Merge remote-tracking branch 'upstream/master'

CONTRIBUTING.md

@@ -87,7 +87,16 @@ Code should loosely follow [pep8](https://www.python.org/dev/peps/pep-0008/). Re
 
 ### Python conventions
 
-All Python code must be written in an **intersection of Python 2 and Python 3**. This is easy in Cython, but somewhat ugly in Python. We could use some extra utilities for this. Please pay particular attention to code that serialises json objects.
+All Python code must be written in an **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 should only live in [`spacy.compat`](spacy/compat.py). To distinguish them from the builtin functions, replacement functions are suffixed with an undersocre, for example `unicode_`. If you need to access the user's version or platform information, for example to show more specific error messages, you can use the `is_config()` helper function.
+
+```python
+from .compat import unicode_, json_dumps, is_config
+
+compatible_unicode = unicode_('hello world')
+compatible_json = json_dumps({'key': 'value'})
+if is_config(windows=True, python2=True):
+    print("You are using Python 2 on Windows.")
+```
 
 Code that interacts with the file-system should accept objects that follow the `pathlib.Path` API, without assuming that the object inherits from `pathlib.Path`. If the function is user-facing and takes a path as an argument, it should check whether the path is provided as a string. Strings should be converted to `pathlib.Path` objects.
 
@@ -95,6 +104,8 @@ At the time of writing (v1.7), spaCy's serialization and deserialization functio
 
 Although spaCy uses a lot of classes, inheritance is viewed with some suspicion — it's seen as a mechanism of last resort. You should discuss plans to extend the class hierarchy before implementing.
 
+We have a number of conventions around variable naming that are still being documented, and aren't 100% strict. A general policy is that instances of the class `Doc` should by default be called `doc`, `Token` `token`, `Lexeme` `lex`, `Vocab` `vocab` and `Language` `nlp`. You should avoid naming variables that are of other types these names. For instance, don't name a text string `doc` — you should usually call this `text`. Two general code style preferences further help with naming. First, lean away from introducing temporary variables, as these clutter your namespace. This is one reason why comprehension expressions are often preferred. Second, keep your functions shortish, so that can work in a smaller scope. Of course, this is a question of trade-offs.
+
 ### Cython conventions
 
 spaCy's core data structures are implemented as [Cython](http://cython.org/) `cdef` classes. Memory is managed through the `cymem.cymem.Pool` class, which allows you to allocate memory which will be freed when the `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 `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.
@@ -126,7 +137,7 @@ cdef int c_total(const int* int_array, int length) nogil:
     return total
 ```
 
-If this is confusing, consider that the compiler couldn't deal with `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 `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.
+If this is confusing, consider that the compiler couldn't deal with `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 `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.
 
 Your functions cannot be declared `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 `nogil` functions. However, if your function isn't `nogil`, you should compile your module with `cython -a --cplus my_module.pyx` and open the resulting `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.
 

README.rst

@@ -10,7 +10,7 @@ open-source software, released under the MIT license.
 
 📊 **Help us improve the library!** `Take the spaCy user survey <https://survey.spacy.io>`_.
 
-💫 **Version 1.7 out now!** `Read the release notes here. <https://github.com/explosion/spaCy/releases/>`_
+💫 **Version 1.8 out now!** `Read the release notes here. <https://github.com/explosion/spaCy/releases/>`_
 
 .. image:: https://img.shields.io/travis/explosion/spaCy/master.svg?style=flat-square
     :target: https://travis-ci.org/explosion/spaCy
@@ -320,6 +320,7 @@ and ``--model`` are optional and enable additional tests:
 =========== ============== ===========
 Version     Date           Description
 =========== ============== ===========
+`v1.8.0`_   ``2017-04-16`` Better NER training, saving and loading
 `v1.7.5`_   ``2017-04-07`` Bug fixes and new CLI commands
 `v1.7.3`_   ``2017-03-26`` Alpha support for Hebrew, new CLI commands and bug fixes
 `v1.7.2`_   ``2017-03-20`` Small fixes to beam parser and model linking
@@ -350,6 +351,7 @@ Version     Date           Description
 `v0.93`_    ``2015-09-22`` Bug fixes to word vectors
 =========== ============== ===========
 
+.. _v1.8.0: https://github.com/explosion/spaCy/releases/tag/v1.8.0
 .. _v1.7.5: https://github.com/explosion/spaCy/releases/tag/v1.7.5
 .. _v1.7.3: https://github.com/explosion/spaCy/releases/tag/v1.7.3
 .. _v1.7.2: https://github.com/explosion/spaCy/releases/tag/v1.7.2

examples/pos_tag.py

@@ -1,7 +1,8 @@
-'''Print part-of-speech tagged, true-cased, (very roughly) sentence-separated
+"""
+Print part-of-speech tagged, true-cased, (very roughly) sentence-separated
 text, with each "sentence" on a newline, and spaces between tokens. Supports
 multi-processing.
-'''
+"""
 from __future__ import print_function, unicode_literals, division
 import io
 import bz2
@@ -22,14 +23,14 @@ def parallelize(func, iterator, n_jobs, extra):
 
 
 def iter_texts_from_json_bz2(loc):
-    '''
+    """
     Iterator of unicode strings, one per document (here, a comment).
     
     Expects a a path to a BZ2 file, which should be new-line delimited JSON. The
     document text should be in a string field titled 'body'.
 
     This is the data format of the Reddit comments corpus.
-    '''
+    """
     with bz2.BZ2File(loc) as file_:
         for i, line in enumerate(file_):
             yield ujson.loads(line)['body']
@@ -80,7 +81,7 @@ def is_sent_begin(word):
 def main(in_loc, out_dir, n_workers=4, batch_size=100000):
     if not path.exists(out_dir):
         path.join(out_dir)
-    texts = partition(batch_size, iter_texts(in_loc))
+    texts = partition(batch_size, iter_texts_from_json_bz2(in_loc))
     parallelize(transform_texts, enumerate(texts), n_workers, [out_dir])
  
 

examples/training/train_ner_standalone.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 '''Example of training a named entity recognition system from scratch using spaCy
 
 This example is written to be self-contained and reasonably transparent.
@@ -81,7 +82,7 @@ def load_vocab(path):
 def init_ner_model(vocab, features=None):
     if features is None:
         features = tuple(EntityRecognizer.feature_templates)
-    return BeamEntityRecognizer(vocab, features=features)
+    return EntityRecognizer(vocab, features=features)
 
 
 def save_ner_model(model, path):
@@ -99,7 +100,7 @@ def save_ner_model(model, path):
 
 
 def load_ner_model(vocab, path):
-    return BeamEntityRecognizer.load(path, vocab)
+    return EntityRecognizer.load(path, vocab)
 
 
 class Pipeline(object):
@@ -110,18 +111,21 @@ class Pipeline(object):
             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 / 'vocab')
+        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, ner_model=None):
+    def __init__(self, vocab=None, tokenizer=None, entity=None):
         if vocab is None:
-            self.vocab = init_vocab()
+            vocab = init_vocab()
         if tokenizer is None:
             tokenizer = Tokenizer(vocab, {}, None, None, None)
-        if ner_model is None:
+        if entity is None:
-            self.entity = init_ner_model(self.vocab)
+            entity = init_ner_model(self.vocab)
+        self.vocab = vocab
+        self.tokenizer = tokenizer
+        self.entity = entity
         self.pipeline = [self.entity]
 
     def __call__(self, input_):
@@ -173,7 +177,7 @@ class Pipeline(object):
         save_ner_model(self.entity, path / 'ner')
 
 
-def train(nlp, train_examples, dev_examples, nr_epoch=5):
+def train(nlp, train_examples, dev_examples, ctx, nr_epoch=5):
     next_epoch = train_examples
     print("Iter", "Loss", "P", "R", "F")
     for i in range(nr_epoch):
@@ -186,14 +190,17 @@ def train(nlp, train_examples, dev_examples, nr_epoch=5):
                 next_epoch.append((input_, annot))
         random.shuffle(next_epoch)
         scores = nlp.evaluate(dev_examples)
+        report_scores(i, loss, scores)
+    nlp.average_weights()
+    scores = nlp.evaluate(dev_examples)
+    report_scores(channels, i+1, loss, scores)
+
+
+def report_scores(i, loss, scores):
     precision = '%.2f' % scores['ents_p']
     recall = '%.2f' % scores['ents_r']
     f_measure = '%.2f' % scores['ents_f']
-        print(i, int(loss), precision, recall, f_measure)
+    print('%d %s %s %s' % (int(loss), precision, recall, f_measure))
-    nlp.average_weights()
-    scores = nlp.evaluate(dev_examples)
-    print("After averaging")
-    print(scores['ents_p'], scores['ents_r'], scores['ents_f'])
 
 
 def read_examples(path):
@@ -221,15 +228,17 @@ def read_examples(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, train_loc, dev_loc, nr_epoch=10):
+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)
     dev_examples = read_examples(dev_loc)
-    nlp = Pipeline()
+    nlp = Pipeline.load(model_dir)
 
-    train(nlp, train_examples, list(dev_examples), nr_epoch)
+    train(nlp, train_examples, list(dev_examples), ctx, nr_epoch)
 
     nlp.save(model_dir)
 
 
 if __name__ == '__main__':
-    plac.call(main)
+    main()

examples/training/train_new_entity_type.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python
+"""
+Example of training an additional entity type
+
+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. 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.
+
+The actual training is performed by looping over the examples, and calling
+`nlp.entity.update()`. The `update()` method steps through the words of the
+input. At each word, it makes a prediction. It then consults the annotations
+provided on the 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.
+
+After training your model, you can save it to a directory. We recommend
+wrapping models as Python packages, for ease of deployment.
+
+For more details, see the documentation:
+* Training the Named Entity Recognizer: https://spacy.io/docs/usage/train-ner
+* Saving and loading models: https://spacy.io/docs/usage/saving-loading
+
+Developed for: spaCy 1.7.6
+Last tested for: spaCy 1.7.6
+"""
+# coding: utf8
+from __future__ import unicode_literals, print_function
+
+import random
+from pathlib import Path
+
+import spacy
+from spacy.gold import GoldParse
+from spacy.tagger import Tagger
+
+
+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]
+
+    for itn in range(20):
+        random.shuffle(train_data)
+        for raw_text, entity_offsets in train_data:
+            gold = GoldParse(doc, entities=entity_offsets)
+            doc = nlp.make_doc(raw_text)
+            nlp.tagger(doc)
+            loss = nlp.entity.update(doc, gold)
+    nlp.end_training()
+    if output_dir:
+        if not output_dir.exists():
+            output_dir.mkdir()
+        nlp.save_to_directory(output_dir)
+
+
+def main(model_name, output_directory=None):
+    print("Loading initial model", model_name)
+    nlp = spacy.load(model_name)
+    if output_directory is not None:
+        output_directory = Path(output_directory)
+
+    train_data = [
+        (
+            "Horses are too tall and they pretend to care about your feelings",
+            [(0, 6, 'ANIMAL')],
+        ),
+        (
+            "horses are too tall and they pretend to care about your feelings",
+            [(0, 6, 'ANIMAL')]
+        ),
+        (
+            "horses pretend to care about your feelings",
+            [(0, 6, 'ANIMAL')]
+        ),
+        (
+            "they pretend to care about your feelings, those horses",
+            [(48, 54, 'ANIMAL')]
+        )
+    ]
+    nlp.entity.add_label('ANIMAL')
+    train_ner(nlp, train_data, output_directory)
+
+    # Test that the entity is recognized
+    doc = nlp('Do you like horses?')
+    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')
+        doc2 = nlp2('Do you like horses?')
+        for ent in doc2.ents:
+            print(ent.label_, ent.text)
+
+
+if __name__ == '__main__':
+    import plac
+    plac.call(main)

fabfile.py

@@ -14,7 +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('virtualenv -p {lang} {env}'.format(lang=lang, env=VENV_DIR))
+    local('python -m virtualenv -p {lang} {env}'.format(lang=lang, env=VENV_DIR))
 
 
 def install():

requirements.txt

@@ -11,3 +11,4 @@ ujson>=1.35
 dill>=0.2,<0.3
 requests>=2.13.0,<3.0.0
 regex==2017.4.5
+pytest>=3.0.6,<4.0.0

spacy/__init__.py

@@ -1,53 +1,40 @@
 # coding: utf8
-from __future__ import unicode_literals, print_function
+from __future__ import unicode_literals
 
-import json
+from . import util
-from pathlib import Path
-from .util import set_lang_class, get_lang_class, parse_package_meta
 from .deprecated import resolve_model_name
 from .cli import info
 
-from . import en
+from . import en, de, zh, es, it, hu, fr, pt, nl, sv, fi, bn, he
-from . import de
-from . import zh
-from . import es
-from . import it
-from . import hu
-from . import fr
-from . import pt
-from . import nl
-from . import sv
-from . import fi
-from . import bn
-from . import he
-
-from .about import *
 
 
-set_lang_class(en.English.lang, en.English)
+_languages = (en.English, de.German, es.Spanish, pt.Portuguese, fr.French,
-set_lang_class(de.German.lang, de.German)
+             it.Italian, hu.Hungarian, zh.Chinese, nl.Dutch, sv.Swedish,
-set_lang_class(es.Spanish.lang, es.Spanish)
+             fi.Finnish, bn.Bengali, he.Hebrew)
-set_lang_class(pt.Portuguese.lang, pt.Portuguese)
+
-set_lang_class(fr.French.lang, fr.French)
+
-set_lang_class(it.Italian.lang, it.Italian)
+for _lang in _languages:
-set_lang_class(hu.Hungarian.lang, hu.Hungarian)
+    util.set_lang_class(_lang.lang, _lang)
-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)
-set_lang_class(bn.Bengali.lang, bn.Bengali)
-set_lang_class(he.Hebrew.lang, he.Hebrew)
 
 
 def load(name, **overrides):
-    data_path = overrides.get('path', util.get_data_path())
+    if overrides.get('path') in (None, False, True):
+        data_path = util.get_data_path()
         model_name = resolve_model_name(name)
-    meta = parse_package_meta(data_path, model_name, require=False)
+        model_path = data_path / model_name
+        if not model_path.exists():
+            lang_name = util.get_lang_class(name).lang
+            model_path = None
+            util.print_msg(
+                "Only loading the '{}' tokenizer.".format(lang_name),
+                title="Warning: no model found for '{}'".format(name))
+    else:
+        model_path = util.ensure_path(overrides['path'])
+        data_path = model_path.parent
+        model_name = ''
+    meta = util.parse_package_meta(data_path, model_name, require=False)
     lang = meta['lang'] if meta and 'lang' in meta else name
-    cls = get_lang_class(lang)
+    cls = util.get_lang_class(lang)
     overrides['meta'] = meta
-    model_path = Path(data_path / model_name)
-    if model_path.exists():
     overrides['path'] = model_path
-
     return cls(**overrides)

spacy/__main__.py

@@ -14,8 +14,9 @@ from spacy.cli import convert as cli_convert
 
 
 class CLI(object):
-    """Command-line interface for spaCy"""
+    """
-
+    Command-line interface for spaCy
+    """
     commands = ('download', 'link', 'info', 'package', 'train', 'model', 'convert')
 
     @plac.annotations(
@@ -29,7 +30,6 @@ class CLI(object):
         can be shortcut, model name or, if --direct flag is set, full model name
         with version.
         """
-
         cli_download(model, direct)
 
 
@@ -44,7 +44,6 @@ class CLI(object):
         either the name of a pip package, or the local path to the model data
         directory. Linking models allows loading them via spacy.load(link_name).
         """
-
         cli_link(origin, link_name, force)
 
 
@@ -58,23 +57,22 @@ class CLI(object):
         speficied as an argument, print model information. Flag --markdown
         prints details in Markdown for easy copy-pasting to GitHub issues.
         """
-
         cli_info(model, markdown)
 
 
     @plac.annotations(
         input_dir=("directory with model data", "positional", None, str),
         output_dir=("output parent directory", "positional", None, str),
+        meta=("path to meta.json", "option", "m", str),
         force=("force overwriting of existing folder in output directory", "flag", "f", bool)
     )
-    def package(self, input_dir, output_dir, force=False):
+    def package(self, input_dir, output_dir, meta=None, force=False):
         """
         Generate Python package for model data, including meta and required
         installation files. A new directory will be created in the specified
         output directory, and model data will be copied over.
         """
-
+        cli_package(input_dir, output_dir, meta, force)
-        cli_package(input_dir, output_dir, force)
 
 
     @plac.annotations(
@@ -93,7 +91,6 @@ class CLI(object):
         """
         Train a model. Expects data in spaCy's JSON format.
         """
-
         cli_train(lang, output_dir, train_data, dev_data, n_iter, not no_tagger,
                   not no_parser, not no_ner, parser_L1)
 
@@ -108,7 +105,6 @@ class CLI(object):
         """
         Initialize a new model and its data directory.
         """
-
         cli_model(lang, model_dir, freqs_data, clusters_data, vectors_data)
 
     @plac.annotations(
@@ -122,7 +118,6 @@ class CLI(object):
         Convert files into JSON format for use with train command and other
         experiment management functions.
         """
-
         cli_convert(input_file, output_dir, n_sents, morphology)
 
 

spacy/about.py

@@ -3,7 +3,7 @@
 # https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
 
 __title__ = 'spacy'
-__version__ = '1.7.5'
+__version__ = '1.8.0'
 __summary__ = 'Industrial-strength Natural Language Processing (NLP) with Python and Cython'
 __uri__ = 'https://spacy.io'
 __author__ = 'Matthew Honnibal'

spacy/attrs.pyx

@@ -1,3 +1,7 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+
 IDS = {
     "": NULL_ATTR,
     "IS_ALPHA": IS_ALPHA,
@@ -92,7 +96,8 @@ NAMES = [key for key, value in sorted(IDS.items(), key=lambda item: item[1])]
 
 
 def intify_attrs(stringy_attrs, strings_map=None, _do_deprecated=False):
-    '''Normalize a dictionary of attributes, converting them to ints.
+    """
+    Normalize a dictionary of attributes, converting them to ints.
 
     Arguments:
         stringy_attrs (dict):
@@ -105,7 +110,7 @@ def intify_attrs(stringy_attrs, strings_map=None, _do_deprecated=False):
         inty_attrs (dict):
             Attributes dictionary with keys and optionally values converted to
             ints.
-    '''
+    """
     inty_attrs = {}
     if _do_deprecated:
         if 'F' in stringy_attrs:

spacy/cfile.pyx

@@ -1,3 +1,6 @@
+# coding: utf8
+from __future__ import unicode_literals
+
 from libc.stdio cimport fopen, fclose, fread, fwrite
 from libc.string cimport memcpy
 

spacy/cli/convert.py

@@ -1,8 +1,7 @@
 # coding: utf8
-from __future__ import unicode_literals, division, print_function
+from __future__ import unicode_literals
 
-import io
+from pathlib import Path
-from pathlib import Path, PurePosixPath
 
 from .converters import conllu2json
 from .. import util

spacy/cli/converters/conllu2json.py

@@ -1,13 +1,14 @@
 # coding: utf8
-from __future__ import unicode_literals, division, print_function
+from __future__ import unicode_literals
 
 import json
-from ...gold import read_json_file, merge_sents
+from ...compat import json_dumps
 from ... import util
 
 
 def conllu2json(input_path, output_path, n_sents=10, use_morphology=False):
-    """Convert conllu files into JSON format for use with train cli.
+    """
+    Convert conllu files into JSON format for use with train cli.
     use_morphology parameter enables appending morphology to tags, which is
     useful for languages such as Spanish, where UD tags are not so rich.
     """
@@ -29,7 +30,8 @@ def conllu2json(input_path, output_path, n_sents=10, use_morphology=False):
 
     output_filename = input_path.parts[-1].replace(".conllu", ".json")
     output_file = output_path / output_filename
-    json.dump(docs, output_file.open('w', encoding='utf-8'), indent=2)
+    with output_file.open('w', encoding='utf-8') as f:
+        f.write(json_dumps(docs))
     util.print_msg("Created {} documents".format(len(docs)),
                    title="Generated output file {}".format(output_file))
 

spacy/cli/download.py

@@ -1,7 +1,6 @@
 # coding: utf8
 from __future__ import unicode_literals
 
-import pip
 import requests
 import os
 import subprocess

spacy/cli/info.py

@@ -4,6 +4,7 @@ from __future__ import unicode_literals
 import platform
 from pathlib import Path
 
+from ..compat import unicode_
 from .. import about
 from .. import util
 
@@ -13,12 +14,11 @@ def info(model=None, markdown=False):
         data = util.parse_package_meta(util.get_data_path(), model, require=True)
         model_path = Path(__file__).parent / util.get_data_path() / model
         if model_path.resolve() != model_path:
-            data['link'] = str(model_path)
+            data['link'] = unicode_(model_path)
-            data['source'] = str(model_path.resolve())
+            data['source'] = unicode_(model_path.resolve())
         else:
-            data['source'] = str(model_path)
+            data['source'] = unicode_(model_path)
         print_info(data, "model " + model, markdown)
-
     else:
         data = get_spacy_data()
         print_info(data, "spaCy", markdown)
@@ -26,10 +26,8 @@ def info(model=None, markdown=False):
 
 def print_info(data, title, markdown):
     title = "Info about {title}".format(title=title)
-
     if markdown:
         util.print_markdown(data, title=title)
-
     else:
         util.print_table(data, title=title)
 
@@ -37,7 +35,7 @@ def print_info(data, title, markdown):
 def get_spacy_data():
     return {
         'spaCy version': about.__version__,
-        'Location': str(Path(__file__).parent.parent),
+        'Location': unicode_(Path(__file__).parent.parent),
         'Platform': platform.platform(),
         'Python version': platform.python_version(),
         'Installed models': ', '.join(list_models())
@@ -49,5 +47,6 @@ def list_models():
     # won't show up in list, but it seems worth it
     exclude = ['cache', 'pycache', '__pycache__']
     data_path = util.get_data_path()
+    if data_path:
         models = [f.parts[-1] for f in data_path.iterdir() if f.is_dir()]
         return [m for m in models if m not in exclude]

spacy/cli/link.py

@@ -4,6 +4,7 @@ from __future__ import unicode_literals
 import pip
 from pathlib import Path
 import importlib
+from ..compat import unicode_, symlink_to
 from .. import util
 
 
@@ -20,7 +21,6 @@ def link_package(package_name, link_name, force=False):
     # Python's installation and import rules are very complicated.
     pkg = importlib.import_module(package_name)
     package_path = Path(pkg.__file__).parent.parent
-
     meta = get_meta(package_path, package_name)
     model_name = package_name + '-' + meta['version']
     model_path = package_path / package_name / model_name
@@ -29,7 +29,7 @@ def link_package(package_name, link_name, force=False):
 
 def symlink(model_path, link_name, force):
     model_path = Path(model_path)
-    if not Path(model_path).exists():
+    if not model_path.exists():
         util.sys_exit(
             "The data should be located in {p}".format(p=model_path),
             title="Can't locate model data")
@@ -43,13 +43,21 @@ def symlink(model_path, link_name, force):
     elif link_path.exists():
         link_path.unlink()
 
-    # Add workaround for Python 2 on Windows (see issue #909)
+    try:
-    if util.is_python2() and util.is_windows():
+        symlink_to(link_path, model_path)
-        import subprocess
+    except:
-        command = ['mklink', '/d', link_path, model_path]
+        # This is quite dirty, but just making sure other errors are caught so
-        subprocess.call(command, shell=True)
+        # users at least see a proper message.
-    else:
+        util.print_msg(
-        link_path.symlink_to(model_path)
+            "Creating a symlink in spacy/data failed. Make sure you have the "
+            "required permissions and try re-running the command as admin, or "
+            "use a virtualenv to install spaCy in a user directory, instead of "
+            "doing a system installation.",
+            "You can still import the model as a Python package and call its "
+            "load() method, or create the symlink manually:",
+            "{a} --> {b}".format(a=unicode_(model_path), b=unicode_(link_path)),
+            title="Error: Couldn't link model to '{l}'".format(l=link_name))
+        raise
 
     util.print_msg(
         "{a} --> {b}".format(a=model_path.as_posix(), b=link_path.as_posix()),

spacy/cli/model.py

@@ -95,7 +95,7 @@ def read_clusters(clusters_path):
     return clusters
 
 
-def populate_vocab(vocab, clusters, probs, oov_probs):
+def populate_vocab(vocab, clusters, probs, oov_prob):
     # Ensure probs has entries for all words seen during clustering.
     for word in clusters:
         if word not in probs:

spacy/cli/package.py

@@ -1,57 +1,67 @@
 # coding: utf8
 from __future__ import unicode_literals
 
-import json
 import shutil
 import requests
 from pathlib import Path
 
-from .. import about
+from ..compat import unicode_, json_dumps
 from .. import util
 
 
-def package(input_dir, output_dir, force):
+def package(input_dir, output_dir, meta_path, force):
     input_path = Path(input_dir)
     output_path = Path(output_dir)
-    check_dirs(input_path, output_path)
+    meta_path = util.ensure_path(meta_path)
+    check_dirs(input_path, output_path, meta_path)
 
     template_setup = get_template('setup.py')
     template_manifest = get_template('MANIFEST.in')
     template_init = get_template('en_model_name/__init__.py')
+
+    meta_path = meta_path or input_path / 'meta.json'
+    if meta_path.is_file():
+        util.print_msg(unicode_(meta_path), title="Reading meta.json from file")
+        meta = util.read_json(meta_path)
+    else:
         meta = generate_meta()
 
+    validate_meta(meta, ['lang', 'name', 'version'])
     model_name = meta['lang'] + '_' + meta['name']
     model_name_v = model_name + '-' + meta['version']
     main_path = output_path / model_name_v
     package_path = main_path / model_name
 
     create_dirs(package_path, force)
-    shutil.copytree(input_path.as_posix(), (package_path / model_name_v).as_posix())
+    shutil.copytree(unicode_(input_path), unicode_(package_path / model_name_v))
-    create_file(main_path / 'meta.json', json.dumps(meta, indent=2))
+    create_file(main_path / 'meta.json', json_dumps(meta))
     create_file(main_path / 'setup.py', template_setup)
     create_file(main_path / 'MANIFEST.in', template_manifest)
     create_file(package_path / '__init__.py', template_init)
 
     util.print_msg(
-        main_path.as_posix(),
+        unicode_(main_path),
         "To build the package, run `python setup.py sdist` in that directory.",
         title="Successfully created package {p}".format(p=model_name_v))
 
 
-def check_dirs(input_path, output_path):
+def check_dirs(input_path, output_path, meta_path):
     if not input_path.exists():
-        util.sys_exit(input_path.as_poisx(), title="Model directory not found")
+        util.sys_exit(unicode_(input_path.as_poisx), title="Model directory not found")
     if not output_path.exists():
-        util.sys_exit(output_path.as_posix(), title="Output directory not found")
+        util.sys_exit(unicode_(output_path), title="Output directory not found")
+    if meta_path and not meta_path.exists():
+        util.sys_exit(unicode_(meta_path), title="meta.json not found")
 
 
 def create_dirs(package_path, force):
     if package_path.exists():
         if force:
-            shutil.rmtree(package_path.as_posix())
+            shutil.rmtree(unicode_(package_path))
         else:
-            util.sys_exit(package_path.as_posix(),
+            util.sys_exit(unicode_(package_path),
-                "Please delete the directory and try again.",
+                "Please delete the directory and try again, or use the --force "
+                "flag to overwrite existing directories.",
                 title="Package directory already exists")
     Path.mkdir(package_path, parents=True)
 
@@ -81,6 +91,14 @@ def generate_meta():
     return meta
 
 
+def validate_meta(meta, keys):
+    for key in keys:
+        if key not in meta or meta[key] == '':
+            util.sys_exit(
+                "This setting is required to build your package.",
+                title='No "{k}" setting found in meta.json'.format(k=key))
+
+
 def get_template(filepath):
     url = 'https://raw.githubusercontent.com/explosion/spacy-dev-resources/master/templates/model/'
     r = requests.get(url + filepath)

spacy/cli/train.py

@@ -2,11 +2,9 @@
 from __future__ import unicode_literals, division, print_function
 
 import json
-from pathlib import Path
 
+from ..util import ensure_path
 from ..scorer import Scorer
-from ..tagger import Tagger
-from ..syntax.parser import Parser
 from ..gold import GoldParse, merge_sents
 from ..gold import read_json_file as read_gold_json
 from .. import util
@@ -14,9 +12,9 @@ from .. import util
 
 def train(language, output_dir, train_data, dev_data, n_iter, tagger, parser, ner,
           parser_L1):
-    output_path = Path(output_dir)
+    output_path = ensure_path(output_dir)
-    train_path = Path(train_data)
+    train_path = ensure_path(train_data)
-    dev_path = Path(dev_data)
+    dev_path = ensure_path(dev_data)
     check_dirs(output_path, train_path, dev_path)
 
     lang = util.get_lang_class(language)
@@ -45,7 +43,7 @@ def train(language, output_dir, train_data, dev_data, n_iter, tagger, parser, ne
 
 
 def train_config(config):
-    config_path = Path(config)
+    config_path = ensure_path(config)
     if not config_path.is_file():
         util.sys_exit(config_path.as_posix(), title="Config file not found")
     config = json.load(config_path)
@@ -59,8 +57,8 @@ def train_model(Language, train_data, dev_data, output_path, tagger_cfg, parser_
                 entity_cfg, n_iter):
     print("Itn.\tN weight\tN feats\tUAS\tNER F.\tTag %\tToken %")
 
-    with Language.train(output_path, train_data, tagger_cfg, parser_cfg, entity_cfg) as trainer:
+    with Language.train(output_path, train_data,
-        loss = 0
+                        pos=tagger_cfg, deps=parser_cfg, ner=entity_cfg) as trainer:
         for itn, epoch in enumerate(trainer.epochs(n_iter, augment_data=None)):
             for doc, gold in epoch:
                 trainer.update(doc, gold)

spacy/compat.py

@@ -0,0 +1,54 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+import six
+import sys
+import ujson
+
+try:
+    import cPickle as pickle
+except ImportError:
+    import pickle
+
+try:
+    import copy_reg
+except ImportError:
+    import copyreg as copy_reg
+
+
+is_python2 = six.PY2
+is_python3 = six.PY3
+is_windows = sys.platform.startswith('win')
+is_linux = sys.platform.startswith('linux')
+is_osx = sys.platform == 'darwin'
+
+
+if is_python2:
+    bytes_ = str
+    unicode_ = unicode
+    basestring_ = basestring
+    input_ = raw_input
+    json_dumps = lambda data: ujson.dumps(data, indent=2).decode('utf8')
+
+elif is_python3:
+    bytes_ = bytes
+    unicode_ = str
+    basestring_ = str
+    input_ = input
+    json_dumps = lambda data: ujson.dumps(data, indent=2)
+
+
+def symlink_to(orig, dest):
+    if is_python2 and is_windows:
+        import subprocess
+        subprocess.call(['mklink', '/d', unicode(orig), unicode(dest)], shell=True)
+    else:
+        orig.symlink_to(dest)
+
+
+def is_config(python2=None, python3=None, windows=None, linux=None, osx=None):
+    return ((python2 == None or python2 == is_python2) and
+            (python3 == None or python3 == is_python3) and
+            (windows == None or windows == is_windows) and
+            (linux == None or linux == is_linux) and
+            (osx == None or osx == is_osx))

spacy/deprecated.py

@@ -1,16 +1,14 @@
+# coding: utf8
+from __future__ import unicode_literals
+
 from pathlib import Path
+
 from . import about
 from . import util
 from .cli import download
 from .cli import link
 
 
-try:
-    basestring
-except NameError:
-    basestring = str
-
-
 def read_lang_data(package):
     tokenization = package.load_json(('tokenizer', 'specials.json'))
     with package.open(('tokenizer', 'prefix.txt'), default=None) as file_:
@@ -36,7 +34,8 @@ def align_tokens(ref, indices): # Deprecated, surely?
 
 
 def detokenize(token_rules, words): # Deprecated?
-    """To align with treebanks, return a list of "chunks", where a chunk is a
+    """
+    To align with treebanks, return a list of "chunks", where a chunk is a
     sequence of tokens that are separated by whitespace in actual strings. Each
     chunk should be a tuple of token indices, e.g.
 
@@ -57,10 +56,30 @@ def detokenize(token_rules, words): # Deprecated?
     return positions
 
 
-def fix_glove_vectors_loading(overrides):
+def match_best_version(target_name, target_version, path):
-    """Special-case hack for loading the GloVe vectors, to support deprecated
+    path = util.ensure_path(path)
-    <1.0 stuff. Phase this out once the data is fixed."""
+    if path is None or not path.exists():
+        return None
+    matches = []
+    for data_name in path.iterdir():
+        name, version = split_data_name(data_name.parts[-1])
+        if name == target_name:
+            matches.append((tuple(float(v) for v in version.split('.')), data_name))
+    if matches:
+        return Path(max(matches)[1])
+    else:
+        return None
 
+
+def split_data_name(name):
+    return name.split('-', 1) if '-' in name else (name, '')
+
+
+def fix_glove_vectors_loading(overrides):
+    """
+    Special-case hack for loading the GloVe vectors, to support deprecated
+    <1.0 stuff. Phase this out once the data is fixed.
+    """
     if 'data_dir' in overrides and 'path' not in overrides:
         raise ValueError("The argument 'data_dir' has been renamed to 'path'")
     if overrides.get('path') is False:
@@ -68,18 +87,16 @@ def fix_glove_vectors_loading(overrides):
     if overrides.get('path') in (None, True):
         data_path = util.get_data_path()
     else:
-        path = overrides['path']
+        path = util.ensure_path(overrides['path'])
-        if isinstance(path, basestring):
-            path = Path(path)
         data_path = path.parent
     vec_path = None
     if 'add_vectors' not in overrides:
         if 'vectors' in overrides:
-            vec_path = util.match_best_version(overrides['vectors'], None, data_path)
+            vec_path = match_best_version(overrides['vectors'], None, data_path)
             if vec_path is None:
                 return overrides
         else:
-            vec_path = util.match_best_version('en_glove_cc_300_1m_vectors', None, data_path)
+            vec_path = match_best_version('en_glove_cc_300_1m_vectors', None, data_path)
         if vec_path is not None:
             vec_path = vec_path / 'vocab' / 'vec.bin'
     if vec_path is not None:
@@ -88,13 +105,13 @@ def fix_glove_vectors_loading(overrides):
 
 
 def resolve_model_name(name):
-    """If spaCy is loaded with 'de', check if symlink already exists. If
+    """
-    not, user have upgraded from older version and have old models installed.
+    If spaCy is loaded with 'de', check if symlink already exists. If
+    not, user may have upgraded from older version and have old models installed.
     Check if old model directory exists and if so, return that instead and create
     shortcut link. If English model is found and no shortcut exists, raise error
     and tell user to install new model.
     """
-
     if name == 'en' or name == 'de':
         versions = ['1.0.0', '1.1.0']
         data_path = Path(util.get_data_path())
@@ -117,9 +134,11 @@ def resolve_model_name(name):
 
 
 class ModelDownload():
-    """Replace download modules within en and de with deprecation warning and
+    """
+    Replace download modules within en and de with deprecation warning and
     download default language model (using shortcut). Use classmethods to allow
-    importing ModelDownload as download and calling download.en() etc."""
+    importing ModelDownload as download and calling download.en() etc.
+    """
 
     @classmethod
     def load(self, lang):

spacy/en/__init__.py

@@ -11,12 +11,6 @@ from ..deprecated import fix_glove_vectors_loading
 from .language_data import *
 
 
-try:
-    basestring
-except NameError:
-    basestring = str
-
-
 class English(Language):
     lang = 'en'
 

spacy/gold.pyx

@@ -1,15 +1,13 @@
 # cython: profile=True
+# coding: utf8
 from __future__ import unicode_literals, print_function
 
 import io
-import json
 import re
-import os
+import ujson
-from os import path
-
-import ujson as json
 
 from .syntax import nonproj
+from .util import ensure_path
 
 
 def tags_to_entities(tags):
@@ -141,12 +139,13 @@ def _min_edit_path(cand_words, gold_words):
 
 
 def read_json_file(loc, docs_filter=None):
-    if path.isdir(loc):
+    loc = ensure_path(loc)
-        for filename in os.listdir(loc):
+    if loc.is_dir():
-            yield from read_json_file(path.join(loc, filename))
+        for filename in loc.iterdir():
+            yield from read_json_file(loc / filename)
     else:
-        with io.open(loc, 'r', encoding='utf8') as file_:
+        with loc.open('r', encoding='utf8') as file_:
-            docs = json.load(file_)
+            docs = ujson.load(file_)
         for doc in docs:
             if docs_filter is not None and not docs_filter(doc):
                 continue
@@ -220,7 +219,8 @@ cdef class GoldParse:
 
     def __init__(self, doc, annot_tuples=None, words=None, tags=None, heads=None,
                  deps=None, entities=None, make_projective=False):
-        """Create a GoldParse.
+        """
+        Create a GoldParse.
 
         Arguments:
             doc (Doc):
@@ -302,7 +302,8 @@ cdef class GoldParse:
             self.heads = proj_heads
 
     def __len__(self):
-        """Get the number of gold-standard tokens.
+        """
+        Get the number of gold-standard tokens.
 
         Returns (int): The number of gold-standard tokens.
         """
@@ -310,13 +311,16 @@ cdef class GoldParse:
 
     @property
     def is_projective(self):
-        """Whether the provided syntactic annotations form a projective dependency
+        """
-        tree."""
+        Whether the provided syntactic annotations form a projective dependency
+        tree.
+        """
         return not nonproj.is_nonproj_tree(self.heads)
 
 
 def biluo_tags_from_offsets(doc, entities):
-    '''Encode labelled spans into per-token tags, using the Begin/In/Last/Unit/Out
+    """
+    Encode labelled spans into per-token tags, using the Begin/In/Last/Unit/Out
     scheme (biluo).
 
     Arguments:
@@ -347,7 +351,7 @@ def biluo_tags_from_offsets(doc, entities):
         tags = biluo_tags_from_offsets(doc, entities)
 
         assert tags == ['O', 'O', 'U-LOC', 'O']
-    '''
+    """
     starts = {token.idx: token.i for token in doc}
     ends = {token.idx+len(token): token.i for token in doc}
     biluo = ['-' for _ in doc]

spacy/language.py

@@ -1,39 +1,25 @@
-from __future__ import absolute_import
+# coding: utf8
-from __future__ import unicode_literals
+from __future__ import absolute_import, unicode_literals
-import pathlib
 from contextlib import contextmanager
 import shutil
 
-import ujson
-
-
-try:
-    basestring
-except NameError:
-    basestring = str
-
-try:
-    unicode
-except NameError:
-    unicode = str
-
 from .tokenizer import Tokenizer
 from .vocab import Vocab
 from .tagger import Tagger
 from .matcher import Matcher
-from . import attrs
-from . import orth
-from . import util
-from . import language_data
 from .lemmatizer import Lemmatizer
 from .train import Trainer
-
-from .attrs import TAG, DEP, ENT_IOB, ENT_TYPE, HEAD, PROB, LANG, IS_STOP
 from .syntax.parser import get_templates
 from .syntax.nonproj import PseudoProjectivity
 from .pipeline import DependencyParser, EntityRecognizer
 from .syntax.arc_eager import ArcEager
 from .syntax.ner import BiluoPushDown
+from .compat import json_dumps
+from .attrs import IS_STOP
+from . import attrs
+from . import orth
+from . import util
+from . import language_data
 
 
 class BaseDefaults(object):
@@ -150,25 +136,15 @@ class BaseDefaults(object):
         return pipeline
 
     token_match = language_data.TOKEN_MATCH
-
     prefixes = tuple(language_data.TOKENIZER_PREFIXES)
-
     suffixes = tuple(language_data.TOKENIZER_SUFFIXES)
-
     infixes = tuple(language_data.TOKENIZER_INFIXES)
-
     tag_map = dict(language_data.TAG_MAP)
-
     tokenizer_exceptions = {}
-
     parser_features = get_templates('parser')
-
     entity_features = get_templates('ner')
-
     tagger_features = Tagger.feature_templates # TODO -- fix this
-
     stop_words = set()
-
     lemma_rules = {}
     lemma_exc = {}
     lemma_index = {}
@@ -202,53 +178,46 @@ class BaseDefaults(object):
 
 
 class Language(object):
-    '''A text-processing pipeline. Usually you'll load this once per process, and
+    """
+    A text-processing pipeline. Usually you'll load this once per process, and
     pass the instance around your program.
-    '''
+    """
     Defaults = BaseDefaults
     lang = None
 
     @classmethod
-    @contextmanager
+    def setup_directory(cls, path, **configs):
-    def train(cls, path, gold_tuples, *configs):
+        """
-        if isinstance(path, basestring):
+        Initialise a model directory.
-            path = pathlib.Path(path)
+        """
-        tagger_cfg, parser_cfg, entity_cfg = configs
+        for name, config in configs.items():
-        dep_model_dir = path / 'deps'
+            directory = path / name
-        ner_model_dir = path / 'ner'
+            if directory.exists():
-        pos_model_dir = path / 'pos'
+                shutil.rmtree(str(directory))
-        if dep_model_dir.exists():
+            directory.mkdir()
-            shutil.rmtree(str(dep_model_dir))
+            with (directory / 'config.json').open('wb') as file_:
-        if ner_model_dir.exists():
+                data = json_dumps(config)
-            shutil.rmtree(str(ner_model_dir))
+                file_.write(data)
-        if pos_model_dir.exists():
+        if not (path / 'vocab').exists():
-            shutil.rmtree(str(pos_model_dir))
+            (path / 'vocab').mkdir()
-        dep_model_dir.mkdir()
-        ner_model_dir.mkdir()
-        pos_model_dir.mkdir()
 
-        if parser_cfg['pseudoprojective']:
+    @classmethod
+    @contextmanager
+    def train(cls, path, gold_tuples, **configs):
+        parser_cfg = configs.get('deps', {})
+        if parser_cfg.get('pseudoprojective'):
             # preprocess training data here before ArcEager.get_labels() is called
             gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples)
 
-        parser_cfg['actions'] = ArcEager.get_actions(gold_parses=gold_tuples)
+        for subdir in ('deps', 'ner', 'pos'):
-        entity_cfg['actions'] = BiluoPushDown.get_actions(gold_parses=gold_tuples)
+            if subdir not in configs:
+                configs[subdir] = {}
+        if parser_cfg:
+            configs['deps']['actions'] = ArcEager.get_actions(gold_parses=gold_tuples)
+        if 'ner' in configs:
+            configs['ner']['actions'] = BiluoPushDown.get_actions(gold_parses=gold_tuples)
 
-        with (dep_model_dir / 'config.json').open('wb') as file_:
+        cls.setup_directory(path, **configs)
-            data = ujson.dumps(parser_cfg)
-            if isinstance(data, unicode):
-                data = data.encode('utf8')
-            file_.write(data)
-        with (ner_model_dir / 'config.json').open('wb') as file_:
-            data = ujson.dumps(entity_cfg)
-            if isinstance(data, unicode):
-                data = data.encode('utf8')
-            file_.write(data)
-        with (pos_model_dir / 'config.json').open('wb') as file_:
-            data = ujson.dumps(tagger_cfg)
-            if isinstance(data, unicode):
-                data = data.encode('utf8')
-            file_.write(data)
 
         self = cls(
                 path=path,
@@ -269,14 +238,22 @@ class Language(object):
         self.entity = self.Defaults.create_entity(self)
         self.pipeline = self.Defaults.create_pipeline(self)
         yield Trainer(self, gold_tuples)
-        self.end_training(path=path)
+        self.end_training()
+        self.save_to_directory(path)
 
     def __init__(self, **overrides):
+        """
+        Create or load the pipeline.
+
+        Arguments:
+            **overrides: Keyword arguments indicating which defaults to override.
+
+        Returns:
+            Language: The newly constructed object.
+        """
         if 'data_dir' in overrides and 'path' not in overrides:
             raise ValueError("The argument 'data_dir' has been renamed to 'path'")
-        path = overrides.get('path', True)
+        path = util.ensure_path(overrides.get('path', True))
-        if isinstance(path, basestring):
-            path = pathlib.Path(path)
         if path is True:
             path = util.get_data_path() / self.lang
             if not path.exists() and 'path' not in overrides:
@@ -322,11 +299,12 @@ class Language(object):
             self.pipeline = [self.tagger, self.parser, self.matcher, self.entity]
 
     def __call__(self, text, tag=True, parse=True, entity=True):
-        """Apply the pipeline to some text.  The text can span multiple sentences,
+        """
+        Apply the pipeline to some text.  The text can span multiple sentences,
         and can contain arbtrary whitespace.  Alignment into the original string
         is preserved.
 
-        Args:
+        Argsuments:
             text (unicode): The text to be processed.
 
         Returns:
@@ -352,7 +330,8 @@ class Language(object):
         return doc
 
     def pipe(self, texts, tag=True, parse=True, entity=True, n_threads=2, batch_size=1000):
-        '''Process texts as a stream, and yield Doc objects in order.
+        """
+        Process texts as a stream, and yield Doc objects in order.
 
         Supports GIL-free multi-threading.
 
@@ -361,7 +340,7 @@ class Language(object):
             tag (bool)
             parse (bool)
             entity (bool)
-        '''
+        """
         skip = {self.tagger: not tag, self.parser: not parse, self.entity: not entity}
         stream = (self.make_doc(text) for text in texts)
         for proc in self.pipeline:
@@ -373,51 +352,42 @@ class Language(object):
         for doc in stream:
             yield doc
 
-    def end_training(self, path=None):
+    def save_to_directory(self, path):
-        if path is None:
+        """
-            path = self.path
+        Save the Vocab, StringStore and pipeline to a directory.
-        elif isinstance(path, basestring):
-            path = pathlib.Path(path)
 
-        if self.tagger:
+        Arguments:
-            self.tagger.model.end_training()
+            path (string or pathlib path): Path to save the model.
-            self.tagger.model.dump(str(path / 'pos' / 'model'))
+        """
-        if self.parser:
+        configs = {
-            self.parser.model.end_training()
+            'pos': self.tagger.cfg if self.tagger else {},
-            self.parser.model.dump(str(path / 'deps' / 'model'))
+            'deps': self.parser.cfg if self.parser else {},
-        if self.entity:
+            'ner': self.entity.cfg if self.entity else {},
-            self.entity.model.end_training()
+        }
-            self.entity.model.dump(str(path / 'ner' / 'model'))
+
+        path = util.ensure_path(path)
+        self.setup_directory(path, **configs)
 
         strings_loc = path / 'vocab' / 'strings.json'
         with strings_loc.open('w', encoding='utf8') as file_:
             self.vocab.strings.dump(file_)
         self.vocab.dump(path / 'vocab' / 'lexemes.bin')
-
+        # TODO: Word vectors?
         if self.tagger:
-            tagger_freqs = list(self.tagger.freqs[TAG].items())
+            self.tagger.model.dump(str(path / 'pos' / 'model'))
-        else:
-            tagger_freqs = []
         if self.parser:
-            dep_freqs = list(self.parser.moves.freqs[DEP].items())
+            self.parser.model.dump(str(path / 'deps' / 'model'))
-            head_freqs = list(self.parser.moves.freqs[HEAD].items())
-        else:
-            dep_freqs = []
-            head_freqs = []
         if self.entity:
-            entity_iob_freqs = list(self.entity.moves.freqs[ENT_IOB].items())
+            self.entity.model.dump(str(path / 'ner' / 'model'))
-            entity_type_freqs = list(self.entity.moves.freqs[ENT_TYPE].items())
+
-        else:
+    def end_training(self, path=None):
-            entity_iob_freqs = []
+        if self.tagger:
-            entity_type_freqs = []
+            self.tagger.model.end_training()
-        with (path / 'vocab' / 'serializer.json').open('wb') as file_:
+        if self.parser:
-            data = ujson.dumps([
+            self.parser.model.end_training()
-                        (TAG, tagger_freqs),
+        if self.entity:
-                        (DEP, dep_freqs),
+            self.entity.model.end_training()
-                        (ENT_IOB, entity_iob_freqs),
+        # NB: This is slightly different from before --- we no longer default
-                        (ENT_TYPE, entity_type_freqs),
+        # to taking nlp.path
-                        (HEAD, head_freqs)
+        if path is not None:
-                    ])
+            self.save_to_directory(path)
-            if isinstance(data, unicode):
-                data = data.encode('utf8')
-            file_.write(data)

spacy/lemmatizer.py

@@ -1,13 +1,8 @@
-from __future__ import unicode_literals, print_function
+# coding: utf8
-import codecs
+from __future__ import unicode_literals
-import pathlib
-
-import ujson as json
 
 from .symbols import POS, NOUN, VERB, ADJ, PUNCT
-from .symbols import VerbForm_inf, VerbForm_none
+from .symbols import VerbForm_inf, VerbForm_none, Number_sing, Degree_pos
-from .symbols import Number_sing
-from .symbols import Degree_pos
 
 
 class Lemmatizer(object):
@@ -38,8 +33,10 @@ class Lemmatizer(object):
         return lemmas
 
     def is_base_form(self, univ_pos, morphology=None):
-        '''Check whether we're dealing with an uninflected paradigm, so we can
+        """
-        avoid lemmatization entirely.'''
+        Check whether we're dealing with an uninflected paradigm, so we can
+        avoid lemmatization entirely.
+        """
         morphology = {} if morphology is None else morphology
         others = [key for key in morphology if key not in (POS, 'number', 'pos', 'verbform')]
         true_morph_key = morphology.get('morph', 0)

spacy/lexeme.pyx

@@ -1,4 +1,7 @@
 # cython: embedsignature=True
+# coding: utf8
+from __future__ import unicode_literals, print_function
+
 from libc.math cimport sqrt
 from cpython.ref cimport Py_INCREF
 from cymem.cymem cimport Pool
@@ -9,14 +12,11 @@ from cython.view cimport array as cvarray
 cimport numpy as np
 np.import_array()
 
-
-
 from libc.string cimport memset
+import numpy
 
 from .orth cimport word_shape
 from .typedefs cimport attr_t, flags_t
-import numpy
-
 from .attrs cimport IS_ALPHA, IS_ASCII, IS_DIGIT, IS_LOWER, IS_PUNCT, IS_SPACE
 from .attrs cimport IS_TITLE, IS_UPPER, LIKE_URL, LIKE_NUM, LIKE_EMAIL, IS_STOP
 from .attrs cimport IS_BRACKET
@@ -30,13 +30,15 @@ memset(&EMPTY_LEXEME, 0, sizeof(LexemeC))
 
 
 cdef class Lexeme:
-    """An entry in the vocabulary.  A Lexeme has no string context --- it's a
+    """
+    An entry in the vocabulary.  A Lexeme has no string context --- it's a
     word-type, as opposed to a word token.  It therefore has no part-of-speech
     tag, dependency parse, or lemma (lemmatization depends on the part-of-speech
     tag).
     """
     def __init__(self, Vocab vocab, int orth):
-        """Create a Lexeme object.
+        """
+        Create a Lexeme object.
 
         Arguments:
             vocab (Vocab): The parent vocabulary
@@ -80,7 +82,8 @@ cdef class Lexeme:
         return self.c.orth
 
     def set_flag(self, attr_id_t flag_id, bint value):
-        """Change the value of a boolean flag.
+        """
+        Change the value of a boolean flag.
 
         Arguments:
             flag_id (int): The attribute ID of the flag to set.
@@ -89,7 +92,8 @@ cdef class Lexeme:
         Lexeme.c_set_flag(self.c, flag_id, value)
 
     def check_flag(self, attr_id_t flag_id):
-        """Check the value of a boolean flag.
+        """
+        Check the value of a boolean flag.
 
         Arguments:
             flag_id (int): The attribute ID of the flag to query.
@@ -98,7 +102,8 @@ cdef class Lexeme:
         return True if Lexeme.c_check_flag(self.c, flag_id) else False
 
     def similarity(self, other):
-        '''Compute a semantic similarity estimate. Defaults to cosine over vectors.
+        """
+        Compute a semantic similarity estimate. Defaults to cosine over vectors.
 
         Arguments:
             other:
@@ -106,7 +111,7 @@ cdef class Lexeme:
                 Token and Lexeme objects.
         Returns:
             score (float): A scalar similarity score. Higher is more similar.
-        '''
+        """
         if self.vector_norm == 0 or other.vector_norm == 0:
             return 0.0
         return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)

spacy/matcher.pyx

@@ -1,7 +1,10 @@
 # cython: profile=True
 # cython: infer_types=True
+# coding: utf8
 from __future__ import unicode_literals
 
+import ujson
+
 from .typedefs cimport attr_t
 from .typedefs cimport hash_t
 from .attrs cimport attr_id_t
@@ -52,12 +55,6 @@ from .attrs import FLAG36 as L9_ENT
 from .attrs import FLAG35 as L10_ENT
 
 
-try:
-    import ujson as json
-except ImportError:
-    import json
-
-
 cpdef enum quantifier_t:
     _META
     ONE
@@ -180,7 +177,8 @@ cdef class Matcher:
 
     @classmethod
     def load(cls, path, vocab):
-        '''Load the matcher and patterns from a file path.
+        """
+        Load the matcher and patterns from a file path.
 
         Arguments:
             path (Path):
@@ -189,16 +187,17 @@ cdef class Matcher:
                 The vocabulary that the documents to match over will refer to.
         Returns:
             Matcher: The newly constructed object.
-        '''
+        """
         if (path / 'gazetteer.json').exists():
             with (path / 'gazetteer.json').open('r', encoding='utf8') as file_:
-                patterns = json.load(file_)
+                patterns = ujson.load(file_)
         else:
             patterns = {}
         return cls(vocab, patterns)
 
     def __init__(self, vocab, patterns={}):
-        """Create the Matcher.
+        """
+        Create the Matcher.
 
         Arguments:
             vocab (Vocab):
@@ -227,7 +226,8 @@ cdef class Matcher:
 
     def add_entity(self, entity_key, attrs=None, if_exists='raise',
                    acceptor=None, on_match=None):
-        """Add an entity to the matcher.
+        """
+        Add an entity to the matcher.
 
         Arguments:
             entity_key (unicode or int):
@@ -264,7 +264,8 @@ cdef class Matcher:
         self._callbacks[entity_key] = on_match
 
     def add_pattern(self, entity_key, token_specs, label=""):
-        """Add a pattern to the matcher.
+        """
+        Add a pattern to the matcher.
 
         Arguments:
             entity_key (unicode or int):
@@ -307,7 +308,8 @@ cdef class Matcher:
             return entity_key
 
     def has_entity(self, entity_key):
-        """Check whether the matcher has an entity.
+        """
+        Check whether the matcher has an entity.
 
         Arguments:
             entity_key (string or int): The entity key to check.
@@ -318,7 +320,8 @@ cdef class Matcher:
         return entity_key in self._entities
 
     def get_entity(self, entity_key):
-        """Retrieve the attributes stored for an entity.
+        """
+        Retrieve the attributes stored for an entity.
 
         Arguments:
             entity_key (unicode or int): The entity to retrieve.
@@ -332,7 +335,8 @@ cdef class Matcher:
             return None
 
     def __call__(self, Doc doc, acceptor=None):
-        """Find all token sequences matching the supplied patterns on the Doc.
+        """
+        Find all token sequences matching the supplied patterns on the Doc.
 
         Arguments:
             doc (Doc):
@@ -445,7 +449,8 @@ cdef class Matcher:
         return matches
 
     def pipe(self, docs, batch_size=1000, n_threads=2):
-        """Match a stream of documents, yielding them in turn.
+        """
+        Match a stream of documents, yielding them in turn.
 
         Arguments:
             docs: A stream of documents.

spacy/morphology.pyx

@@ -1,13 +1,9 @@
 # cython: infer_types
+# coding: utf8
 from __future__ import unicode_literals
 
 from libc.string cimport memset
 
-try:
-    import ujson as json
-except ImportError:
-    import json
-
 from .parts_of_speech cimport ADJ, VERB, NOUN, PUNCT
 from .attrs cimport POS, IS_SPACE
 from .parts_of_speech import IDS as POS_IDS
@@ -16,7 +12,9 @@ from .attrs import LEMMA, intify_attrs
 
 
 def _normalize_props(props):
-    '''Transform deprecated string keys to correct names.'''
+    """
+    Transform deprecated string keys to correct names.
+    """
     out = {}
     for key, value in props.items():
         if key == POS:
@@ -98,13 +96,14 @@ cdef class Morphology:
             flags[0] &= ~(one << flag_id)
 
     def add_special_case(self, unicode tag_str, unicode orth_str, attrs, force=False):
-        '''Add a special-case rule to the morphological analyser. Tokens whose
+        """
+        Add a special-case rule to the morphological analyser. Tokens whose
         tag and orth match the rule will receive the specified properties.
 
         Arguments:
             tag (unicode): The part-of-speech tag to key the exception.
             orth (unicode): The word-form to key the exception.
-        '''
+        """
         tag = self.strings[tag_str]
         tag_id = self.reverse_index[tag]
         orth = self.strings[orth_str]

spacy/multi_words.py

@@ -1,8 +0,0 @@
-class RegexMerger(object):
-    def __init__(self, regexes):
-        self.regexes = regexes
-
-    def __call__(self, tokens):
-        for tag, entity_type, regex in self.regexes:
-            for m in regex.finditer(tokens.string):
-                tokens.merge(m.start(), m.end(), tag, m.group(), entity_type)

spacy/orth.pyx

@@ -1,6 +1,7 @@
-# coding: utf8
 # cython: infer_types=True
+# coding: utf8
 from __future__ import unicode_literals
+
 import unicodedata
 import re
 

spacy/parts_of_speech.pyx

@@ -1,3 +1,4 @@
+# coding: utf8
 from __future__ import unicode_literals
 
 

spacy/pipeline.pyx

@@ -1,3 +1,6 @@
+# coding: utf8
+from __future__ import unicode_literals
+
 from .syntax.parser cimport Parser
 from .syntax.beam_parser cimport BeamParser
 from .syntax.ner cimport BiluoPushDown
@@ -11,44 +14,40 @@ from .attrs import DEP, ENT_TYPE
 
 
 cdef class EntityRecognizer(Parser):
-    """Annotate named entities on Doc objects."""
+    """
+    Annotate named entities on Doc objects.
+    """
     TransitionSystem = BiluoPushDown
 
     feature_templates = get_feature_templates('ner')
 
     def add_label(self, label):
-        for action in self.moves.action_types:
+        Parser.add_label(self, label)
-            self.moves.add_action(action, label)
-            if 'actions' in self.cfg:
-                self.cfg['actions'].setdefault(action,
-                                        {}).setdefault(label, True)
         if isinstance(label, basestring):
             label = self.vocab.strings[label]
+        # Set label into serializer. Super hacky :(
         for attr, freqs in self.vocab.serializer_freqs:
             if attr == ENT_TYPE and label not in freqs:
                 freqs.append([label, 1])
-        # Super hacky :(
         self.vocab._serializer = None
 
 
 cdef class BeamEntityRecognizer(BeamParser):
-    """Annotate named entities on Doc objects."""
+    """
+    Annotate named entities on Doc objects.
+    """
     TransitionSystem = BiluoPushDown
 
     feature_templates = get_feature_templates('ner')
 
     def add_label(self, label):
-        for action in self.moves.action_types:
+        Parser.add_label(self, label)
-            self.moves.add_action(action, label)
-            if 'actions' in self.cfg:
-                self.cfg['actions'].setdefault(action,
-                                        {}).setdefault(label, True)
         if isinstance(label, basestring):
             label = self.vocab.strings[label]
+        # Set label into serializer. Super hacky :(
         for attr, freqs in self.vocab.serializer_freqs:
             if attr == ENT_TYPE and label not in freqs:
                 freqs.append([label, 1])
-        # Super hacky :(
         self.vocab._serializer = None
 
 
@@ -58,11 +57,7 @@ cdef class DependencyParser(Parser):
     feature_templates = get_feature_templates('basic')
 
     def add_label(self, label):
-        for action in self.moves.action_types:
+        Parser.add_label(self, label)
-            self.moves.add_action(action, label)
-            if 'actions' in self.cfg:
-                self.cfg['actions'].setdefault(action,
-                                        {}).setdefault(label, True)
         if isinstance(label, basestring):
             label = self.vocab.strings[label]
         for attr, freqs in self.vocab.serializer_freqs:
@@ -78,11 +73,7 @@ cdef class BeamDependencyParser(BeamParser):
     feature_templates = get_feature_templates('basic')
 
     def add_label(self, label):
-        for action in self.moves.action_types:
+        Parser.add_label(self, label)
-            self.moves.add_action(action, label)
-            if 'actions' in self.cfg:
-                self.cfg['actions'].setdefault(action,
-                                        {}).setdefault(label, True)
         if isinstance(label, basestring):
             label = self.vocab.strings[label]
         for attr, freqs in self.vocab.serializer_freqs:

spacy/scorer.py

@@ -1,12 +1,13 @@
-from __future__ import division
+# coding: utf8
-from __future__ import print_function
+from __future__ import division, print_function, unicode_literals
-from __future__ import unicode_literals
 
 from .gold import tags_to_entities
 
 
 class PRFScore(object):
-    """A precision / recall / F score"""
+    """
+    A precision / recall / F score
+    """
     def __init__(self):
         self.tp = 0
         self.fp = 0

spacy/strings.pyx

@@ -1,12 +1,11 @@
 # cython: infer_types=True
+# coding: utf8
 from __future__ import unicode_literals, absolute_import
 
 cimport cython
 from libc.string cimport memcpy
 from libc.stdint cimport uint64_t, uint32_t
-
 from murmurhash.mrmr cimport hash64, hash32
-
 from preshed.maps cimport map_iter, key_t
 
 from .typedefs cimport hash_t
@@ -73,13 +72,16 @@ cdef Utf8Str _allocate(Pool mem, const unsigned char* chars, uint32_t length) ex
 
 
 cdef class StringStore:
-    '''Map strings to and from integer IDs.'''
+    """
+    Map strings to and from integer IDs.
+    """
     def __init__(self, strings=None, freeze=False):
-        '''Create the StringStore.
+        """
+        Create the StringStore.
 
         Arguments:
             strings: A sequence of unicode strings to add to the store.
-        '''
+        """
         self.mem = Pool()
         self._map = PreshMap()
         self._oov = PreshMap()
@@ -104,7 +106,8 @@ cdef class StringStore:
         return (StringStore, (list(self),))
 
     def __len__(self):
-        """The number of strings in the store.
+        """
+        The number of strings in the store.
 
         Returns:
             int The number of strings in the store.
@@ -112,7 +115,8 @@ cdef class StringStore:
         return self.size-1
 
     def __getitem__(self, object string_or_id):
-        """Retrieve a string from a given integer ID, or vice versa.
+        """
+        Retrieve a string from a given integer ID, or vice versa.
 
         Arguments:
             string_or_id (bytes or unicode or int):
@@ -159,7 +163,8 @@ cdef class StringStore:
                 return utf8str - self.c
 
     def __contains__(self, unicode string not None):
-        """Check whether a string is in the store.
+        """
+        Check whether a string is in the store.
 
         Arguments:
             string (unicode): The string to check.
@@ -172,7 +177,8 @@ cdef class StringStore:
         return self._map.get(key) is not NULL
 
     def __iter__(self):
-        """Iterate over the strings in the store, in order.
+        """
+        Iterate over the strings in the store, in order.
 
         Yields: unicode A string in the store.
         """
@@ -230,7 +236,8 @@ cdef class StringStore:
         return &self.c[self.size-1]
 
     def dump(self, file_):
-        """Save the strings to a JSON file.
+        """
+        Save the strings to a JSON file.
 
         Arguments:
             file_ (buffer): The file to save the strings.
@@ -244,7 +251,8 @@ cdef class StringStore:
         file_.write(string_data)
 
     def load(self, file_):
-        """Load the strings from a JSON file.
+        """
+        Load the strings from a JSON file.
 
         Arguments:
             file_ (buffer): The file from which to load the strings.

spacy/symbols.pyx

@@ -1,3 +1,4 @@
+# coding: utf8
 from __future__ import unicode_literals
 
 IDS = {

spacy/syntax/_parse_features.pyx

@@ -7,17 +7,17 @@ out of "context") is in features/extractor.pyx
 The atomic feature names are listed in a big enum, so that the feature tuples
 can refer to them.
 """
-from libc.string cimport memset
+# coding: utf-8
+from __future__ import unicode_literals
 
+from libc.string cimport memset
 from itertools import combinations
+from cymem.cymem cimport Pool
 
 from ..structs cimport TokenC
-
 from .stateclass cimport StateClass
 from ._state cimport StateC
 
-from cymem.cymem cimport Pool
-
 
 cdef inline void fill_token(atom_t* context, const TokenC* token) nogil:
     if token is NULL:

spacy/syntax/arc_eager.pyx

@@ -1,29 +1,26 @@
 # cython: profile=True
 # cython: cdivision=True
 # cython: infer_types=True
+# coding: utf-8
 from __future__ import unicode_literals
+
 from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
-
 import ctypes
-import os
+from libc.stdint cimport uint32_t
-
+from libc.string cimport memcpy
-from ..structs cimport TokenC
+from cymem.cymem cimport Pool
 
+from .stateclass cimport StateClass
+from ._state cimport StateC, is_space_token
+from .nonproj import PseudoProjectivity
+from .nonproj import is_nonproj_tree
 from .transition_system cimport do_func_t, get_cost_func_t
 from .transition_system cimport move_cost_func_t, label_cost_func_t
 from ..gold cimport GoldParse
 from ..gold cimport GoldParseC
 from ..attrs cimport TAG, HEAD, DEP, ENT_IOB, ENT_TYPE, IS_SPACE
 from ..lexeme cimport Lexeme
-
+from ..structs cimport TokenC
-from libc.stdint cimport uint32_t
-from libc.string cimport memcpy
-
-from cymem.cymem cimport Pool
-from .stateclass cimport StateClass
-from ._state cimport StateC, is_space_token
-from .nonproj import PseudoProjectivity
-from .nonproj import is_nonproj_tree
 
 
 DEF NON_MONOTONIC = True
@@ -317,17 +314,20 @@ cdef class ArcEager(TransitionSystem):
     def get_actions(cls, **kwargs):
         actions = kwargs.get('actions',
                     {
-                        SHIFT: {'': True},
+                        SHIFT: [''],
-                        REDUCE: {'': True},
+                        REDUCE: [''],
-                        RIGHT: {},
+                        RIGHT: [],
-                        LEFT: {},
+                        LEFT: [],
-                        BREAK: {'ROOT': True}})
+                        BREAK: ['ROOT']})
+        seen_actions = set()
         for label in kwargs.get('left_labels', []):
             if label.upper() != 'ROOT':
-                actions[LEFT][label] = True
+                if (LEFT, label) not in seen_actions:
+                    actions[LEFT].append(label)
         for label in kwargs.get('right_labels', []):
             if label.upper() != 'ROOT':
-                actions[RIGHT][label] = True
+                if (RIGHT, label) not in seen_actions:
+                    actions[RIGHT].append(label)
 
         for raw_text, sents in kwargs.get('gold_parses', []):
             for (ids, words, tags, heads, labels, iob), ctnts in sents:
@@ -336,9 +336,11 @@ cdef class ArcEager(TransitionSystem):
                         label = 'ROOT'
                     if label != 'ROOT':
                         if head < child:
-                            actions[RIGHT][label] = True
+                            if (RIGHT, label) not in seen_actions:
+                                actions[RIGHT].append(label)
                         elif head > child:
-                            actions[LEFT][label] = True
+                            if (LEFT, label) not in seen_actions:
+                                actions[LEFT].append(label)
         return actions
 
     property action_types:

spacy/syntax/beam_parser.pyx

@@ -1,50 +1,34 @@
+"""
+MALT-style dependency parser
+"""
 # cython: profile=True
 # cython: experimental_cpp_class_def=True
 # cython: cdivision=True
 # cython: infer_types=True
-"""
+# coding: utf-8
-MALT-style dependency parser
+
-"""
+from __future__ import unicode_literals, print_function
-from __future__ import unicode_literals
 cimport cython
 
 from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
-
 from libc.stdint cimport uint32_t, uint64_t
 from libc.string cimport memset, memcpy
 from libc.stdlib cimport rand
 from libc.math cimport log, exp, isnan, isinf
-import random
-import os.path
-from os import path
-import shutil
-import json
-import math
-
 from cymem.cymem cimport Pool, Address
 from murmurhash.mrmr cimport real_hash64 as hash64
 from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
-
-
-from util import Config
-
 from thinc.linear.features cimport ConjunctionExtracter
 from thinc.structs cimport FeatureC, ExampleC
-
+from thinc.extra.search cimport Beam, MaxViolation
-from thinc.extra.search cimport Beam
-from thinc.extra.search cimport MaxViolation
 from thinc.extra.eg cimport Example
 from thinc.extra.mb cimport Minibatch
 
 from ..structs cimport TokenC
-
 from ..tokens.doc cimport Doc
 from ..strings cimport StringStore
-
 from .transition_system cimport TransitionSystem, Transition
-
 from ..gold cimport GoldParse
-
 from . import _parse_features
 from ._parse_features cimport CONTEXT_SIZE
 from ._parse_features cimport fill_context
@@ -266,4 +250,3 @@ def is_gold(StateClass state, GoldParse gold, StringStore strings):
         id_, word, tag, head, dep, ner = gold.orig_annot[gold.cand_to_gold[i]]
         truth.add((id_, head, dep))
     return truth == predicted
-

spacy/syntax/iterators.pyx

@@ -1,9 +1,14 @@
-from spacy.parts_of_speech cimport NOUN, PROPN, PRON
+# coding: utf-8
+from __future__ import unicode_literals
+
+from ..parts_of_speech cimport NOUN, PROPN, PRON
 
 
 def english_noun_chunks(obj):
-    '''Detect base noun phrases from a dependency parse.
+    """
-    Works on both Doc and Span.'''
+    Detect base noun phrases from a dependency parse.
+    Works on both Doc and Span.
+    """
     labels = ['nsubj', 'dobj', 'nsubjpass', 'pcomp', 'pobj',
               'attr', 'ROOT', 'root']
     doc = obj.doc # Ensure works on both Doc and Span.

spacy/syntax/ner.pyx

@@ -1,17 +1,16 @@
+# coding: utf-8
 from __future__ import unicode_literals
 
-from .transition_system cimport Transition
-from .transition_system cimport do_func_t
-
-from ..structs cimport TokenC, Entity
-
 from thinc.typedefs cimport weight_t
-from ..gold cimport GoldParseC
-from ..gold cimport GoldParse
-from ..attrs cimport ENT_TYPE, ENT_IOB
 
 from .stateclass cimport StateClass
 from ._state cimport StateC
+from .transition_system cimport Transition
+from .transition_system cimport do_func_t
+from ..structs cimport TokenC, Entity
+from ..gold cimport GoldParseC
+from ..gold cimport GoldParse
+from ..attrs cimport ENT_TYPE, ENT_IOB
 
 
 cdef enum:
@@ -21,6 +20,7 @@ cdef enum:
     LAST
     UNIT
     OUT
+    ISNT
     N_MOVES
 
 
@@ -31,6 +31,7 @@ MOVE_NAMES[IN] = 'I'
 MOVE_NAMES[LAST] = 'L'
 MOVE_NAMES[UNIT] = 'U'
 MOVE_NAMES[OUT] = 'O'
+MOVE_NAMES[ISNT] = 'x'
 
 
 cdef do_func_t[N_MOVES] do_funcs
@@ -54,16 +55,20 @@ cdef class BiluoPushDown(TransitionSystem):
     def get_actions(cls, **kwargs):
         actions = kwargs.get('actions',
                     {
-                        MISSING: {'': True},
+                        MISSING: [''],
-                        BEGIN: {},
+                        BEGIN: [],
-                        IN: {},
+                        IN: [],
-                        LAST: {},
+                        LAST: [],
-                        UNIT: {},
+                        UNIT: [],
-                        OUT: {'': True}
+                        OUT: ['']
                     })
+        seen_entities = set()
         for entity_type in kwargs.get('entity_types', []):
+            if entity_type in seen_entities:
+                continue
+            seen_entities.add(entity_type)
             for action in (BEGIN, IN, LAST, UNIT):
-                actions[action][entity_type] = True
+                actions[action].append(entity_type)
         moves = ('M', 'B', 'I', 'L', 'U')
         for raw_text, sents in kwargs.get('gold_parses', []):
             for (ids, words, tags, heads, labels, biluo), _ in sents:
@@ -72,8 +77,10 @@ cdef class BiluoPushDown(TransitionSystem):
                         if ner_tag.count('-') != 1:
                             raise ValueError(ner_tag)
                         _, label = ner_tag.split('-')
+                        if label not in seen_entities:
+                            seen_entities.add(label)
                             for move_str in ('B', 'I', 'L', 'U'):
-                            actions[moves.index(move_str)][label] = True
+                                actions[moves.index(move_str)].append(label)
         return actions
 
     property action_types:
@@ -111,11 +118,17 @@ cdef class BiluoPushDown(TransitionSystem):
             label = 0
         elif '-' in name:
             move_str, label_str = name.split('-', 1)
+            # Hacky way to denote 'not this entity'
+            if label_str.startswith('!'):
+                label_str = label_str[1:]
+                move_str = 'x'
             label = self.strings[label_str]
         else:
             move_str = name
             label = 0
         move = MOVE_NAMES.index(move_str)
+        if move == ISNT:
+            return Transition(clas=0, move=ISNT, label=label, score=0)
         for i in range(self.n_moves):
             if self.c[i].move == move and self.c[i].label == label:
                 return self.c[i]
@@ -225,6 +238,9 @@ cdef class Begin:
         elif g_act == BEGIN:
             # B, Gold B --> Label match
             return label != g_tag
+        # Support partial supervision in the form of "not this label"
+        elif g_act == ISNT:
+            return label == g_tag
         else:
             # B, Gold I --> False (P)
             # B, Gold L --> False (P)
@@ -359,6 +375,9 @@ cdef class Unit:
         elif g_act == UNIT:
             # U, Gold U --> True iff tag match
             return label != g_tag
+        # Support partial supervision in the form of "not this label"
+        elif g_act == ISNT:
+            return label == g_tag
         else:
             # U, Gold B --> False
             # U, Gold I --> False
@@ -388,7 +407,7 @@ cdef class Out:
         cdef int g_act = gold.ner[s.B(0)].move
         cdef int g_tag = gold.ner[s.B(0)].label
 
-        if g_act == MISSING:
+        if g_act == MISSING or g_act == ISNT:
             return 0
         elif g_act == BEGIN:
             # O, Gold B --> False

spacy/syntax/nonproj.pyx

@@ -1,8 +1,9 @@
+# coding: utf-8
 from __future__ import unicode_literals
 from copy import copy
 
 from ..tokens.doc cimport Doc
-from spacy.attrs import DEP, HEAD
+from ..attrs import DEP, HEAD
 
 
 def ancestors(tokenid, heads):
@@ -201,5 +202,3 @@ class PseudoProjectivity:
                 filtered_sents.append(((ids,words,tags,heads,filtered_labels,iob), ctnts))
             filtered.append((raw_text, filtered_sents))
         return filtered
-
-

spacy/syntax/parser.pyx

@@ -1,56 +1,44 @@
-# cython: infer_types=True
 """
 MALT-style dependency parser
 """
+# coding: utf-8
+# cython: infer_types=True
 from __future__ import unicode_literals
+
+from collections import Counter
+import ujson
+
 cimport cython
 cimport cython.parallel
 
 from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
 from cpython.exc cimport PyErr_CheckSignals
-
 from libc.stdint cimport uint32_t, uint64_t
 from libc.string cimport memset, memcpy
 from libc.stdlib cimport malloc, calloc, free
-
-import os.path
-from collections import Counter
-from os import path
-import shutil
-import json
-import sys
-from .nonproj import PseudoProjectivity
-
-from cymem.cymem cimport Pool, Address
-from murmurhash.mrmr cimport hash64
 from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
 from thinc.linear.avgtron cimport AveragedPerceptron
 from thinc.linalg cimport VecVec
-from thinc.structs cimport SparseArrayC
+from thinc.structs cimport SparseArrayC, FeatureC, ExampleC
+from thinc.extra.eg cimport Example
+from cymem.cymem cimport Pool, Address
+from murmurhash.mrmr cimport hash64
 from preshed.maps cimport MapStruct
 from preshed.maps cimport map_get
 
-from thinc.structs cimport FeatureC
-from thinc.structs cimport ExampleC
-from thinc.extra.eg cimport Example
-
-from util import Config
-
-from ..structs cimport TokenC
-
-from ..tokens.doc cimport Doc
-from ..strings cimport StringStore
-
-from .transition_system import OracleError
-from .transition_system cimport TransitionSystem, Transition
-
-from ..gold cimport GoldParse
-
 from . import _parse_features
 from ._parse_features cimport CONTEXT_SIZE
 from ._parse_features cimport fill_context
 from .stateclass cimport StateClass
 from ._state cimport StateC
+from .nonproj import PseudoProjectivity
+from .transition_system import OracleError
+from .transition_system cimport TransitionSystem, Transition
+from ..structs cimport TokenC
+from ..tokens.doc cimport Doc
+from ..strings cimport StringStore
+from ..gold cimport GoldParse
+
 
 USE_FTRL = True
 DEBUG = False
@@ -80,7 +68,9 @@ cdef class ParserModel(AveragedPerceptron):
         return nr_feat
 
     def update(self, Example eg, itn=0):
-        '''Does regression on negative cost. Sort of cute?'''
+        """
+        Does regression on negative cost. Sort of cute?
+        """
         self.time += 1
         cdef int best = arg_max_if_gold(eg.c.scores, eg.c.costs, eg.c.nr_class)
         cdef int guess = eg.guess
@@ -132,10 +122,13 @@ cdef class ParserModel(AveragedPerceptron):
 
 
 cdef class Parser:
-    """Base class of the DependencyParser and EntityRecognizer."""
+    """
+    Base class of the DependencyParser and EntityRecognizer.
+    """
     @classmethod
     def load(cls, path, Vocab vocab, TransitionSystem=None, require=False, **cfg):
-        """Load the statistical model from the supplied path.
+        """
+        Load the statistical model from the supplied path.
 
         Arguments:
             path (Path):
@@ -148,10 +141,16 @@ cdef class Parser:
             The newly constructed object.
         """
         with (path / 'config.json').open() as file_:
-            cfg = json.load(file_)
+            cfg = ujson.load(file_)
         # TODO: remove this shim when we don't have to support older data
         if 'labels' in cfg and 'actions' not in cfg:
             cfg['actions'] = cfg.pop('labels')
+        # TODO: remove this shim when we don't have to support older data
+        for action_name, labels in dict(cfg['actions']).items():
+            # We need this to be sorted
+            if isinstance(labels, dict):
+                labels = list(sorted(labels.keys()))
+            cfg['actions'][action_name] = labels
         self = cls(vocab, TransitionSystem=TransitionSystem, model=None, **cfg)
         if (path / 'model').exists():
             self.model.load(str(path / 'model'))
@@ -161,7 +160,8 @@ cdef class Parser:
         return self
 
     def __init__(self, Vocab vocab, TransitionSystem=None, ParserModel model=None, **cfg):
-        """Create a Parser.
+        """
+        Create a Parser.
 
         Arguments:
             vocab (Vocab):
@@ -186,12 +186,18 @@ cdef class Parser:
         self.model.learn_rate = cfg.get('learn_rate', 0.001)
 
         self.cfg = cfg
+        # TODO: This is a pretty hacky fix to the problem of adding more
+        # labels. The issue is they come in out of order, if labels are
+        # added during training
+        for label in cfg.get('extra_labels', []):
+            self.add_label(label)
 
     def __reduce__(self):
         return (Parser, (self.vocab, self.moves, self.model), None, None)
 
     def __call__(self, Doc tokens):
-        """Apply the entity recognizer, setting the annotations onto the Doc object.
+        """
+        Apply the entity recognizer, setting the annotations onto the Doc object.
 
         Arguments:
             doc (Doc): The document to be processed.
@@ -208,7 +214,8 @@ cdef class Parser:
         self.moves.finalize_doc(tokens)
 
     def pipe(self, stream, int batch_size=1000, int n_threads=2):
-        """Process a stream of documents.
+        """
+        Process a stream of documents.
 
         Arguments:
             stream: The sequence of documents to process.
@@ -296,7 +303,8 @@ cdef class Parser:
         return 0
 
     def update(self, Doc tokens, GoldParse gold, itn=0):
-        """Update the statistical model.
+        """
+        Update the statistical model.
 
         Arguments:
             doc (Doc):
@@ -334,15 +342,17 @@ cdef class Parser:
         self.moves.finalize_state(stcls.c)
         return loss
 
-    def step_through(self, Doc doc):
+    def step_through(self, Doc doc, GoldParse gold=None):
-        """Set up a stepwise state, to introspect and control the transition sequence.
+        """
+        Set up a stepwise state, to introspect and control the transition sequence.
 
         Arguments:
             doc (Doc): The document to step through.
+            gold (GoldParse): Optional gold parse
         Returns (StepwiseState):
             A state object, to step through the annotation process.
         """
-        return StepwiseState(self, doc)
+        return StepwiseState(self, doc, gold=gold)
 
     def from_transition_sequence(self, Doc doc, sequence):
         """Control the annotations on a document by specifying a transition sequence
@@ -360,18 +370,28 @@ cdef class Parser:
     def add_label(self, label):
         # Doesn't set label into serializer -- subclasses override it to do that.
         for action in self.moves.action_types:
-            self.moves.add_action(action, label)
+            added = self.moves.add_action(action, label)
+            if added:
+                # Important that the labels be stored as a list! We need the
+                # order, or the model goes out of synch
+                self.cfg.setdefault('extra_labels', []).append(label)
 
 
 cdef class StepwiseState:
     cdef readonly StateClass stcls
     cdef readonly Example eg
     cdef readonly Doc doc
+    cdef readonly GoldParse gold
     cdef readonly Parser parser
 
-    def __init__(self, Parser parser, Doc doc):
+    def __init__(self, Parser parser, Doc doc, GoldParse gold=None):
         self.parser = parser
         self.doc = doc
+        if gold is not None:
+            self.gold = gold
+            self.parser.moves.preprocess_gold(self.gold)
+        else:
+            self.gold = GoldParse(doc)
         self.stcls = StateClass.init(doc.c, doc.length)
         self.parser.moves.initialize_state(self.stcls.c)
         self.eg = Example(
@@ -406,6 +426,24 @@ cdef class StepwiseState:
         return [self.doc.vocab.strings[self.stcls.c._sent[i].dep]
                 for i in range(self.stcls.c.length)]
 
+    @property
+    def costs(self):
+        """
+        Find the action-costs for the current state.
+        """
+        if not self.gold:
+            raise ValueError("Can't set costs: No GoldParse provided")
+        self.parser.moves.set_costs(self.eg.c.is_valid, self.eg.c.costs,
+                self.stcls, self.gold)
+        costs = {}
+        for i in range(self.parser.moves.n_moves):
+            if not self.eg.c.is_valid[i]:
+                continue
+            transition = self.parser.moves.c[i]
+            name = self.parser.moves.move_name(transition.move, transition.label)
+            costs[name] = self.eg.c.costs[i]
+        return costs
+
     def predict(self):
         self.eg.reset()
         self.eg.c.nr_feat = self.parser.model.set_featuresC(self.eg.c.atoms, self.eg.c.features,

spacy/syntax/stateclass.pyx

@@ -1,5 +1,9 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
 from libc.string cimport memcpy, memset
 from libc.stdint cimport uint32_t
+
 from ..vocab cimport EMPTY_LEXEME
 from ..structs cimport Entity
 from ..lexeme cimport Lexeme

spacy/syntax/transition_system.pyx

@@ -1,4 +1,8 @@
 # cython: infer_types=True
+# coding: utf-8
+from __future__ import unicode_literals
+
+from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
 from cymem.cymem cimport Pool
 from thinc.typedefs cimport weight_t
 from collections import defaultdict
@@ -6,7 +10,6 @@ from collections import defaultdict
 from ..structs cimport TokenC
 from .stateclass cimport StateClass
 from ..attrs cimport TAG, HEAD, DEP, ENT_TYPE, ENT_IOB
-from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
 
 
 cdef weight_t MIN_SCORE = -90000
@@ -32,7 +35,7 @@ cdef class TransitionSystem:
         self.c = <Transition*>self.mem.alloc(self._size, sizeof(Transition))
 
         for action, label_strs in sorted(labels_by_action.items()):
-            for label_str in sorted(label_strs):
+            for label_str in label_strs:
                 self.add_action(int(action), label_str)
         self.root_label = self.strings['ROOT']
         self.freqs = {} if _freqs is None else _freqs

spacy/syntax/util.py

@@ -1,18 +0,0 @@
-from os import path
-import json
-
-class Config(object):
-    def __init__(self, **kwargs):
-        for key, value in kwargs.items():
-            setattr(self, key, value)
-
-    def get(self, attr, default=None):
-        return self.__dict__.get(attr, default)
-
-    @classmethod
-    def write(cls, model_dir, name, **kwargs):
-        open(path.join(model_dir, '%s.json' % name), 'w').write(json.dumps(kwargs))
-
-    @classmethod
-    def read(cls, model_dir, name):
-        return cls(**json.load(open(path.join(model_dir, '%s.json' % name))))

spacy/tagger.pyx

@@ -1,5 +1,7 @@
-import json
+# coding: utf8
-import pathlib
+from __future__ import unicode_literals
+
+import ujson
 from collections import defaultdict
 
 from cymem.cymem cimport Pool
@@ -12,8 +14,8 @@ from thinc.linalg cimport VecVec
 from .tokens.doc cimport Doc
 from .attrs cimport TAG
 from .gold cimport GoldParse
-
 from .attrs cimport *
+from . import util
 
 
 cpdef enum:
@@ -106,10 +108,13 @@ cdef inline void _fill_from_token(atom_t* context, const TokenC* t) nogil:
 
 
 cdef class Tagger:
-    """Annotate part-of-speech tags on Doc objects."""
+    """
+    Annotate part-of-speech tags on Doc objects.
+    """
     @classmethod
     def load(cls, path, vocab, require=False):
-        """Load the statistical model from the supplied path.
+        """
+        Load the statistical model from the supplied path.
 
         Arguments:
             path (Path):
@@ -123,10 +128,10 @@ cdef class Tagger:
         """
         # TODO: Change this to expect config.json when we don't have to
         # support old data.
-        path = path if not isinstance(path, basestring) else pathlib.Path(path)
+        path = util.ensure_path(path)
         if (path / 'templates.json').exists():
             with (path / 'templates.json').open('r', encoding='utf8') as file_:
-                templates = json.load(file_)
+                templates = ujson.load(file_)
         elif require:
             raise IOError(
                 "Required file %s/templates.json not found when loading Tagger" % str(path))
@@ -142,7 +147,8 @@ cdef class Tagger:
         return self
 
     def __init__(self, Vocab vocab, TaggerModel model=None, **cfg):
-        """Create a Tagger.
+        """
+        Create a Tagger.
 
         Arguments:
             vocab (Vocab):
@@ -180,7 +186,8 @@ cdef class Tagger:
         tokens._py_tokens = [None] * tokens.length
 
     def __call__(self, Doc tokens):
-        """Apply the tagger, setting the POS tags onto the Doc object.
+        """
+        Apply the tagger, setting the POS tags onto the Doc object.
 
         Arguments:
             doc (Doc): The tokens to be tagged.
@@ -208,7 +215,8 @@ cdef class Tagger:
         tokens._py_tokens = [None] * tokens.length
 
     def pipe(self, stream, batch_size=1000, n_threads=2):
-        """Tag a stream of documents.
+        """
+        Tag a stream of documents.
 
         Arguments:
             stream: The sequence of documents to tag.
@@ -225,7 +233,8 @@ cdef class Tagger:
             yield doc
 
     def update(self, Doc tokens, GoldParse gold, itn=0):
-        """Update the statistical model, with tags supplied for the given document.
+        """
+        Update the statistical model, with tags supplied for the given document.
 
         Arguments:
             doc (Doc):

spacy/tests/he/test_tokenizer.py

@@ -3,15 +3,21 @@ from __future__ import unicode_literals
 
 import pytest
 
-ABBREVIATION_TESTS = [
-    ('פייתון היא שפת תכנות דינמית', ['פייתון', 'היא', 'שפת', 'תכנות', 'דינמית'])
-]
 
-TESTCASES = ABBREVIATION_TESTS
+@pytest.mark.parametrize('text,expected_tokens',
-
+    [('פייתון היא שפת תכנות דינמית', ['פייתון', 'היא', 'שפת', 'תכנות', 'דינמית'])])
-
+def test_tokenizer_handles_abbreviation(he_tokenizer, text, expected_tokens):
-@pytest.mark.parametrize('text,expected_tokens', TESTCASES)
-def test_tokenizer_handles_testcases(he_tokenizer, text, expected_tokens):
     tokens = he_tokenizer(text)
     token_list = [token.text for token in tokens if not token.is_space]
     assert expected_tokens == token_list
+
+
+@pytest.mark.parametrize('text,expected_tokens', [
+    pytest.mark.xfail(('עקבת אחריו בכל רחבי המדינה.', ['עקבת', 'אחריו', 'בכל', 'רחבי', 'המדינה', '.'])),
+    ('עקבת אחריו בכל רחבי המדינה?', ['עקבת', 'אחריו', 'בכל', 'רחבי', 'המדינה', '?']),
+    ('עקבת אחריו בכל רחבי המדינה!', ['עקבת', 'אחריו', 'בכל', 'רחבי', 'המדינה', '!']),
+    ('עקבת אחריו בכל רחבי המדינה..', ['עקבת', 'אחריו', 'בכל', 'רחבי', 'המדינה', '..']),
+    ('עקבת אחריו בכל רחבי המדינה...', ['עקבת', 'אחריו', 'בכל', 'רחבי', 'המדינה', '...'])])
+def test_tokenizer_handles_punct(he_tokenizer, text, expected_tokens):
+    tokens = he_tokenizer(text)
+    assert expected_tokens == [token.text for token in tokens]

spacy/tests/tagger/test_lemmatizer.py

@@ -16,6 +16,7 @@ def test_tagger_lemmatizer_noun_lemmas(lemmatizer, text, lemmas):
     assert lemmatizer.noun(text) == set(lemmas)
 
 
+@pytest.mark.xfail
 @pytest.mark.models
 def test_tagger_lemmatizer_base_forms(lemmatizer):
     if lemmatizer is None:

spacy/tests/tokenizer/test_tokenizer.py

@@ -3,9 +3,8 @@ from __future__ import unicode_literals
 
 from ...vocab import Vocab
 from ...tokenizer import Tokenizer
-from ...util import utf8open
+from ... import util
 
-from os import path
 import pytest
 
 
@@ -75,8 +74,8 @@ Phasellus tincidunt, augue quis porta finibus, massa sapien consectetur augue, n
 
 @pytest.mark.parametrize('file_name', ["sun.txt"])
 def test_tokenizer_handle_text_from_file(tokenizer, file_name):
-    loc = path.join(path.dirname(__file__), file_name)
+    loc = util.ensure_path(__file__).parent / file_name
-    text = utf8open(loc).read()
+    text = loc.open('r', encoding='utf8').read()
     assert len(text) != 0
     tokens = tokenizer(text)
     assert len(tokens) > 100

spacy/tokenizer.pyx

@@ -1,17 +1,11 @@
 # cython: embedsignature=True
+# coding: utf8
 from __future__ import unicode_literals
 
-import pathlib
+import ujson
 
 from cython.operator cimport dereference as deref
 from cython.operator cimport preincrement as preinc
-
-try:
-    import ujson as json
-except ImportError:
-    import json
-
-
 from cymem.cymem cimport Pool
 from preshed.maps cimport PreshMap
 
@@ -23,11 +17,14 @@ from .tokens.doc cimport Doc
 
 
 cdef class Tokenizer:
-    """Segment text, and create Doc objects with the discovered segment boundaries."""
+    """
+    Segment text, and create Doc objects with the discovered segment boundaries.
+    """
     @classmethod
     def load(cls, path, Vocab vocab, rules=None, prefix_search=None, suffix_search=None,
              infix_finditer=None, token_match=None):
-        '''Load a Tokenizer, reading unsupplied components from the path.
+        """
+        Load a Tokenizer, reading unsupplied components from the path.
 
         Arguments:
             path (Path):
@@ -45,13 +42,11 @@ cdef class Tokenizer:
             infix_finditer:
                 Signature of re.compile(string).finditer
         Returns Tokenizer
-        '''
+        """
-        if isinstance(path, basestring):
+        path = util.ensure_path(path)
-            path = pathlib.Path(path)
-
         if rules is None:
             with (path / 'tokenizer' / 'specials.json').open('r', encoding='utf8') as file_:
-                rules = json.load(file_)
+                rules = ujson.load(file_)
         if prefix_search in (None, True):
             with (path / 'tokenizer' / 'prefix.txt').open() as file_:
                 entries = file_.read().split('\n')
@@ -67,7 +62,8 @@ cdef class Tokenizer:
         return cls(vocab, rules, prefix_search, suffix_search, infix_finditer, token_match)
 
     def __init__(self, Vocab vocab, rules, prefix_search, suffix_search, infix_finditer, token_match=None):
-        '''Create a Tokenizer, to create Doc objects given unicode text.
+        """
+        Create a Tokenizer, to create Doc objects given unicode text.
 
         Arguments:
             vocab (Vocab):
@@ -85,7 +81,7 @@ cdef class Tokenizer:
                 to find infixes.
             token_match:
                 A boolean function matching strings that becomes tokens.
-        '''
+        """
         self.mem = Pool()
         self._cache = PreshMap()
         self._specials = PreshMap()
@@ -117,7 +113,8 @@ cdef class Tokenizer:
 
     @cython.boundscheck(False)
     def __call__(self, unicode string):
-        """Tokenize a string.
+        """
+        Tokenize a string.
 
         Arguments:
             string (unicode): The string to tokenize.
@@ -170,7 +167,8 @@ cdef class Tokenizer:
         return tokens
 
     def pipe(self, texts, batch_size=1000, n_threads=2):
-        """Tokenize a stream of texts.
+        """
+        Tokenize a stream of texts.
 
         Arguments:
             texts: A sequence of unicode texts.
@@ -324,7 +322,8 @@ cdef class Tokenizer:
         self._cache.set(key, cached)
 
     def find_infix(self, unicode string):
-        """Find internal split points of the string, such as hyphens.
+        """
+        Find internal split points of the string, such as hyphens.
 
         string (unicode): The string to segment.
 
@@ -337,7 +336,8 @@ cdef class Tokenizer:
         return list(self.infix_finditer(string))
 
     def find_prefix(self, unicode string):
-        """Find the length of a prefix that should be segmented from the string,
+        """
+        Find the length of a prefix that should be segmented from the string,
         or None if no prefix rules match.
 
         Arguments:
@@ -350,7 +350,8 @@ cdef class Tokenizer:
         return (match.end() - match.start()) if match is not None else 0
 
     def find_suffix(self, unicode string):
-        """Find the length of a suffix that should be segmented from the string,
+        """
+        Find the length of a suffix that should be segmented from the string,
         or None if no suffix rules match.
 
         Arguments:
@@ -363,13 +364,15 @@ cdef class Tokenizer:
         return (match.end() - match.start()) if match is not None else 0
 
     def _load_special_tokenization(self, special_cases):
-        '''Add special-case tokenization rules.
+        """
-        '''
+        Add special-case tokenization rules.
+        """
         for chunk, substrings in sorted(special_cases.items()):
             self.add_special_case(chunk, substrings)
 
     def add_special_case(self, unicode string, substrings):
-        '''Add a special-case tokenization rule.
+        """
+        Add a special-case tokenization rule.
 
         Arguments:
             string (unicode): The string to specially tokenize.
@@ -378,7 +381,7 @@ cdef class Tokenizer:
                 attributes. The ORTH fields of the attributes must exactly match
                 the string when they are concatenated.
         Returns None
-        '''
+        """
         substrings = list(substrings)
         cached = <_Cached*>self.mem.alloc(1, sizeof(_Cached))
         cached.length = len(substrings)

spacy/tokens/doc.pyx

@@ -1,15 +1,18 @@
+# coding: utf8
+from __future__ import unicode_literals
+
 cimport cython
+cimport numpy as np
+import numpy
+import numpy.linalg
+import struct
+
 from libc.string cimport memcpy, memset
 from libc.stdint cimport uint32_t
 from libc.math cimport sqrt
 
-import numpy
+from .span cimport Span
-import numpy.linalg
+from .token cimport Token
-import struct
-cimport numpy as np
-import six
-import warnings
-
 from ..lexeme cimport Lexeme
 from ..lexeme cimport EMPTY_LEXEME
 from ..typedefs cimport attr_t, flags_t
@@ -19,11 +22,10 @@ from ..attrs cimport POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB, ENT_TYPE
 from ..parts_of_speech cimport CCONJ, PUNCT, NOUN
 from ..parts_of_speech cimport univ_pos_t
 from ..lexeme cimport Lexeme
-from .span cimport Span
-from .token cimport Token
 from ..serialize.bits cimport BitArray
 from ..util import normalize_slice
 from ..syntax.iterators import CHUNKERS
+from ..compat import is_config
 
 
 DEF PADDING = 5
@@ -76,7 +78,7 @@ cdef class Doc:
 
     """
     def __init__(self, Vocab vocab, words=None, spaces=None, orths_and_spaces=None):
-        '''
+        """
         Create a Doc object.
 
         Aside: Implementation
@@ -97,7 +99,7 @@ cdef class Doc:
                 A list of boolean values, of the same length as words. True
                 means that the word is followed by a space, False means it is not.
                 If None, defaults to [True]*len(words)
-        '''
+        """
         self.vocab = vocab
         size = 20
         self.mem = Pool()
@@ -158,7 +160,7 @@ cdef class Doc:
             self.is_parsed = True
 
     def __getitem__(self, object i):
-        '''
+        """
         doc[i]
             Get the Token object at position i, where i is an integer.
             Negative indexing is supported, and follows the usual Python
@@ -172,7 +174,7 @@ cdef class Doc:
             are not supported, as `Span` objects must be contiguous (cannot have gaps).
             You can use negative indices and open-ended ranges, which have their
             normal Python semantics.
-        '''
+        """
         if isinstance(i, slice):
             start, stop = normalize_slice(len(self), i.start, i.stop, i.step)
             return Span(self, start, stop, label=0)
@@ -186,7 +188,7 @@ cdef class Doc:
             return Token.cinit(self.vocab, &self.c[i], i, self)
 
     def __iter__(self):
-        '''
+        """
         for token in doc
             Iterate over `Token`  objects, from which the annotations can
             be easily accessed. This is the main way of accessing Token
@@ -194,7 +196,7 @@ cdef class Doc:
             Python. If faster-than-Python speeds are required, you can
             instead access the annotations as a numpy array, or access the
             underlying C data directly from Cython.
-        '''
+        """
         cdef int i
         for i in range(self.length):
             if self._py_tokens[i] is not None:
@@ -203,10 +205,10 @@ cdef class Doc:
                 yield Token.cinit(self.vocab, &self.c[i], i, self)
 
     def __len__(self):
-        '''
+        """
         len(doc)
             The number of tokens in the document.
-        '''
+        """
         return self.length
 
     def __unicode__(self):
@@ -216,7 +218,7 @@ cdef class Doc:
         return u''.join([t.text_with_ws for t in self]).encode('utf-8')
 
     def __str__(self):
-        if six.PY3:
+        if is_config(python3=True):
             return self.__unicode__()
         return self.__bytes__()
 
@@ -228,7 +230,8 @@ cdef class Doc:
         return self
 
     def similarity(self, other):
-        '''Make a semantic similarity estimate. The default estimate is cosine
+        """
+        Make a semantic similarity estimate. The default estimate is cosine
         similarity using an average of word vectors.
 
         Arguments:
@@ -237,7 +240,7 @@ cdef class Doc:
 
         Return:
             score (float): A scalar similarity score. Higher is more similar.
-        '''
+        """
         if 'similarity' in self.user_hooks:
             return self.user_hooks['similarity'](self, other)
         if self.vector_norm == 0 or other.vector_norm == 0:
@@ -245,9 +248,9 @@ cdef class Doc:
         return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
 
     property has_vector:
-        '''
+        """
         A boolean value indicating whether a word vector is associated with the object.
-        '''
+        """
         def __get__(self):
             if 'has_vector' in self.user_hooks:
                 return self.user_hooks['has_vector'](self)
@@ -255,11 +258,11 @@ cdef class Doc:
             return any(token.has_vector for token in self)
 
     property vector:
-        '''
+        """
         A real-valued meaning representation. Defaults to an average of the token vectors.
 
         Type: numpy.ndarray[ndim=1, dtype='float32']
-        '''
+        """
         def __get__(self):
             if 'vector' in self.user_hooks:
                 return self.user_hooks['vector'](self)
@@ -294,17 +297,21 @@ cdef class Doc:
         return self.text
 
     property text:
-        '''A unicode representation of the document text.'''
+        """
+        A unicode representation of the document text.
+        """
         def __get__(self):
             return u''.join(t.text_with_ws for t in self)
 
     property text_with_ws:
-        '''An alias of Doc.text, provided for duck-type compatibility with Span and Token.'''
+        """
+        An alias of Doc.text, provided for duck-type compatibility with Span and Token.
+        """
         def __get__(self):
             return self.text
 
     property ents:
-        '''
+        """
         Yields named-entity `Span` objects, if the entity recognizer
         has been applied to the document. Iterate over the span to get
         individual Token objects, or access the label:
@@ -318,7 +325,7 @@ cdef class Doc:
             assert ents[0].label_ == 'PERSON'
             assert ents[0].orth_ == 'Best'
             assert ents[0].text == 'Mr. Best'
-        '''
+        """
         def __get__(self):
             cdef int i
             cdef const TokenC* token
@@ -382,13 +389,13 @@ cdef class Doc:
                     self.c[start].ent_iob = 3
 
     property noun_chunks:
-        '''
+        """
         Yields base noun-phrase #[code Span] objects, if the document
         has been syntactically parsed. A base noun phrase, or
         'NP chunk', is a noun phrase that does not permit other NPs to
         be nested within it – so no NP-level coordination, no prepositional
-        phrases, and no relative clauses. For example:
+        phrases, and no relative clauses.
-        '''
+        """
         def __get__(self):
             if not self.is_parsed:
                 raise ValueError(
@@ -496,7 +503,8 @@ cdef class Doc:
         return output
 
     def count_by(self, attr_id_t attr_id, exclude=None, PreshCounter counts=None):
-        """Produce a dict of {attribute (int): count (ints)} frequencies, keyed
+        """
+        Produce a dict of {attribute (int): count (ints)} frequencies, keyed
         by the values of the given attribute ID.
 
         Example:
@@ -563,8 +571,9 @@ cdef class Doc:
             self.c[i] = parsed[i]
 
     def from_array(self, attrs, array):
-        '''Write to a `Doc` object, from an `(M, N)` array of attributes.
+        """
-        '''
+        Write to a `Doc` object, from an `(M, N)` array of attributes.
+        """
         cdef int i, col
         cdef attr_id_t attr_id
         cdef TokenC* tokens = self.c
@@ -603,19 +612,23 @@ cdef class Doc:
         return self
 
     def to_bytes(self):
-        '''Serialize, producing a byte string.'''
+        """
+        Serialize, producing a byte string.
+        """
         byte_string = self.vocab.serializer.pack(self)
         cdef uint32_t length = len(byte_string)
         return struct.pack('I', length) + byte_string
 
     def from_bytes(self, data):
-        '''Deserialize, loading from bytes.'''
+        """
+        Deserialize, loading from bytes.
+        """
         self.vocab.serializer.unpack_into(data[4:], self)
         return self
 
     @staticmethod
     def read_bytes(file_):
-        '''
+        """
         A static method, used to read serialized #[code Doc] objects from
         a file. For example:
 
@@ -630,7 +643,7 @@ cdef class Doc:
                 for byte_string in Doc.read_bytes(file_):
                     docs.append(Doc(nlp.vocab).from_bytes(byte_string))
             assert len(docs) == 2
-        '''
+        """
         keep_reading = True
         while keep_reading:
             try:
@@ -644,7 +657,8 @@ cdef class Doc:
             yield n_bytes_str + data
 
     def merge(self, int start_idx, int end_idx, *args, **attributes):
-        """Retokenize the document, such that the span at doc.text[start_idx : end_idx]
+        """
+        Retokenize the document, such that the span at doc.text[start_idx : end_idx]
         is merged into a single token. If start_idx and end_idx do not mark start
         and end token boundaries, the document remains unchanged.
 
@@ -658,7 +672,6 @@ cdef class Doc:
             token (Token):
                 The newly merged token, or None if the start and end indices did
                 not fall at token boundaries.
-
         """
         cdef unicode tag, lemma, ent_type
         if len(args) == 3:

spacy/tokens/span.pyx

@@ -1,26 +1,31 @@
+# coding: utf8
 from __future__ import unicode_literals
 from collections import defaultdict
+
+cimport numpy as np
 import numpy
 import numpy.linalg
-cimport numpy as np
 from libc.math cimport sqrt
-import six
 
+from .doc cimport token_by_start, token_by_end
 from ..structs cimport TokenC, LexemeC
 from ..typedefs cimport flags_t, attr_t, hash_t
 from ..attrs cimport attr_id_t
 from ..parts_of_speech cimport univ_pos_t
 from ..util import normalize_slice
-from .doc cimport token_by_start, token_by_end
 from ..attrs cimport IS_PUNCT, IS_SPACE
 from ..lexeme cimport Lexeme
+from ..compat import is_config
 
 
 cdef class Span:
-    """A slice from a Doc object."""
+    """
+    A slice from a Doc object.
+    """
     def __cinit__(self, Doc doc, int start, int end, int label=0, vector=None,
                   vector_norm=None):
-        '''Create a Span object from the slice doc[start : end]
+        """
+        Create a Span object from the slice doc[start : end]
 
         Arguments:
             doc (Doc): The parent document.
@@ -30,7 +35,7 @@ cdef class Span:
             vector (ndarray[ndim=1, dtype='float32']): A meaning representation of the span.
         Returns:
             Span The newly constructed object.
-        '''
+        """
         if not (0 <= start <= end <= len(doc)):
             raise IndexError
 
@@ -68,7 +73,7 @@ cdef class Span:
         return self.end - self.start
 
     def __repr__(self):
-        if six.PY3:
+        if is_config(python3=True):
             return self.text
         return self.text.encode('utf-8')
 
@@ -89,7 +94,8 @@ cdef class Span:
             yield self.doc[i]
 
     def merge(self, *args, **attributes):
-        """Retokenize the document, such that the span is merged into a single token.
+        """
+        Retokenize the document, such that the span is merged into a single token.
 
         Arguments:
             **attributes:
@@ -102,7 +108,8 @@ cdef class Span:
         return self.doc.merge(self.start_char, self.end_char, *args, **attributes)
 
     def similarity(self, other):
-        '''Make a semantic similarity estimate. The default estimate is cosine
+        """
+        Make a semantic similarity estimate. The default estimate is cosine
         similarity using an average of word vectors.
 
         Arguments:
@@ -111,7 +118,7 @@ cdef class Span:
 
         Return:
             score (float): A scalar similarity score. Higher is more similar.
-        '''
+        """
         if 'similarity' in self.doc.user_span_hooks:
             self.doc.user_span_hooks['similarity'](self, other)
         if self.vector_norm == 0.0 or other.vector_norm == 0.0:
@@ -133,11 +140,12 @@ cdef class Span:
             self.end = end + 1
 
     property sent:
-        '''The sentence span that this span is a part of.
+        """
+        The sentence span that this span is a part of.
 
         Returns:
             Span The sentence this is part of.
-        '''
+        """
         def __get__(self):
             if 'sent' in self.doc.user_span_hooks:
                 return self.doc.user_span_hooks['sent'](self)
@@ -198,13 +206,13 @@ cdef class Span:
             return u''.join([t.text_with_ws for t in self])
 
     property noun_chunks:
-        '''
+        """
         Yields base noun-phrase #[code Span] objects, if the document
         has been syntactically parsed. A base noun phrase, or
         'NP chunk', is a noun phrase that does not permit other NPs to
         be nested within it – so no NP-level coordination, no prepositional
         phrases, and no relative clauses. For example:
-        '''
+        """
         def __get__(self):
             if not self.doc.is_parsed:
                 raise ValueError(
@@ -223,17 +231,16 @@ cdef class Span:
                 yield span
 
     property root:
-        """The token within the span that's highest in the parse tree. If there's a tie, the earlist is prefered.
+        """
+        The token within the span that's highest in the parse tree. If there's a
+        tie, the earlist is prefered.
 
         Returns:
             Token: The root token.
 
-        i.e. has the
+        i.e. has the shortest path to the root of the sentence (or is the root
-        shortest path to the root of the sentence (or is the root itself).
+        itself). If multiple words are equally high in the tree, the first word
-
+        is taken. For example:
-        If multiple words are equally high in the tree, the first word is taken.
-
-        For example:
 
         >>> toks = nlp(u'I like New York in Autumn.')
 
@@ -303,7 +310,8 @@ cdef class Span:
                 return self.doc[root]
 
     property lefts:
-        """Tokens that are to the left of the span, whose head is within the Span.
+        """
+        Tokens that are to the left of the span, whose head is within the Span.
 
         Yields: Token A left-child of a token of the span.
         """
@@ -314,7 +322,8 @@ cdef class Span:
                         yield left
 
     property rights:
-        """Tokens that are to the right of the Span, whose head is within the Span.
+        """
+        Tokens that are to the right of the Span, whose head is within the Span.
 
         Yields: Token A right-child of a token of the span.
         """
@@ -325,7 +334,8 @@ cdef class Span:
                         yield right
 
     property subtree:
-        """Tokens that descend from tokens in the span, but fall outside it.
+        """
+        Tokens that descend from tokens in the span, but fall outside it.
 
         Yields: Token A descendant of a token within the span.
         """
@@ -337,7 +347,9 @@ cdef class Span:
                 yield from word.subtree
 
     property ent_id:
-        '''An (integer) entity ID. Usually assigned by patterns in the Matcher.'''
+        """
+        An (integer) entity ID. Usually assigned by patterns in the Matcher.
+        """
         def __get__(self):
             return self.root.ent_id
 
@@ -345,9 +357,11 @@ cdef class Span:
             # TODO
             raise NotImplementedError(
                 "Can't yet set ent_id from Span. Vote for this feature on the issue "
-                "tracker: http://github.com/spacy-io/spaCy")
+                "tracker: http://github.com/explosion/spaCy/issues")
     property ent_id_:
-        '''A (string) entity ID. Usually assigned by patterns in the Matcher.'''
+        """
+        A (string) entity ID. Usually assigned by patterns in the Matcher.
+        """
         def __get__(self):
             return self.root.ent_id_
 
@@ -355,7 +369,7 @@ cdef class Span:
             # TODO
             raise NotImplementedError(
                 "Can't yet set ent_id_ from Span. Vote for this feature on the issue "
-                "tracker: http://github.com/spacy-io/spaCy")
+                "tracker: http://github.com/explosion/spaCy/issues")
 
     property orth_:
         def __get__(self):
@@ -397,5 +411,5 @@ cdef int _count_words_to_root(const TokenC* token, int sent_length) except -1:
             raise RuntimeError(
                 "Array bounds exceeded while searching for root word. This likely "
                 "means the parse tree is in an invalid state. Please report this "
-                "issue here: http://github.com/honnibal/spaCy/")
+                "issue here: http://github.com/explosion/spaCy/issues")
     return n

spacy/tokens/token.pyx

@@ -1,5 +1,5 @@
-# coding: utf8
 # cython: infer_types=True
+# coding: utf8
 from __future__ import unicode_literals
 
 from libc.string cimport memcpy
@@ -8,20 +8,15 @@ from cpython.mem cimport PyMem_Malloc, PyMem_Free
 from cython.view cimport array as cvarray
 cimport numpy as np
 np.import_array()
-
 import numpy
-import six
-
 
 from ..typedefs cimport hash_t
 from ..lexeme cimport Lexeme
 from .. import parts_of_speech
-
 from ..attrs cimport LEMMA
 from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, LENGTH, CLUSTER
 from ..attrs cimport POS, LEMMA, TAG, DEP
 from ..parts_of_speech cimport CCONJ, PUNCT
-
 from ..attrs cimport IS_ALPHA, IS_ASCII, IS_DIGIT, IS_LOWER, IS_PUNCT, IS_SPACE
 from ..attrs cimport IS_BRACKET
 from ..attrs cimport IS_QUOTE
@@ -29,12 +24,13 @@ from ..attrs cimport IS_LEFT_PUNCT
 from ..attrs cimport IS_RIGHT_PUNCT
 from ..attrs cimport IS_TITLE, IS_UPPER, LIKE_URL, LIKE_NUM, LIKE_EMAIL, IS_STOP
 from ..attrs cimport IS_OOV
-
 from ..lexeme cimport Lexeme
+from ..compat import is_config
 
 
 cdef class Token:
-    """An individual token --- i.e. a word, punctuation symbol, whitespace, etc.
+    """
+    An individual token --- i.e. a word, punctuation symbol, whitespace, etc.
     """
     def __cinit__(self, Vocab vocab, Doc doc, int offset):
         self.vocab = vocab
@@ -46,7 +42,9 @@ cdef class Token:
         return hash((self.doc, self.i))
 
     def __len__(self):
-        '''Number of unicode characters in token.text'''
+        """
+        Number of unicode characters in token.text.
+        """
         return self.c.lex.length
 
     def __unicode__(self):
@@ -56,7 +54,7 @@ cdef class Token:
         return self.text.encode('utf8')
 
     def __str__(self):
-        if six.PY3:
+        if is_config(python3=True):
             return self.__unicode__()
         return self.__bytes__()
 
@@ -83,27 +81,30 @@ cdef class Token:
             raise ValueError(op)
 
     cpdef bint check_flag(self, attr_id_t flag_id) except -1:
-        '''Check the value of a boolean flag.
+        """
+        Check the value of a boolean flag.
 
         Arguments:
             flag_id (int): The ID of the flag attribute.
         Returns:
             is_set (bool): Whether the flag is set.
-        '''
+        """
         return Lexeme.c_check_flag(self.c.lex, flag_id)
 
     def nbor(self, int i=1):
-        '''Get a neighboring token.
+        """
+        Get a neighboring token.
 
         Arguments:
             i (int): The relative position of the token to get. Defaults to 1.
         Returns:
             neighbor (Token): The token at position self.doc[self.i+i]
-        '''
+        """
         return self.doc[self.i+i]
 
     def similarity(self, other):
-        '''Compute a semantic similarity estimate. Defaults to cosine over vectors.
+        """
+        Compute a semantic similarity estimate. Defaults to cosine over vectors.
 
         Arguments:
             other:
@@ -111,7 +112,7 @@ cdef class Token:
                 Token and Lexeme objects.
         Returns:
             score (float): A scalar similarity score. Higher is more similar.
-        '''
+        """
         if 'similarity' in self.doc.user_token_hooks:
                 return self.doc.user_token_hooks['similarity'](self)
         if self.vector_norm == 0 or other.vector_norm == 0:
@@ -191,6 +192,8 @@ cdef class Token:
     property lemma:
         def __get__(self):
             return self.c.lemma
+        def __set__(self, int lemma):
+            self.c.lemma = lemma
 
     property pos:
         def __get__(self):
@@ -209,9 +212,9 @@ cdef class Token:
             self.c.dep = label
 
     property has_vector:
-        '''
+        """
         A boolean value indicating whether a word vector is associated with the object.
-        '''
+        """
         def __get__(self):
             if 'has_vector' in self.doc.user_token_hooks:
                 return self.doc.user_token_hooks['has_vector'](self)
@@ -223,11 +226,11 @@ cdef class Token:
                 return False
 
     property vector:
-        '''
+        """
         A real-valued meaning representation.
 
         Type: numpy.ndarray[ndim=1, dtype='float32']
-        '''
+        """
         def __get__(self):
             if 'vector' in self.doc.user_token_hooks:
                 return self.doc.user_token_hooks['vector'](self)
@@ -245,6 +248,7 @@ cdef class Token:
     property repvec:
         def __get__(self):
             raise AttributeError("repvec was renamed to vector in v0.100")
+
     property has_repvec:
         def __get__(self):
             raise AttributeError("has_repvec was renamed to has_vector in v0.100")
@@ -265,7 +269,8 @@ cdef class Token:
 
     property lefts:
         def __get__(self):
-            """The leftward immediate children of the word, in the syntactic
+            """
+            The leftward immediate children of the word, in the syntactic
             dependency parse.
             """
             cdef int nr_iter = 0
@@ -282,8 +287,10 @@ cdef class Token:
 
     property rights:
         def __get__(self):
-            """The rightward immediate children of the word, in the syntactic
+            """
-            dependency parse."""
+            The rightward immediate children of the word, in the syntactic
+            dependency parse.
+            """
             cdef const TokenC* ptr = self.c + (self.c.r_edge - self.i)
             tokens = []
             cdef int nr_iter = 0
@@ -300,19 +307,21 @@ cdef class Token:
                 yield t
 
     property children:
-        '''A sequence of the token's immediate syntactic children.
+        """
+        A sequence of the token's immediate syntactic children.
 
         Yields: Token A child token such that child.head==self
-        '''
+        """
         def __get__(self):
             yield from self.lefts
             yield from self.rights
 
     property subtree:
-        '''A sequence of all the token's syntactic descendents.
+        """
+        A sequence of all the token's syntactic descendents.
 
         Yields: Token A descendent token such that self.is_ancestor(descendent)
-        '''
+        """
         def __get__(self):
             for word in self.lefts:
                 yield from word.subtree
@@ -321,26 +330,29 @@ cdef class Token:
                 yield from word.subtree
 
     property left_edge:
-        '''The leftmost token of this token's syntactic descendents.
+        """
+        The leftmost token of this token's syntactic descendents.
 
         Returns: Token The first token such that self.is_ancestor(token)
-        '''
+        """
         def __get__(self):
             return self.doc[self.c.l_edge]
 
     property right_edge:
-        '''The rightmost token of this token's syntactic descendents.
+        """
+        The rightmost token of this token's syntactic descendents.
 
         Returns: Token The last token such that self.is_ancestor(token)
-        '''
+        """
         def __get__(self):
             return self.doc[self.c.r_edge]
 
     property ancestors:
-        '''A sequence of this token's syntactic ancestors.
+        """
+        A sequence of this token's syntactic ancestors.
 
         Yields: Token A sequence of ancestor tokens such that ancestor.is_ancestor(self)
-        '''
+        """
         def __get__(self):
             cdef const TokenC* head_ptr = self.c
             # guard against infinite loop, no token can have
@@ -356,25 +368,29 @@ cdef class Token:
         return self.is_ancestor(descendant)
 
     def is_ancestor(self, descendant):
-        '''Check whether this token is a parent, grandparent, etc. of another
+        """
+        Check whether this token is a parent, grandparent, etc. of another
         in the dependency tree.
 
         Arguments:
             descendant (Token): Another token.
         Returns:
             is_ancestor (bool): Whether this token is the ancestor of the descendant.
-        '''
+        """
         if self.doc is not descendant.doc:
             return False
         return any( ancestor.i == self.i for ancestor in descendant.ancestors )
 
     property head:
-        '''The syntactic parent, or "governor", of this token.
+        """
+        The syntactic parent, or "governor", of this token.
 
         Returns: Token
-        '''
+        """
         def __get__(self):
-            """The token predicted by the parser to be the head of the current token."""
+            """
+            The token predicted by the parser to be the head of the current token.
+            """
             return self.doc[self.i + self.c.head]
         def __set__(self, Token new_head):
             # this function sets the head of self to new_head
@@ -467,10 +483,11 @@ cdef class Token:
             self.c.head = rel_newhead_i
 
     property conjuncts:
-        '''A sequence of coordinated tokens, including the token itself.
+        """
+        A sequence of coordinated tokens, including the token itself.
 
         Yields: Token A coordinated token
-        '''
+        """
         def __get__(self):
             """Get a list of conjoined words."""
             cdef Token word
@@ -501,7 +518,9 @@ cdef class Token:
             return iob_strings[self.c.ent_iob]
 
     property ent_id:
-        '''An (integer) entity ID. Usually assigned by patterns in the Matcher.'''
+        """
+        An (integer) entity ID. Usually assigned by patterns in the Matcher.
+        """
         def __get__(self):
             return self.c.ent_id
 
@@ -509,7 +528,9 @@ cdef class Token:
             self.c.ent_id = key
 
     property ent_id_:
-        '''A (string) entity ID. Usually assigned by patterns in the Matcher.'''
+        """
+        A (string) entity ID. Usually assigned by patterns in the Matcher.
+        """
         def __get__(self):
             return self.vocab.strings[self.c.ent_id]
 
@@ -551,6 +572,8 @@ cdef class Token:
     property lemma_:
         def __get__(self):
             return self.vocab.strings[self.c.lemma]
+        def __set__(self, unicode lemma_):
+            self.c.lemma = self.vocab.strings[lemma_]
 
     property pos_:
         def __get__(self):

spacy/train.py

@@ -1,15 +1,16 @@
-from __future__ import absolute_import
+# coding: utf8
-from __future__ import unicode_literals
+from __future__ import absolute_import, unicode_literals
 
 import random
 import tqdm
-from .gold import GoldParse
+from .gold import GoldParse, merge_sents
 from .scorer import Scorer
-from .gold import merge_sents
 
 
 class Trainer(object):
-    '''Manage training of an NLP pipeline.'''
+    """
+    Manage training of an NLP pipeline.
+    """
     def __init__(self, nlp, gold_tuples):
         self.nlp = nlp
         self.gold_tuples = gold_tuples

spacy/util.py

@@ -1,29 +1,17 @@
 # coding: utf8
 from __future__ import unicode_literals, print_function
-import os
+
-import io
+import ujson
-import json
 import re
-import os.path
+from pathlib import Path
-import pathlib
 import sys
 import textwrap
 
-
+from .compat import basestring_, unicode_, input_
-try:
-    basestring
-except NameError:
-    basestring = str
-
-
-try:
-    raw_input
-except NameError: # Python 3
-    raw_input = input
 
 
 LANGUAGES = {}
-_data_path = pathlib.Path(__file__).parent / 'data'
+_data_path = Path(__file__).parent / 'data'
 
 
 def set_lang_class(name, cls):
@@ -32,9 +20,11 @@ def set_lang_class(name, cls):
 
 
 def get_lang_class(name):
+    if name in LANGUAGES:
+        return LANGUAGES[name]
     lang = re.split('[^a-zA-Z0-9]', name, 1)[0]
     if lang not in LANGUAGES:
-        raise RuntimeError('Language not supported: %s' % lang)
+        raise RuntimeError('Language not supported: %s' % name)
     return LANGUAGES[lang]
 
 
@@ -47,55 +37,18 @@ def get_data_path(require_exists=True):
 
 def set_data_path(path):
     global _data_path
-    if isinstance(path, basestring):
+    _data_path = ensure_path(path)
-        path = pathlib.Path(path)
-    _data_path = path
 
 
-def or_(val1, val2):
+def ensure_path(path):
-    if val1 is not None:
+    if isinstance(path, basestring_):
-        return val1
+        return Path(path)
-    elif callable(val2):
-        return val2()
     else:
-        return val2
+        return path
-
-
-def match_best_version(target_name, target_version, path):
-    path = path if not isinstance(path, basestring) else pathlib.Path(path)
-    if path is None or not path.exists():
-        return None
-    matches = []
-    for data_name in path.iterdir():
-        name, version = split_data_name(data_name.parts[-1])
-        if name == target_name and constraint_match(target_version, version):
-            matches.append((tuple(float(v) for v in version.split('.')), data_name))
-    if matches:
-        return pathlib.Path(max(matches)[1])
-    else:
-        return None
-
-
-def split_data_name(name):
-    return name.split('-', 1) if '-' in name else (name, '')
-
-
-def constraint_match(constraint_string, version):
-    # From http://github.com/spacy-io/sputnik
-    if not constraint_string:
-        return True
-
-    constraints = [c.strip() for c in constraint_string.split(',') if c.strip()]
-
-    for c in constraints:
-        if not re.match(r'[><=][=]?\d+(\.\d+)*', c):
-            raise ValueError('invalid constraint: %s' % c)
-
-    return all(semver.match(version, c) for c in constraints)
 
 
 def read_regex(path):
-    path = path if not isinstance(path, basestring) else pathlib.Path(path)
+    path = ensure_path(path)
     with path.open() as file_:
         entries = file_.read().split('\n')
     expression = '|'.join(['^' + re.escape(piece) for piece in entries if piece.strip()])
@@ -142,32 +95,28 @@ def normalize_slice(length, start, stop, step=None):
     return start, stop
 
 
-def utf8open(loc, mode='r'):
-    return io.open(loc, mode, encoding='utf8')
-
-
 def check_renamed_kwargs(renamed, kwargs):
     for old, new in renamed.items():
         if old in kwargs:
             raise TypeError("Keyword argument %s now renamed to %s" % (old, new))
 
 
-def is_windows():
+def read_json(location):
-    """Check if user is on Windows."""
+    with location.open('r', encoding='utf8') as f:
-    return sys.platform.startswith('win')
+        return ujson.load(f)
-
-
-def is_python2():
-    """Check if Python 2 is used."""
-    return sys.version.startswith('2.')
 
 
 def parse_package_meta(package_path, package, require=True):
-    location = os.path.join(str(package_path), package, 'meta.json')
+    """
-    if os.path.isfile(location):
+    Check if a meta.json exists in a package and return its contents as a
-        with io.open(location, encoding='utf8') as f:
+    dictionary. If require is set to True, raise an error if no meta.json found.
-            meta = json.load(f)
+    """
-            return meta
+    # TODO: Allow passing in full model path and only require one argument
+    # instead of path and package name. This lets us avoid passing in an awkward
+    # empty string in spacy.load() if user supplies full model path.
+    location = package_path / package / 'meta.json'
+    if location.is_file():
+        return read_json(location)
     elif require:
         raise IOError("Could not read meta.json from %s" % location)
     else:
@@ -175,20 +124,22 @@ def parse_package_meta(package_path, package, require=True):
 
 
 def get_raw_input(description, default=False):
-    """Get user input via raw_input / input and return input value. Takes a
+    """
+    Get user input via raw_input / input and return input value. Takes a
     description for the prompt, and an optional default value that's displayed
-    with the prompt."""
+    with the prompt.
-
+    """
     additional = ' (default: {d})'.format(d=default) if default else ''
     prompt = '    {d}{a}: '.format(d=description, a=additional)
-    user_input = raw_input(prompt)
+    user_input = input_(prompt)
     return user_input
 
 
 def print_table(data, **kwargs):
-    """Print data in table format. Can either take a list of tuples or a
+    """
-    dictionary, which will be converted to a list of tuples."""
+    Print data in table format. Can either take a list of tuples or a
-
+    dictionary, which will be converted to a list of tuples.
+    """
     if type(data) == dict:
         data = list(data.items())
 
@@ -204,15 +155,15 @@ def print_table(data, **kwargs):
 
 
 def print_markdown(data, **kwargs):
-    """Print listed data in GitHub-flavoured Markdown format so it can be
+    """
+    Print listed data in GitHub-flavoured Markdown format so it can be
     copy-pasted into issues. Can either take a list of tuples or a dictionary,
-    which will be converted to a list of tuples."""
+    which will be converted to a list of tuples.
-
+    """
     def excl_value(value):
-        # don't print value if it contains absolute path of directory
+        # don't print value if it contains absolute path of directory (i.e.
-        # (i.e. personal info that shouldn't need to be shared)
+        # personal info). Other conditions can be included here if necessary.
-        # other conditions can be included here if necessary
+        if unicode_(Path(__file__).parent) in value:
-        if str(pathlib.Path(__file__).parent) in value:
             return True
 
     if type(data) == dict:
@@ -225,16 +176,16 @@ def print_markdown(data, **kwargs):
 
     if 'title' in kwargs and kwargs['title']:
         print(tpl_title.format(msg=kwargs['title']))
-
     print(tpl_msg.format(msg=markdown))
 
 
 def print_msg(*text, **kwargs):
-    """Print formatted message. Each positional argument is rendered as newline-
+    """
+    Print formatted message. Each positional argument is rendered as newline-
     separated paragraph. If kwarg 'title' exist, title is printed above the text
     and highlighted (using ANSI escape sequences manually to avoid unnecessary
-    dependency)."""
+    dependency).
-
+    """
     message = '\n\n'.join([_wrap_text(t) for t in text])
     tpl_msg = '\n{msg}\n'
     tpl_title = '\n\033[93m{msg}\033[0m'
@@ -246,9 +197,10 @@ def print_msg(*text, **kwargs):
 
 
 def _wrap_text(text):
-    """Wrap text at given width using textwrap module. Indent should consist of
+    """
-    spaces. Its length is deducted from wrap width to ensure exact wrapping."""
+    Wrap text at given width using textwrap module. Indent should consist of
-
+    spaces. Its length is deducted from wrap width to ensure exact wrapping.
+    """
     wrap_max = 80
     indent = '    '
     wrap_width = wrap_max - len(indent)
@@ -258,10 +210,11 @@ def _wrap_text(text):
 
 
 def sys_exit(*messages, **kwargs):
-    """Performs SystemExit. For modules used from the command line, like
+    """
+    Performs SystemExit. For modules used from the command line, like
     download and link. To print message, use the same arguments as for
-    print_msg()."""
+    print_msg().
-
+    """
     if messages:
         print_msg(*messages, **kwargs)
     sys.exit(0)

spacy/vocab.pyx

@@ -1,41 +1,29 @@
+# coding: utf8
 from __future__ import unicode_literals
 
+import bz2
+import ujson
+import re
+
 from libc.string cimport memset
 from libc.stdint cimport int32_t
 from libc.math cimport sqrt
-
+from cymem.cymem cimport Address
-from pathlib import Path
-import bz2
-import ujson as json
-import re
-
-try:
-    import cPickle as pickle
-except ImportError:
-    import pickle
-
 from .lexeme cimport EMPTY_LEXEME
 from .lexeme cimport Lexeme
 from .strings cimport hash_string
 from .typedefs cimport attr_t
 from .cfile cimport CFile, StringCFile
-from .lemmatizer import Lemmatizer
-from .attrs import intify_attrs
 from .tokens.token cimport Token
-
-from . import attrs
-from . import symbols
-
-from cymem.cymem cimport Address
 from .serialize.packer cimport Packer
 from .attrs cimport PROB, LANG
+
+from .compat import copy_reg, pickle
+from .lemmatizer import Lemmatizer
+from .attrs import intify_attrs
 from . import util
-
+from . import attrs
-
+from . import symbols
-try:
-    import copy_reg
-except ImportError:
-    import copyreg as copy_reg
 
 
 DEF MAX_VEC_SIZE = 100000
@@ -48,8 +36,9 @@ EMPTY_LEXEME.vector = EMPTY_VEC
 
 
 cdef class Vocab:
-    '''A map container for a language's LexemeC structs.
+    """
-    '''
+    A map container for a language's LexemeC structs.
+    """
     @classmethod
     def load(cls, path, lex_attr_getters=None, lemmatizer=True,
              tag_map=True, serializer_freqs=True, oov_prob=True, **deprecated_kwargs):
@@ -72,8 +61,7 @@ cdef class Vocab:
         Returns:
             Vocab: The newly constructed vocab object.
         """
-        if isinstance(path, basestring):
+        path = util.ensure_path(path)
-            path = Path(path)
         util.check_renamed_kwargs({'get_lex_attr': 'lex_attr_getters'}, deprecated_kwargs)
         if 'vectors' in deprecated_kwargs:
             raise AttributeError(
@@ -81,7 +69,7 @@ cdef class Vocab:
                 "Install vectors after loading.")
         if tag_map is True and (path / 'vocab' / 'tag_map.json').exists():
             with (path / 'vocab' / 'tag_map.json').open('r', encoding='utf8') as file_:
-                tag_map = json.load(file_)
+                tag_map = ujson.load(file_)
         elif tag_map is True:
             tag_map = None
         if lex_attr_getters is not None \
@@ -94,12 +82,12 @@ cdef class Vocab:
             lemmatizer = Lemmatizer.load(path)
         if serializer_freqs is True and (path / 'vocab' / 'serializer.json').exists():
             with (path / 'vocab' / 'serializer.json').open('r', encoding='utf8') as file_:
-                serializer_freqs = json.load(file_)
+                serializer_freqs = ujson.load(file_)
         else:
             serializer_freqs = None
 
         with (path / 'vocab' / 'strings.json').open('r', encoding='utf8') as file_:
-            strings_list = json.load(file_)
+            strings_list = ujson.load(file_)
         cdef Vocab self = cls(lex_attr_getters=lex_attr_getters, tag_map=tag_map,
                               lemmatizer=lemmatizer, serializer_freqs=serializer_freqs,
                               strings=strings_list)
@@ -108,7 +96,8 @@ cdef class Vocab:
 
     def __init__(self, lex_attr_getters=None, tag_map=None, lemmatizer=None,
             serializer_freqs=None, strings=tuple(), **deprecated_kwargs):
-        '''Create the vocabulary.
+        """
+        Create the vocabulary.
 
         lex_attr_getters (dict):
             A dictionary mapping attribute IDs to functions to compute them.
@@ -123,7 +112,7 @@ cdef class Vocab:
 
         Returns:
             Vocab: The newly constructed vocab object.
-        '''
+        """
         util.check_renamed_kwargs({'get_lex_attr': 'lex_attr_getters'}, deprecated_kwargs)
 
         lex_attr_getters = lex_attr_getters if lex_attr_getters is not None else {}
@@ -172,17 +161,19 @@ cdef class Vocab:
             return langfunc('_') if langfunc else ''
 
     def __len__(self):
-        """The current number of lexemes stored."""
+        """
+        The current number of lexemes stored.
+        """
         return self.length
 
     def resize_vectors(self, int new_size):
-        '''
+        """
         Set vectors_length to a new size, and allocate more memory for the Lexeme
         vectors if necessary. The memory will be zeroed.
 
         Arguments:
             new_size (int): The new size of the vectors.
-        '''
+        """
         cdef hash_t key
         cdef size_t addr
         if new_size > self.vectors_length:
@@ -193,7 +184,8 @@ cdef class Vocab:
         self.vectors_length = new_size
 
     def add_flag(self, flag_getter, int flag_id=-1):
-        '''Set a new boolean flag to words in the vocabulary.
+        """
+        Set a new boolean flag to words in the vocabulary.
 
         The flag_setter function will be called over the words currently in the
         vocab, and then applied to new words as they occur. You'll then be able
@@ -213,7 +205,7 @@ cdef class Vocab:
 
         Returns:
             flag_id (int): The integer ID by which the flag value can be checked.
-        '''
+        """
         if flag_id == -1:
             for bit in range(1, 64):
                 if bit not in self.lex_attr_getters:
@@ -234,9 +226,11 @@ cdef class Vocab:
         return flag_id
 
     cdef const LexemeC* get(self, Pool mem, unicode string) except NULL:
-        '''Get a pointer to a LexemeC from the lexicon, creating a new Lexeme
+        """
+        Get a pointer to a LexemeC from the lexicon, creating a new Lexeme
         if necessary, using memory acquired from the given pool.  If the pool
-        is the lexicon's own memory, the lexeme is saved in the lexicon.'''
+        is the lexicon's own memory, the lexeme is saved in the lexicon.
+        """
         if string == u'':
             return &EMPTY_LEXEME
         cdef LexemeC* lex
@@ -252,9 +246,11 @@ cdef class Vocab:
             return self._new_lexeme(mem, string)
 
     cdef const LexemeC* get_by_orth(self, Pool mem, attr_t orth) except NULL:
-        '''Get a pointer to a LexemeC from the lexicon, creating a new Lexeme
+        """
+        Get a pointer to a LexemeC from the lexicon, creating a new Lexeme
         if necessary, using memory acquired from the given pool.  If the pool
-        is the lexicon's own memory, the lexeme is saved in the lexicon.'''
+        is the lexicon's own memory, the lexeme is saved in the lexicon.
+        """
         if orth == 0:
             return &EMPTY_LEXEME
         cdef LexemeC* lex
@@ -297,30 +293,33 @@ cdef class Vocab:
         self.length += 1
 
     def __contains__(self, unicode string):
-        '''Check whether the string has an entry in the vocabulary.
+        """
+        Check whether the string has an entry in the vocabulary.
 
         Arguments:
             string (unicode): The ID string.
 
         Returns:
             bool Whether the string has an entry in the vocabulary.
-        '''
+        """
         key = hash_string(string)
         lex = self._by_hash.get(key)
         return lex is not NULL
 
     def __iter__(self):
-        '''Iterate over the lexemes in the vocabulary.
+        """
+        Iterate over the lexemes in the vocabulary.
 
         Yields: Lexeme An entry in the vocabulary.
-        '''
+        """
         cdef attr_t orth
         cdef size_t addr
         for orth, addr in self._by_orth.items():
             yield Lexeme(self, orth)
 
     def __getitem__(self,  id_or_string):
-        '''Retrieve a lexeme, given an int ID or a unicode string.  If a previously
+        """
+        Retrieve a lexeme, given an int ID or a unicode string.  If a previously
         unseen unicode string is given, a new lexeme is created and stored.
 
         Arguments:
@@ -332,7 +331,7 @@ cdef class Vocab:
 
         Returns:
             lexeme (Lexeme): The lexeme indicated by the given ID.
-        '''
+        """
         cdef attr_t orth
         if type(id_or_string) == unicode:
             orth = self.strings[id_or_string]
@@ -355,7 +354,8 @@ cdef class Vocab:
         return tokens
 
     def dump(self, loc=None):
-        """Save the lexemes binary data to the given location, or
+        """
+        Save the lexemes binary data to the given location, or
         return a byte-string with the data if loc is None.
 
         Arguments:
@@ -392,14 +392,15 @@ cdef class Vocab:
             return fp.string_data()
 
     def load_lexemes(self, loc):
-        '''Load the binary vocabulary data from the given location.
+        """
+        Load the binary vocabulary data from the given location.
 
         Arguments:
             loc (Path): The path to load from.
 
         Returns:
             None
-        '''
+        """
         fp = CFile(loc, 'rb',
                 on_open_error=lambda: IOError('LexemeCs file not found at %s' % loc))
         cdef LexemeC* lexeme = NULL
@@ -440,8 +441,9 @@ cdef class Vocab:
         fp.close()
 
     def _deserialize_lexemes(self, CFile fp):
-        '''Load the binary vocabulary data from the given CFile.
+        """
-        '''
+        Load the binary vocabulary data from the given CFile.
+        """
         cdef LexemeC* lexeme = NULL
         cdef hash_t key
         cdef unicode py_str
@@ -494,13 +496,14 @@ cdef class Vocab:
         fp.close()
 
     def dump_vectors(self, out_loc):
-        '''Save the word vectors to a binary file.
+        """
+        Save the word vectors to a binary file.
 
         Arguments:
             loc (Path): The path to save to.
         Returns:
             None
-        '''
+        """
         cdef int32_t vec_len = self.vectors_length
         cdef int32_t word_len
         cdef bytes word_str
@@ -522,7 +525,8 @@ cdef class Vocab:
         out_file.close()
 
     def load_vectors(self, file_):
-        """Load vectors from a text-based file.
+        """
+        Load vectors from a text-based file.
 
         Arguments:
             file_ (buffer): The file to read from. Entries should be separated by newlines,
@@ -561,7 +565,8 @@ cdef class Vocab:
         return vec_len
 
     def load_vectors_from_bin_loc(self, loc):
-        """Load vectors from the location of a binary file.
+        """
+        Load vectors from the location of a binary file.
 
         Arguments:
             loc (unicode): The path of the binary file to load from.

website/_harp.json

@@ -12,7 +12,7 @@
         "COMPANY_URL": "https://explosion.ai",
         "DEMOS_URL": "https://demos.explosion.ai",
 
-        "SPACY_VERSION": "1.7",
+        "SPACY_VERSION": "1.8",
         "LATEST_NEWS": {
             "url": "https://survey.spacy.io/",
             "title": "Take the spaCy user survey and help us improve the library!"

website/assets/img/graphics.svg

@@ -2,9 +2,11 @@
     <defs>
         <symbol id="usersurvey" viewBox="0 0 200 111">
             <title>spaCy user survey 2017</title>
-            <path fill="#DDD" d="M183.3 89.2l-164.6-40-1-29.2 164.6 40M3.8 106.8l41.6-1.4-1-29.2-41.6 1.4L13.2 92"/>
+            <path fill="#ddd" d="M183.3 89.2l-164.6-40-1-29.2 164.6 40M3.8 106.8l41.6-1.4-1-29.2-41.6 1.4L13.2 92"/>
-            <path fill="#DDD" d="M196.6 2L155 3.4l1 29.2 41.6-1.4L187.2 17"/>
+            <path fill="#a3cad3" d="M45.4 105.4L19.6 94.6l25.4-1"/>
-            <path fill="#FFF" d="M17.6 19.4l163-5.6 1 29.2-163 5.6zM19.2 65.6l163-5.6 1 29.2-163 5.6z"/>
+            <path fill="#ddd" d="M196.6 2L155 3.4l1 29.2 41.6-1.4L187.2 17"/>
+            <path fill="#a3cad3" d="M155 3.4l25.8 10.8-25.4 1"/>
+            <path fill="#fff" d="M17.6 19.4l163-5.6 1 29.2-163 5.6zM19.2 65.6l163-5.6 1 29.2-163 5.6z"/>
             <path fill="#008EBC" d="M56.8 29h-3.6v-2.4l10-.4.2 2.5h-3.6l.4 10.8h-3L56.8 29zM71 36l-4 .2-.6 3.2h-3L67 26.2h3.6l4.6 13-3.2.2-1-3zm-.6-2.3l-.4-1.2-1.2-4.2-1 4.3-.3 1.2h3zM76 25.8h3l.3 5.3 4-5.4h3.2l-3.8 5.3 5 7.7h-3.3L81 33.4l-1.5 2V39h-3l-.4-13.2zM88.5 25.4l8.3-.3v2.6l-5.2.2v2.6l4.6-.2v2.5L92 33v3l5.6-.3v2.5l-8.4.3-.5-13zM106.4 27.3h-3.6V25l10-.5.2 2.5h-3.6l.4 10.8h-3l-.4-10.5zM115 24.5h3v5l4.7-.2-.2-5 3-.2.5 13.3h-3l-.2-5.4h-4.6l.2 5.6h-3l-.5-13zM128.5 24l8.3-.3v2.5l-5.2.2V29l4.6-.2v2.5l-4.4.2v3l5.6-.2v2.5l-8.4.3-.5-13z"/>
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         </symbol>

website/docs/api/language.jade

@@ -55,14 +55,14 @@ p Create or load the pipeline.
 
 +table(["Name", "Type", "Description"])
     +row
-        +cell #[code **kwrags]
+        +cell #[code **overrides]
         +cell -
         +cell Keyword arguments indicating which defaults to override.
 
     +footrow
         +cell return
         +cell #[code Language]
-        +cell #[code self]
+        +cell The newly constructed object.
 
 +h(2, "call") Language.__call__
     +tag method
@@ -136,3 +136,19 @@ p
         +cell yield
         +cell #[code Doc]
         +cell Containers for accessing the linguistic annotations.
+
++h(2, "save_to_directory") Language.save_to_directory
+    +tag method
+
+p Save the #[code Vocab], #[code StringStore] and pipeline to a directory.
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code path]
+        +cell string or pathlib path
+        +cell Path to save the model.
+
+    +footrow
+        +cell return
+        +cell #[code None]
+        +cell -

website/docs/usage/_data.json

@@ -20,8 +20,10 @@
             "Word vectors": "word-vectors-similarities",
             "Deep learning": "deep-learning",
             "Custom tokenization": "customizing-tokenizer",
+            "Adding languages": "adding-languages",
             "Training": "training",
-            "Adding languages": "adding-languages"
+            "Training NER": "training-ner",
+            "Saving & loading": "saving-loading"
         },
         "Examples": {
             "Tutorials": "tutorials",
@@ -101,11 +103,21 @@
 
     "customizing-tokenizer": {
         "title": "Customizing the tokenizer",
-        "next": "training"
+        "next": "adding-languages"
     },
 
     "training": {
-        "title": "Training the tagger, parser and entity recognizer"
+        "title": "Training spaCy's statistical models",
+        "next": "saving-loading"
+    },
+
+    "training-ner": {
+        "title": "Training the Named Entity Recognizer",
+        "next": "saving-loading"
+    },
+
+    "saving-loading": {
+        "title": "Saving and loading models"
     },
 
     "pos-tagging": {
@@ -356,6 +368,18 @@
         },
 
         "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",

website/docs/usage/adding-languages.jade

@@ -63,14 +63,16 @@ p
             tag_map = TAG_MAP
             stop_words = STOP_WORDS
 
-p Additionally, the new #[code Language] class needs to be registered in #[+src(gh("spaCy", "spacy/__init__.py")) spacy/__init__.py] using the #[code set_lang_class()] function, so that you can use #[code spacy.load()].
+p
+    |  Additionally, the new #[code Language] class needs to be added to the
+    |  list of available languages in #[+src(gh("spaCy", "spacy/__init__.py")) __init__.py].
+    |  The languages are then registered using the #[code set_lang_class()] function.
 
 +code("spacy/__init__.py").
     from . import en
     from . import xx
 
-    set_lang_class(en.English.lang, en.English)
+    _languages = (en.English, ..., xx.Xxxxx)
-    set_lang_class(xx.Xxxxx.lang, xx.Xxxxx)
 
 p You'll also need to list the new package in #[+src(gh("spaCy", "spacy/setup.py")) setup.py]:
 
@@ -398,11 +400,12 @@ p
     |  vectors files, you can use the
     |  #[+src(gh("spacy-dev-resources", "training/init.py")) init.py]
     |  script from our
-    |  #[+a(gh("spacy-dev-resources")) developer resources] to create a
+    |  #[+a(gh("spacy-dev-resources")) developer resources], or use the new
-    |  spaCy data directory:
+    |  #[+a("/docs/usage/cli#model") #[code model] command] to create a data
+    |  directory:
 
 +code(false, "bash").
-    python training/init.py xx your_data_directory/ my_data/word_freqs.txt my_data/clusters.txt my_data/word_vectors.bz2
+    python -m spacy model [lang] [model_dir] [freqs_data] [clusters_data] [vectors_data]
 
 +aside-code("your_data_directory", "yaml").
     ├── vocab/
@@ -421,17 +424,14 @@ p
 
 p
     |  This creates a spaCy data directory with a vocabulary model, ready to be
-    |  loaded. By default, the
+    |  loaded. By default, the command expects to be able to find your language
-    |  #[+src(gh("spacy-dev-resources", "training/init.py")) init.py]
+    |  class using #[code spacy.util.get_lang_class(lang_id)].
-    |  script expects to be able to find your language class using
-    |  #[code spacy.util.get_lang_class(lang_id)]. You can edit the script to
-    |  help it find your language class if necessary.
 
 +h(3, "word-frequencies") Word frequencies
 
 p
-    |  The #[+src(gh("spacy-dev-resources", "training/init.py")) init.py]
+    |  The #[+a("/docs/usage/cli#model") #[code model] command] expects a
-    |  script expects a tab-separated word frequencies file with three columns:
+    |  tab-separated word frequencies file with three columns:
 
 +list("numbers")
     +item The number of times the word occurred in your language sample.

website/docs/usage/cli.jade

@@ -145,7 +145,9 @@ p
 +h(2, "model") Model
     +tag experimental
 
-p Initialise a new model and its data directory.
+p
+    |  Initialise a new model and its data directory. For more info on this, see
+    |  the documentation on #[+a("/docs/usage/adding-languages") adding languages].
 
 +code(false, "bash").
     python -m spacy model [lang] [model_dir] [freqs_data] [clusters_data] [vectors_data]
@@ -246,15 +248,17 @@ p
     +tag experimental
 
 p
-    |  Generate a #[+a("/docs/usage/models#own-models") model Python package]
+    |  Generate a #[+a("/docs/usage/saving-loading#generating") model Python package]
-    |  from an existing model data directory. All data files are copied over,
+    |  from an existing model data directory. All data files are copied over.
-    |  and the meta data can be entered directly from the command line. While
+    |  If the path to a meta.json is supplied, or a meta.json is found in the
-    |  this feature is still experimental, the required file templates are
+    |  input directory, this file is used. Otherwise, the data can be entered
-    |  downloaded from #[+src(gh("spacy-dev-resources", "templates/model")) GitHub].
+    |  directly from the command line. While this feature is still experimental,
-    |  This means you need to be connected to the internet to use this command.
+    |  the required file templates are downloaded from
+    |  #[+src(gh("spacy-dev-resources", "templates/model")) GitHub]. This means
+    |  you need to be connected to the internet to use this command.
 
 +code(false, "bash").
-    python -m spacy package [input_dir] [output_dir] [--force]
+    python -m spacy package [input_dir] [output_dir] [--meta] [--force]
 
 +table(["Argument", "Type", "Description"])
     +row
@@ -267,6 +271,11 @@ p
         +cell positional
         +cell Directory to create package folder in.
 
+    +row
+        +cell #[code meta]
+        +cell option
+        +cell Path to meta.json file (optional).
+
     +row
         +cell #[code --force], #[code -f]
         +cell flag

website/docs/usage/entity-recognition.jade

@@ -57,7 +57,7 @@ p
     doc.ents = [Span(doc, 0, 1, label=doc.vocab.strings['GPE'])]
     assert doc[0].ent_type_ == 'GPE'
     doc.ents = []
-    doc.ents = [(u'LondonCity', doc.vocab.strings['GPE']), 0, 1)]
+    doc.ents = [(u'LondonCity', doc.vocab.strings['GPE'], 0, 1)]
 
 p
     |  The value you assign should be a sequence, the values of which

website/docs/usage/lightning-tour.jade

@@ -137,7 +137,7 @@ p
         return word.ent_type != 0
 
     def count_parent_verb_by_person(docs):
-        counts = defaultdict(defaultdict(int))
+        counts = defaultdict(lambda: defaultdict(int))
         for doc in docs:
             for ent in doc.ents:
                 if ent.label_ == 'PERSON' and ent.root.head.pos == VERB:

website/docs/usage/models.jade

@@ -235,62 +235,13 @@ p
 
 p
     |  If you've trained your own model, for example for
-    |  #[+a("/docs/usage/adding-languages") additional languages], you can
+    |  #[+a("/docs/usage/adding-languages") additional languages] or
-    |  create a shortuct link for it by pointing #[code spacy.link] to the
+    |  #[+a("/docs/usage/train-ner") custom named entities], you can save its
-    |  model's data directory. To allow your model to be downloaded and
+    |  state using the #[code Language.save_to_directory()] method. To make the
-    |  installed via pip, you'll also need to generate a package for it. You can
+    |  model more convenient to deploy, we recommend wrapping it as a Python
-    |  do this manually, or via the new
+    |  package.
-    |  #[+a("/docs/usage/cli#package") #[code spacy package] command] that will
-    |  create all required files, and walk you through generating the meta data.
 
-
++infobox("Saving and loading models")
-+infobox("Important note")
+    |  For more information and a detailed guide on how to package your model,
-    |  The model packages are #[strong not suitable] for the public
+    |  see the documentation on
-    |  #[+a("https://pypi.python.org") pypi.python.org] directory, which is not
+    |  #[+a("/docs/usage/saving-loading") saving and loading models].
-    |  designed for binary data and files over 50 MB. However, if your company
-    |  is running an internal installation of pypi, publishing your models on
-    |  there can be a convenient solution to share them with your team.
-
-p The model directory should look like this:
-
-+code("Directory structure", "yaml").
-    └── /
-        ├── MANIFEST.in                   # to include meta.json
-        ├── meta.json                     # model meta data
-        ├── setup.py                      # setup file for pip installation
-        └── en_core_web_md                # model directory
-            ├── __init__.py               # init for pip installation
-            └── en_core_web_md-1.2.0      # model data
-
-p
-    |  You can find templates for all files in our
-    |  #[+a(gh("spacy-dev-resouces", "templates/model")) spaCy dev resources].
-    |  Unless you want to customise installation and loading, the only file
-    |  you'll need to modify is #[code meta.json], which includes the model's
-    |  meta data. It will later be copied into the package and data directory.
-
-+code("meta.json", "json").
-    {
-        "name": "core_web_md",
-        "lang": "en",
-        "version": "1.2.0",
-        "spacy_version": "1.7.0",
-        "description": "English model for spaCy",
-        "author": "Explosion AI",
-        "email": "contact@explosion.ai",
-        "license": "MIT"
-    }
-
-p
-    |  Keep in mind that the directories need to be named according to the
-    |  naming conventions. The #[code lang] setting is also used to create the
-    |  respective #[code Language] class in spaCy, which will later be returned
-    |  by the model's #[code load()] method.
-
-p
-    |  To generate the package, run the following command from within the
-    |  directory. This will create a #[code .tar.gz] archive in a directory
-    |  #[code /dist].
-
-+code(false, "bash").
-    python setup.py sdist

website/docs/usage/saving-loading.jade

@@ -0,0 +1,109 @@
+include ../../_includes/_mixins
+
+p
+    |  After training your model, you'll usually want to save its state, and load
+    |  it back later. You can do this with the
+    |  #[+api("language#save_to_directory") #[code Language.save_to_directory()]]
+    |  method:
+
++code.
+    nlp.save_to_directory('/home/me/data/en_example_model')
+
+p
+    |  The directory will be created if it doesn't exist, and the whole pipeline
+    |  will be written out. To make the model more convenient to deploy, we
+    |  recommend wrapping it as a Python package.
+
++h(2, "generating") Generating a model package
+
++infobox("Important note")
+    |  The model packages are #[strong not suitable] for the public
+    |  #[+a("https://pypi.python.org") pypi.python.org] directory, which is not
+    |  designed for binary data and files over 50 MB. However, if your company
+    |  is running an internal installation of pypi, publishing your models on
+    |  there can be a convenient solution to share them with your team.
+
+p
+    |  spaCy comes with a handy CLI command that will create all required files,
+    |  and walk you through generating the meta data. You can also create the
+    |  meta.json manually and place it in the model data directory, or supply a
+    |  path to it using the #[code --meta] flag. For more info on this, see the
+    |  #[+a("/docs/usage/cli/#package") #[code package] command] documentation.
+
++aside-code("meta.json", "json").
+    {
+        "name": "example_model",
+        "lang": "en",
+        "version": "1.0.0",
+        "spacy_version": ">=1.7.0,<2.0.0",
+        "description": "Example model for spaCy",
+        "author": "You",
+        "email": "you@example.com",
+        "license": "CC BY-SA 3.0"
+    }
+
++code(false, "bash").
+    python -m spacy package /home/me/data/en_example_model /home/me/my_models
+
+p This command will create a model package directory that should look like this:
+
++code("Directory structure", "yaml").
+    └── /
+        ├── MANIFEST.in                   # to include meta.json
+        ├── meta.json                     # model meta data
+        ├── setup.py                      # setup file for pip installation
+        └── en_example_model              # model directory
+            ├── __init__.py               # init for pip installation
+            └── en_example_model-1.0.0    # model data
+
+p
+    |  You can also find templates for all files in our
+    |  #[+a(gh("spacy-dev-resouces", "templates/model")) spaCy dev resources].
+    |  If you're creating the package manually, keep in mind that the directories
+    |  need to be named according to the naming conventions of
+    |  #[code [language]_[type]] and #[code [language]_[type]-[version]]. The
+    |  #[code lang] setting in the meta.json is also used to create the
+    |  respective #[code Language] class in spaCy, which will later be returned
+    |  by the model's #[code load()] method.
+
++h(2, "building") Building a model package
+
+p
+    |  To build the package, run the following command from within the
+    |  directory. This will create a #[code .tar.gz] archive in a directory
+    |  #[code /dist].
+
++code(false, "bash").
+    python setup.py sdist
+
+p
+    |  For more information on building Python packages, see the
+    |  #[+a("https://setuptools.readthedocs.io/en/latest/") Python Setuptools documentation].
+
+
++h(2, "loading") Loading a model package
+
+p
+    |  Model packages can be installed by pointing pip to the model's
+    |  #[code .tar.gz] archive:
+
++code(false, "bash").
+    pip install /path/to/en_example_model-1.0.0.tar.gz
+
+p You'll then be able to load the model as follows:
+
++code.
+    import en_example_model
+    nlp = en_example_model.load()
+
+p
+    |  To load the model via #[code spacy.load()], you can also
+    |  create a #[+a("/docs/usage/models#usage") shortcut link] that maps the
+    |  package name to a custom model name of your choice:
+
++code(false, "bash").
+    python -m spacy link en_example_model example
+
++code.
+    import spacy
+    nlp = spacy.load('example')

website/docs/usage/training-ner.jade

@@ -0,0 +1,174 @@
+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 spacy.gold.GoldParse]], with the entity labels
+    |  you want to learn. You will then pass this instance to the
+    |  #[+api("entityrecognizer#update") #[code EntityRecognizer.update()]]
+    |  method. For example:
+
++code.
+    import spacy
+    from spacy.gold import GoldParse
+
+    nlp = spacy.load('en')
+    doc = nlp.make_doc(u'Facebook released React in 2014')
+    gold = GoldParse(doc, entities=['U-ORG', 'O', 'U-TECHNOLOGY', 'O', 'U-DATE'])
+    nlp.entity.update(doc, gold)
+
+p
+    |  You'll usually need to provide many examples to meaningfully improve the
+    |  system — a few hundred is a good start, although more is better. 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, "adding") Adding a new entity type
+
+p
+    |  You can add new entity types to an existing model. Let's say we want to
+    |  recognise the category #[code TECHNOLOGY]. The new category will include
+    |  programming languages, frameworks and platforms. First, we need to
+    |  register the new entity type:
+
++code.
+    nlp.entity.add_label('TECHNOLOGY')
+
+p
+    |  Next, iterate over your examples, calling #[code entity.update()]. As
+    |  above, we want to avoid iterating over only a small number of sentences.
+    |  A useful compromise is to run the model over a number of plain-text
+    |  sentences, and pass the entities to #[code GoldParse], as "true"
+    |  annotations. This encourages the optimizer to find a solution that
+    |  predicts the new category with minimal difference from the previous
+    |  output.
+
++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 #[code Language.save_to_directory()]
+    |  method:
+
++code.
+    nlp.save_to_directory('/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 #[+a("/docs/usage/cli#package") CLI command]
+    |  to create all required files and directories.
+
++code(false, "bash").
+    python -m 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") saving and loading models].
+
+p
+    |  After you've generated and installed the package, you'll be able to
+    |  load the model as follows:
+
++code.
+    import en_technology
+    nlp = en_technology.load()
+
++h(2, "example") Example: Adding and training an #[code ANIMAL] entity
+
+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 —
+    |  #[strong a few hundred] would be a good start. You will also likely need
+    |  to mix in #[strong 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
+    |  For the full, runnable script of this example, see
+    |  #[+src(gh("spacy", "examples/training/train_new_entity_type.py")) train_new_entity_type.py].
+
++code("Training the entity recognizer").
+    import spacy
+    from spacy.pipeline import EntityRecognizer
+    from spacy.gold import GoldParse
+    from spacy.tagger import Tagger
+    import random
+
+    model_name = 'en'
+    entity_label = 'ANIMAL'
+    output_directory = '/path/to/model'
+    train_data = [
+        ("Horses are too tall and they pretend to care about your feelings",
+        [(0, 6, 'ANIMAL')]),
+        ("horses are too tall and they pretend to care about your feelings",
+        [(0, 6, 'ANIMAL')]),
+        ("horses pretend to care about your feelings",
+        [(0, 6, 'ANIMAL')]),
+        ("they pretend to care about your feelings, those horses",
+        [(48, 54, 'ANIMAL')])
+    ]
+
+    nlp = spacy.load(model_name)
+    nlp.entity.add_label(entity_label)
+    ner = train_ner(nlp, train_data, output_directory)
+
+    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]
+
+        for itn in range(20):
+            random.shuffle(train_data)
+            for raw_text, entity_offsets in train_data:
+                gold = GoldParse(doc, entities=entity_offsets)
+                doc = nlp.make_doc(raw_text)
+                nlp.tagger(doc)
+                loss = nlp.entity.update(doc, gold)
+        nlp.end_training()
+        nlp.save_to_directory(output_dir)
+
+p
+    +button(gh("spaCy", "examples/training/train_new_entity_type.py"), false, "secondary") Full example
+
+p
+    |  The actual training is performed by looping over the examples, and
+    |  calling #[code nlp.entity.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 #[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.
+
+p
+    |  After training your model, you can
+    |  #[+a("/docs/usage/saving-loading") save it to a directory]. We recommend wrapping
+    |  models as Python packages, for ease of deployment.

website/docs/usage/training.jade

@@ -1,13 +1,10 @@
 include ../../_includes/_mixins
 
 p
-    |  This tutorial describes how to train new statistical models for spaCy's
+    |  This workflow 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
-p
+    |  #[+a("/docs/usage/saving-loading") save and load] it.
-    |  I'll start with some quick code examples, that describe how to train
-    |  each model. I'll then provide a bit of background about the algorithms,
-    |  and explain how the data and feature templates work.
 
 +h(2, "train-pos-tagger") Training the part-of-speech tagger
 
@@ -48,7 +45,21 @@ p
 p
     +button(gh("spaCy", "examples/training/train_ner.py"), false, "secondary") Full example
 
-+h(2, "train-entity") Training the dependency parser
++h(2, "extend-entity") Extending the named entity recognizer
+
+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.
+
+p.o-inline-list
+    +button(gh("spaCy", "examples/training/train_new_entity_type.py"), true, "secondary") Full example
+    +button("/docs/usage/training-ner", false, "secondary") Usage Workflow
+
++h(2, "train-dependency") Training the dependency parser
 
 +code.
     from spacy.vocab import Vocab
@@ -67,7 +78,7 @@ p
 p
     +button(gh("spaCy", "examples/training/train_parser.py"), false, "secondary") Full example
 
-+h(2, 'feature-templates') Customizing the feature extraction
++h(2, "feature-templates") Customizing the feature extraction
 
 p
     |  spaCy currently uses linear models for the tagger, parser and entity