa toisessa
+toisesta toista toistaiseksi toki tosin tuhannen tuhat tule tulee tulemme tulen
+tulet tulette tulevat tulimme tulin tulisi tulisimme tulisin tulisit tulisitte
+tulisivat tulit tulitte tulivat tulla tulleet tullut tuntuu tuo tuohon tuoksi
+tuolla tuolle tuolloin tuolta tuon tuona tuonne tuossa tuosta tuota tuskin tykö
+tähän täksi tälle tällä tällöin tältä tämä tämän tänne tänä tänään tässä tästä
+täten tätä täysin täytyvät täytyy täällä täältä
+
+ulkopuolella usea useasti useimmiten usein useita uudeksi uudelleen uuden uudet
+uusi uusia uusien uusinta uuteen uutta
+
+vaan vai vaiheessa vaikea vaikean vaikeat vaikeilla vaikeille vaikeilta
+vaikeissa vaikeista vaikka vain varmasti varsin varsinkin varten vasen
+vasemmalla vasta vastaan vastakkain vastan verran vielä vierekkäin vieressä
+vieri viiden viime viimeinen viimeisen viimeksi viisi voi voidaan voimme voin
+voisi voit voitte voivat vuoden vuoksi vuosi vuosien vuosina vuotta vähemmän
+vähintään vähiten vähän välillä
+
+yhdeksän yhden yhdessä yhteen yhteensä yhteydessä yhteyteen yhtä yhtäälle
+yhtäällä yhtäältä yhtään yhä yksi yksin yksittäin yleensä ylemmäs yli ylös
+ympäri
+
+älköön älä
+
+""".split())

spacy/fi/tokenizer_exceptions.py

@@ -0,0 +1,202 @@
+# encoding: utf8
+from __future__ import unicode_literals
+
+from ..symbols import *
+from ..language_data import PRON_LEMMA
+
+# Source https://www.cs.tut.fi/~jkorpela/kielenopas/5.5.html
+
+TOKENIZER_EXCEPTIONS = {
+    "aik.": [
+        {ORTH: "aik.", LEMMA: "aikaisempi"}
+    ],
+    "alk.": [
+        {ORTH: "alk.", LEMMA: "alkaen"}
+    ],
+    "alv.": [
+        {ORTH: "alv.", LEMMA: "arvonlisävero"}
+    ],
+    "ark.": [
+        {ORTH: "ark.", LEMMA: "arkisin"}
+    ],
+    "as.": [
+        {ORTH: "as.", LEMMA: "asunto"}
+    ],
+    "ed.": [
+        {ORTH: "ed.", LEMMA: "edellinen"}
+    ],
+    "esim.": [
+        {ORTH: "esim.", LEMMA: "esimerkki"}
+    ],
+    "huom.": [
+        {ORTH: "huom.", LEMMA: "huomautus"}
+    ],
+    "jne.": [
+        {ORTH: "jne.", LEMMA: "ja niin edelleen"}
+    ],
+    "joht.": [
+        {ORTH: "joht.", LEMMA: "johtaja"}
+    ],
+    "k.": [
+        {ORTH: "k.", LEMMA: "kuollut"}
+    ],
+    "ks.": [
+        {ORTH: "ks.", LEMMA: "katso"}
+    ],
+    "lk.": [
+        {ORTH: "lk.", LEMMA: "luokka"}
+    ],
+    "lkm.": [
+        {ORTH: "lkm.", LEMMA: "lukumäärä"}
+    ],
+    "lyh.": [
+        {ORTH: "lyh.", LEMMA: "lyhenne"}
+    ],
+    "läh.": [
+        {ORTH: "läh.", LEMMA: "lähettäjä"}
+    ],
+    "miel.": [
+        {ORTH: "miel.", LEMMA: "mieluummin"}
+    ],
+    "milj.": [
+        {ORTH: "milj.", LEMMA: "miljoona"}
+    ],
+    "mm.": [
+        {ORTH: "mm.", LEMMA: "muun muassa"}
+    ],
+    "myöh.": [
+        {ORTH: "myöh.", LEMMA: "myöhempi"}
+    ],
+    "n.": [
+        {ORTH: "n.", LEMMA: "noin"}
+    ],
+    "nimim.": [
+        {ORTH: "nimim.", LEMMA: "nimimerkki"}
+    ],
+    "ns.": [
+        {ORTH: "ns.", LEMMA: "niin sanottu"}
+    ],
+    "nyk.": [
+        {ORTH: "nyk.", LEMMA: "nykyinen"}
+    ],
+    "oik.": [
+        {ORTH: "oik.", LEMMA: "oikealla"}
+    ],
+    "os.": [
+        {ORTH: "os.", LEMMA: "osoite"}
+    ],
+    "p.": [
+        {ORTH: "p.", LEMMA: "päivä"}
+    ],
+    "par.": [
+        {ORTH: "par.", LEMMA: "paremmin"}
+    ],
+    "per.": [
+        {ORTH: "per.", LEMMA: "perustettu"}
+    ],
+    "pj.": [
+        {ORTH: "pj.", LEMMA: "puheenjohtaja"}
+    ],
+    "puh.joht.": [
+        {ORTH: "puh.joht.", LEMMA: "puheenjohtaja"}
+    ],
+    "prof.": [
+        {ORTH: "prof.", LEMMA: "professori"}
+    ],
+    "puh.": [
+        {ORTH: "puh.", LEMMA: "puhelin"}
+    ],
+    "pvm.": [
+        {ORTH: "pvm.", LEMMA: "päivämäärä"}
+    ],
+    "rak.": [
+        {ORTH: "rak.", LEMMA: "rakennettu"}
+    ],
+    "ry.": [
+        {ORTH: "ry.", LEMMA: "rekisteröity yhdistys"}
+    ],
+    "s.": [
+        {ORTH: "s.", LEMMA: "sivu"}
+    ],
+    "siht.": [
+        {ORTH: "siht.", LEMMA: "sihteeri"}
+    ],
+    "synt.": [
+        {ORTH: "synt.", LEMMA: "syntynyt"}
+    ],
+    "t.": [
+        {ORTH: "t.", LEMMA: "toivoo"}
+    ],
+    "tark.": [
+        {ORTH: "tark.", LEMMA: "tarkastanut"}
+    ],
+    "til.": [
+        {ORTH: "til.", LEMMA: "tilattu"}
+    ],
+    "tms.": [
+        {ORTH: "tms.", LEMMA: "tai muuta sellaista"}
+    ],
+    "toim.": [
+        {ORTH: "toim.", LEMMA: "toimittanut"}
+    ],
+    "v.": [
+        {ORTH: "v.", LEMMA: "vuosi"}
+    ],
+    "vas.": [
+        {ORTH: "vas.", LEMMA: "vasen"}
+    ],
+    "vast.": [
+        {ORTH: "vast.", LEMMA: "vastaus"}
+    ],
+    "vrt.": [
+        {ORTH: "vrt.", LEMMA: "vertaa"}
+    ],
+    "yht.": [
+        {ORTH: "yht.", LEMMA: "yhteensä"}
+    ],
+    "yl.": [
+        {ORTH: "yl.", LEMMA: "yleinen"}
+    ],
+    "ym.": [
+        {ORTH: "ym.", LEMMA: "ynnä muuta"}
+    ],
+    "yms.": [
+        {ORTH: "yms.", LEMMA: "ynnä muuta sellaista"}
+    ],
+    "yo.": [
+        {ORTH: "yo.", LEMMA: "ylioppilas"}
+    ],
+    "yliopp.": [
+        {ORTH: "yliopp.", LEMMA: "ylioppilas"}
+    ],
+    "ao.": [
+        {ORTH: "ao.", LEMMA: "asianomainen"}
+    ],
+    "em.": [
+        {ORTH: "em.", LEMMA: "edellä mainittu"}
+    ],
+    "ko.": [
+        {ORTH: "ko.", LEMMA: "kyseessä oleva"}
+    ],
+    "ml.": [
+        {ORTH: "ml.", LEMMA: "mukaan luettuna"}
+    ],
+    "po.": [
+        {ORTH: "po.", LEMMA: "puheena oleva"}
+    ],
+    "so.": [
+        {ORTH: "so.", LEMMA: "se on"}
+    ],
+    "ts.": [
+        {ORTH: "ts.", LEMMA: "toisin sanoen"}
+    ],
+    "vm.": [
+        {ORTH: "vm.", LEMMA: "viimeksi mainittu"}
+    ],
+    "siht.": [
+        {ORTH: "siht.", LEMMA: "sihteeri"}
+    ],
+    "srk.": [
+        {ORTH: "srk.", LEMMA: "seurakunta"}
+    ]
+}

spacy/language_data/emoticons.py

@@ -50,6 +50,7 @@ EMOTICONS = set("""
 :/
 :-/
 =/
+=|
 :|
 :-|
 :1

spacy/language_data/punctuation.py

@@ -72,7 +72,7 @@ HYPHENS = _HYPHENS.strip().replace(' ', '|')
 # Prefixes
 
 TOKENIZER_PREFIXES = (
-    ['§', '%', r'\+'] +
+    ['§', '%', '=', r'\+'] +
     LIST_PUNCT +
     LIST_ELLIPSES +
     LIST_QUOTES +
@@ -106,7 +106,7 @@ TOKENIZER_INFIXES = (
         r'(?<=[0-9])[+\-\*^](?=[0-9-])',
         r'(?<=[{al}])\.(?=[{au}])'.format(al=ALPHA_LOWER, au=ALPHA_UPPER),
         r'(?<=[{a}]),(?=[{a}])'.format(a=ALPHA),
-        r'(?<=[{a}])(?:{h})(?=[{a}])'.format(a=ALPHA, h=HYPHENS),
+        r'(?<=[{a}])[?";:=,.]*(?:{h})(?=[{a}])'.format(a=ALPHA, h=HYPHENS),
         r'(?<=[{a}"])[:<>=](?=[{a}])'.format(a=ALPHA)
     ]
 )

spacy/sv/language_data.py

@@ -5,12 +5,14 @@ from .. import language_data as base
 from ..language_data import update_exc, strings_to_exc
 
 from .stop_words import STOP_WORDS
+from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS, ORTH_ONLY
 
 
 STOP_WORDS = set(STOP_WORDS)
 
-
+TOKENIZER_EXCEPTIONS = dict(TOKENIZER_EXCEPTIONS)
-TOKENIZER_EXCEPTIONS = strings_to_exc(base.EMOTICONS)
+update_exc(TOKENIZER_EXCEPTIONS, strings_to_exc(ORTH_ONLY))
+update_exc(TOKENIZER_EXCEPTIONS, strings_to_exc(base.EMOTICONS))
 update_exc(TOKENIZER_EXCEPTIONS, strings_to_exc(base.ABBREVIATIONS))
 
 

spacy/sv/lemma_rules.py

@@ -0,0 +1,45 @@
+# encoding: utf8
+from __future__ import unicode_literals
+
+
+LEMMA_RULES = {
+    "noun": [
+        ["t", ""],
+        ["n", ""],
+        ["na", ""],
+        ["na", "e"],
+        ["or", "a"],
+        ["orna", "a"],
+        ["et", ""],
+        ["en", ""],
+        ["en", "e"],
+        ["er", ""],
+        ["erna", ""],
+        ["ar", "e"],
+        ["ar", ""],
+        ["lar", "el"],
+        ["arna", "e"],
+        ["arna", ""],
+        ["larna", "el"]
+    ],
+
+    "adj": [
+        ["are", ""],
+        ["ast", ""],
+        ["re", ""],
+        ["st", ""],
+        ["ägre", "åg"],
+        ["ägst", "åg"],
+        ["ängre", "ång"],
+        ["ängst", "ång"],
+        ["örre", "or"],
+        ["örst", "or"],
+    ],
+
+    "punct": [
+        ["“", "\""],
+        ["”", "\""],
+        ["\u2018", "'"],
+        ["\u2019", "'"]
+    ]
+}

spacy/sv/tokenizer_exceptions.py

@@ -5,7 +5,31 @@ from ..symbols import *
 from ..language_data import PRON_LEMMA
 
 
-TOKENIZER_EXCEPTIONS = {
+EXC = {}
+
+# Verbs
+
+for verb_data in [
+    {ORTH: "driver"},
+    {ORTH: "kör"},
+    {ORTH: "hörr", LEMMA: "hör"},
+    {ORTH: "fattar"},
+    {ORTH: "hajar", LEMMA: "förstår"},
+    {ORTH: "lever"},
+    {ORTH: "serr", LEMMA: "ser"},
+    {ORTH: "fixar"}
+]:
+    verb_data_tc = dict(verb_data)
+    verb_data_tc[ORTH] = verb_data_tc[ORTH].title()
+
+    for data in [verb_data, verb_data_tc]:
+        EXC[data[ORTH] + "u"] = [
+            dict(data),
+            {ORTH: "u", LEMMA: PRON_LEMMA, NORM: "du"}
+        ]
+
+
+ABBREVIATIONS = {
     "jan.": [
         {ORTH: "jan.", LEMMA: "januari"}
     ],
@@ -63,6 +87,63 @@ TOKENIZER_EXCEPTIONS = {
     "sön.": [
         {ORTH: "sön.", LEMMA: "söndag"}
     ],
+    "Jan.": [
+        {ORTH: "Jan.", LEMMA: "Januari"}
+    ],
+    "Febr.": [
+        {ORTH: "Febr.", LEMMA: "Februari"}
+    ],
+    "Feb.": [
+        {ORTH: "Feb.", LEMMA: "Februari"}
+    ],
+    "Apr.": [
+        {ORTH: "Apr.", LEMMA: "April"}
+    ],
+    "Jun.": [
+        {ORTH: "Jun.", LEMMA: "Juni"}
+    ],
+    "Jul.": [
+        {ORTH: "Jul.", LEMMA: "Juli"}
+    ],
+    "Aug.": [
+        {ORTH: "Aug.", LEMMA: "Augusti"}
+    ],
+    "Sept.": [
+        {ORTH: "Sept.", LEMMA: "September"}
+    ],
+    "Sep.": [
+        {ORTH: "Sep.", LEMMA: "September"}
+    ],
+    "Okt.": [
+        {ORTH: "Okt.", LEMMA: "Oktober"}
+    ],
+    "Nov.": [
+        {ORTH: "Nov.", LEMMA: "November"}
+    ],
+    "Dec.": [
+        {ORTH: "Dec.", LEMMA: "December"}
+    ],
+    "Mån.": [
+        {ORTH: "Mån.", LEMMA: "Måndag"}
+    ],
+    "Tis.": [
+        {ORTH: "Tis.", LEMMA: "Tisdag"}
+    ],
+    "Ons.": [
+        {ORTH: "Ons.", LEMMA: "Onsdag"}
+    ],
+    "Tors.": [
+        {ORTH: "Tors.", LEMMA: "Torsdag"}
+    ],
+    "Fre.": [
+        {ORTH: "Fre.", LEMMA: "Fredag"}
+    ],
+    "Lör.": [
+        {ORTH: "Lör.", LEMMA: "Lördag"}
+    ],
+    "Sön.": [
+        {ORTH: "Sön.", LEMMA: "Söndag"}
+    ],
     "sthlm": [
         {ORTH: "sthlm", LEMMA: "Stockholm"}
     ],
@@ -72,6 +153,10 @@ TOKENIZER_EXCEPTIONS = {
 }
 
 
+TOKENIZER_EXCEPTIONS = dict(EXC)
+TOKENIZER_EXCEPTIONS.update(ABBREVIATIONS)
+
+
 ORTH_ONLY = [
     "ang.",
     "anm.",
@@ -107,7 +192,6 @@ ORTH_ONLY = [
     "p.g.a.",
     "ref.",
     "resp.",
-    "s.",
     "s.a.s.",
     "s.k.",
     "st.",

spacy/tests/conftest.py

@@ -10,6 +10,7 @@ from ..pt import Portuguese
 from ..nl import Dutch
 from ..sv import Swedish
 from ..hu import Hungarian
+from ..fi import Finnish
 from ..tokens import Doc
 from ..strings import StringStore
 from ..lemmatizer import Lemmatizer
@@ -23,7 +24,7 @@ import pytest
 
 
 LANGUAGES = [English, German, Spanish, Italian, French, Portuguese, Dutch,
-             Swedish, Hungarian]
+             Swedish, Hungarian, Finnish]
 
 
 @pytest.fixture(params=LANGUAGES)
@@ -62,6 +63,16 @@ def hu_tokenizer():
     return Hungarian.Defaults.create_tokenizer()
 
 
+@pytest.fixture
+def fi_tokenizer():
+    return Finnish.Defaults.create_tokenizer()
+
+
+@pytest.fixture
+def sv_tokenizer():
+    return Swedish.Defaults.create_tokenizer()
+
+
 @pytest.fixture
 def stringstore():
     return StringStore()

spacy/tests/fi/test_tokenizer.py

@@ -0,0 +1,18 @@
+# encoding: utf8
+from __future__ import unicode_literals
+
+import pytest
+
+ABBREVIATION_TESTS = [
+    ('Hyvää uutta vuotta t. siht. Niemelä!', ['Hyvää', 'uutta', 'vuotta', 't.', 'siht.', 'Niemelä', '!']),
+    ('Paino on n. 2.2 kg', ['Paino', 'on', 'n.', '2.2', 'kg'])
+]
+
+TESTCASES = ABBREVIATION_TESTS
+
+
+@pytest.mark.parametrize('text,expected_tokens', TESTCASES)
+def test_tokenizer_handles_testcases(fi_tokenizer, text, expected_tokens):
+    tokens = fi_tokenizer(text)
+    token_list = [token.text for token in tokens if not token.is_space]
+    assert expected_tokens == token_list

spacy/tests/regression/test_issue792.py

@@ -0,0 +1,12 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import pytest
+
+
+@pytest.mark.xfail
+@pytest.mark.parametrize('text', ["This is a string ", "This is a string\u0020"])
+def test_issue792(en_tokenizer, text):
+    """Test for Issue #792: Trailing whitespace is removed after parsing."""
+    doc = en_tokenizer(text)
+    assert doc.text_with_ws == text

spacy/tests/regression/test_issue801.py

@@ -0,0 +1,19 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import pytest
+
+
+@pytest.mark.parametrize('text,tokens', [
+    ('"deserve,"--and', ['"', "deserve", ',"--', "and"]),
+    ("exception;--exclusive", ["exception", ";--", "exclusive"]),
+    ("day.--Is", ["day", ".--", "Is"]),
+    ("refinement:--just", ["refinement", ":--", "just"]),
+    ("memories?--To", ["memories", "?--", "To"]),
+    ("Useful.=--Therefore", ["Useful", ".=--", "Therefore"]),
+    ("=Hope.=--Pandora", ["=", "Hope", ".=--", "Pandora"])])
+def test_issue801(en_tokenizer, text, tokens):
+    """Test that special characters + hyphens are split correctly."""
+    doc = en_tokenizer(text)
+    assert len(doc) == len(tokens)
+    assert [t.text for t in doc] == tokens

spacy/tests/regression/test_issue805.py

@@ -0,0 +1,15 @@
+# encoding: utf8
+from __future__ import unicode_literals
+
+import pytest
+
+SV_TOKEN_EXCEPTION_TESTS = [
+    ('Smörsåsen används bl.a. till fisk', ['Smörsåsen', 'används', 'bl.a.', 'till', 'fisk']),
+    ('Jag kommer först kl. 13 p.g.a. diverse förseningar', ['Jag', 'kommer', 'först', 'kl.', '13', 'p.g.a.', 'diverse', 'förseningar'])
+]
+
+@pytest.mark.parametrize('text,expected_tokens', SV_TOKEN_EXCEPTION_TESTS)
+def test_issue805(sv_tokenizer, text, expected_tokens):
+    tokens = sv_tokenizer(text)
+    token_list = [token.text for token in tokens if not token.is_space]
+    assert expected_tokens == token_list

spacy/tests/sv/test_tokenizer.py

@@ -0,0 +1,24 @@
+# encoding: utf8
+from __future__ import unicode_literals
+
+import pytest
+
+
+SV_TOKEN_EXCEPTION_TESTS = [
+    ('Smörsåsen används bl.a. till fisk', ['Smörsåsen', 'används', 'bl.a.', 'till', 'fisk']),
+    ('Jag kommer först kl. 13 p.g.a. diverse förseningar', ['Jag', 'kommer', 'först', 'kl.', '13', 'p.g.a.', 'diverse', 'förseningar'])
+]
+
+
+@pytest.mark.parametrize('text,expected_tokens', SV_TOKEN_EXCEPTION_TESTS)
+def test_tokenizer_handles_exception_cases(sv_tokenizer, text, expected_tokens):
+    tokens = sv_tokenizer(text)
+    token_list = [token.text for token in tokens if not token.is_space]
+    assert expected_tokens == token_list
+
+
+@pytest.mark.parametrize('text', ["driveru", "hajaru", "Serru", "Fixaru"])
+def test_tokenizer_handles_verb_exceptions(sv_tokenizer, text):
+    tokens = sv_tokenizer(text)
+    assert len(tokens) == 2
+    assert tokens[1].text == "u"

spacy/tokens/doc.pyx

@@ -500,7 +500,8 @@ cdef class Doc:
         by the values of the given attribute ID.
 
         Example:
-            from spacy.en import English, attrs
+            from spacy.en import English
+            from spacy import attrs
             nlp = English()
             tokens = nlp(u'apple apple orange banana')
             tokens.count_by(attrs.ORTH)
@@ -585,9 +586,6 @@ cdef class Doc:
             elif attr_id == POS:
                 for i in range(length):
                     tokens[i].pos = <univ_pos_t>values[i]
-            elif attr_id == TAG:
-                for i in range(length):
-                    tokens[i].tag = <univ_pos_t>values[i]
             elif attr_id == DEP:
                 for i in range(length):
                     tokens[i].dep = values[i]

website/_harp.json

@@ -55,7 +55,7 @@
         },
 
         "V_CSS": "1.15",
-        "V_JS": "1.0",
+        "V_JS": "1.1",
         "DEFAULT_SYNTAX": "python",
         "ANALYTICS": "UA-58931649-1",
         "MAILCHIMP": {

website/assets/js/main.js

@@ -14,11 +14,11 @@
     const updateNav = () => {
         const vh = updateVh()
         const newScrollY = (window.pageYOffset || document.scrollTop) - (document.clientTop || 0)
-        scrollUp = newScrollY <= scrollY
+        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)
+        else if (!scrollUp || (isNaN(scrollY) || scrollY <= vh/2)) nav.classList.remove(fixedClass)
     }
 
     window.addEventListener('scroll', () => requestAnimationFrame(updateNav))

website/docs/api/language-models.jade

@@ -19,21 +19,6 @@ p spaCy currently supports the following languages and capabilities:
         each icon in [ "pro", "pro", "con", "pro", "pro", "pro", "pro", "con" ]
             +cell.u-text-center #[+procon(icon)]
 
-    +row
-        +cell Chinese #[code zh]
-        each icon in [ "pro", "con", "con", "con", "con", "con", "con", "con" ]
-            +cell.u-text-center #[+procon(icon)]
-
-    +row
-        +cell Spanish #[code es]
-        each icon in [ "pro", "con", "con", "con", "con", "con", "con", "con" ]
-            +cell.u-text-center #[+procon(icon)]
-
-p
-    |  Chinese tokenization requires the
-    |  #[+a("https://github.com/fxsjy/jieba") Jieba] library. Statistical
-    |  models are coming soon.
-
 
 +h(2, "alpha-support") Alpha support
 
@@ -42,8 +27,13 @@ p
     |  the existing language data and extending the tokenization patterns.
 
 +table([ "Language", "Source" ])
-    each language, code in { it: "Italian", fr: "French", pt: "Portuguese", nl: "Dutch", sv: "Swedish", hu: "Hungarian" }
+    each language, code in { zh: "Chinese", es: "Spanish", it: "Italian", fr: "French", pt: "Portuguese", nl: "Dutch", sv: "Swedish", fi: "Finnish", hu: "Hungarian" }
         +row
             +cell #{language} #[code=code]
             +cell
                 +src(gh("spaCy", "spacy/" + code)) spacy/#{code}
+
+p
+    |  Chinese tokenization requires the
+    |  #[+a("https://github.com/fxsjy/jieba") Jieba] library. Statistical
+    |  models are coming soon.

website/docs/usage/entity-recognition.jade

@@ -54,7 +54,7 @@ p
     doc = nlp(u'London is a big city in the United Kingdom.')
     doc.ents = []
     assert doc[0].ent_type_ == ''
-    doc.ents = [Span(0, 1, label='GPE')]
+    doc.ents = [Span(doc, 0, 1, label=doc.vocab.strings['GPE'])]
     assert doc[0].ent_type_ == 'GPE'
     doc.ents = []
     doc.ents = [(u'LondonCity', u'GPE', 0, 1)]

website/docs/usage/rule-based-matching.jade

@@ -20,7 +20,7 @@ p
     |  Once we've added the pattern, we can use the #[code matcher] as a
     |  callable, to receive a list of #[code (ent_id, start, end)] tuples.
     |  Note that #[code LOWER] and #[code IS_PUNCT] are data attributes
-    |  of #[code Matcher.attrs].
+    |  of #[code spacy.attrs].
 
 +code.
     from spacy.matcher import Matcher