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106
.github/contributors/luvogels.md vendored Normal file
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@ -0,0 +1,106 @@
# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Leif Uwe Vogelsang |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 4/27/2017 |
| GitHub username | luvogels |
| Website (optional) | |

2
CONTRIBUTING.md
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@ -40,7 +40,7 @@ To distinguish issues that are opened by us, the maintainers, we usually add a
| [`performance`](https://github.com/explosion/spaCy/labels/performance) | Accuracy, speed and memory use problems |
| [`tests`](https://github.com/explosion/spaCy/labels/tests) | Missing or incorrect [tests](spacy/tests) |
| [`docs`](https://github.com/explosion/spaCy/labels/docs), [`examples`](https://github.com/explosion/spaCy/labels/examples) | Issues related to the [documentation](https://spacy.io/docs) and [examples](spacy/examples) |
| [`models`](https://github.com/explosion/spaCy/labels/models), [`english`](https://github.com/explosion/spaCy/labels/english), [`german`](https://github.com/explosion/spaCy/labels/german) | Issues related to the specific [models](https://github.com/explosion/spacy-models), languages and data |
| [`models`](https://github.com/explosion/spaCy/labels/models), `language / [name]` | Issues related to the specific [models](https://github.com/explosion/spacy-models), languages and data |
| [`linux`](https://github.com/explosion/spaCy/labels/linux), [`osx`](https://github.com/explosion/spaCy/labels/osx), [`windows`](https://github.com/explosion/spaCy/labels/windows) | Issues related to the specific operating systems |
| [`pip`](https://github.com/explosion/spaCy/labels/pip), [`conda`](https://github.com/explosion/spaCy/labels/conda) | Issues related to the specific package managers |
| [`duplicate`](https://github.com/explosion/spaCy/labels/duplicate) | Duplicates, i.e. issues that have been reported before |

1
CONTRIBUTORS.md
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@ -29,6 +29,7 @@ This is a list of everyone who has made significant contributions to spaCy, in a
* Juan Miguel Cejuela, [@juanmirocks](https://github.com/juanmirocks)
* Kendrick Tan, [@kendricktan](https://github.com/kendricktan)
* Kyle P. Johnson, [@kylepjohnson](https://github.com/kylepjohnson)
* Leif Uwe Vogelsang, [@luvogels](https://github.com/luvogels)
* Liling Tan, [@alvations](https://github.com/alvations)
* Magnus Burton, [@magnusburton](https://github.com/magnusburton)
* Mark Amery, [@ExplodingCabbage](https://github.com/ExplodingCabbage)

10
README.rst
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@ -4,9 +4,9 @@ spaCy: Industrial-strength NLP
spaCy is a library for advanced natural language processing in Python and
Cython. spaCy is built on the very latest research, but it isn't researchware.
It was designed from day one to be used in real products. spaCy currently supports
English and German, as well as tokenization for Chinese, Spanish, Italian, French,
Portuguese, Dutch, Swedish, Finnish, Hungarian, Bengali and Hebrew. It's commercial
open-source software, released under the MIT license.
English, German and French, as well as tokenization for Chinese, Spanish, Italian,
Portuguese, Dutch, Swedish, Finnish, Norwegian, Hungarian, Bengali and Hebrew. It's
commercial open-source software, released under the MIT license.
📊 **Help us improve the library!** `Take the spaCy user survey <https://survey.spacy.io>`_.
@ -320,6 +320,8 @@ and ``--model`` are optional and enable additional tests:
=========== ============== ===========
Version Date Description
=========== ============== ===========
`v1.8.2`_ ``2017-04-26`` French model and small improvements
`v1.8.1`_ ``2017-04-23`` Saving, loading and training bug fixes
`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
@ -351,6 +353,8 @@ Version Date Description
`v0.93`_ ``2015-09-22`` Bug fixes to word vectors
=========== ============== ===========
.. _v1.8.2: https://github.com/explosion/spaCy/releases/tag/v1.8.2
.. _v1.8.1: https://github.com/explosion/spaCy/releases/tag/v1.8.1
.. _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

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examples/training/train_new_entity_type.py
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@ -1,4 +1,5 @@
#!/usr/bin/env python
# coding: utf8
"""
Example of training an additional entity type
@ -26,11 +27,11 @@ For more details, see the documentation:
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 random
import spacy
from spacy.gold import GoldParse
@ -43,14 +44,35 @@ def train_ner(nlp, train_data, output_dir):
doc = nlp.make_doc(raw_text)
for word in doc:
_ = nlp.vocab[word.orth]
for itn in range(20):
random.seed(0)
# You may need to change the learning rate. It's generally difficult to
# guess what rate you should set, especially when you have limited data.
nlp.entity.model.learn_rate = 0.001
for itn in range(1000):
random.shuffle(train_data)
loss = 0.
for raw_text, entity_offsets in train_data:
gold = GoldParse(doc, entities=entity_offsets)
# By default, the GoldParse class assumes that the entities
# described by offset are complete, and all other words should
# have the tag 'O'. You can tell it to make no assumptions
# about the tag of a word by giving it the tag '-'.
# However, this allows a trivial solution to the current
# learning problem: if words are either 'any tag' or 'ANIMAL',
# the model can learn that all words can be tagged 'ANIMAL'.
#for i in range(len(gold.ner)):
#if not gold.ner[i].endswith('ANIMAL'):
# gold.ner[i] = '-'
doc = nlp.make_doc(raw_text)
nlp.tagger(doc)
loss = nlp.entity.update(doc, gold)
# As of 1.9, spaCy's parser now lets you supply a dropout probability
# This might help the model generalize better from only a few
# examples.
loss += nlp.entity.update(doc, gold, drop=0.9)
if loss == 0:
break
# This step averages the model's weights. This may or may not be good for
# your situation --- it's empirical.
nlp.end_training()
if output_dir:
if not output_dir.exists():
@ -80,13 +102,19 @@ def main(model_name, output_directory=None):
(
"they pretend to care about your feelings, those horses",
[(48, 54, 'ANIMAL')]
),
(
"horses?",
[(0, 6, '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?')
print("Ents in 'Do you like horses?':")
for ent in doc.ents:
print(ent.label_, ent.text)
if output_directory:

1
setup.py Normal file → Executable file
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@ -36,6 +36,7 @@ PACKAGES = [
'spacy.fi',
'spacy.bn',
'spacy.he',
'spacy.nb',
'spacy.en.lemmatizer',
'spacy.cli.converters',
'spacy.language_data',

6
spacy/__init__.py
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@ -3,14 +3,14 @@ from __future__ import unicode_literals
from . import util
from .deprecated import resolve_model_name
from .cli import info
from .cli.info import info
from . import en, de, zh, es, it, hu, fr, pt, nl, sv, fi, bn, he
from . import en, de, zh, es, it, hu, fr, pt, nl, sv, fi, bn, he, nb
_languages = (en.English, de.German, es.Spanish, pt.Portuguese, fr.French,
it.Italian, hu.Hungarian, zh.Chinese, nl.Dutch, sv.Swedish,
fi.Finnish, bn.Bengali, he.Hebrew)
fi.Finnish, bn.Bengali, he.Hebrew, nb.Norwegian)
for _lang in _languages:

4
spacy/about.py
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@ -3,7 +3,7 @@
# https://github.com/pypa/warehouse/blob/master/warehouse/__about__.py
__title__ = 'spacy'
__version__ = '1.8.0'
__version__ = '1.8.2'
__summary__ = 'Industrial-strength Natural Language Processing (NLP) with Python and Cython'
__uri__ = 'https://spacy.io'
__author__ = 'Matthew Honnibal'
@ -13,4 +13,4 @@ __license__ = 'MIT'
__docs__ = 'https://spacy.io/docs/usage'
__download_url__ = 'https://github.com/explosion/spacy-models/releases/download'
__compatibility__ = 'https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json'
__shortcuts__ = {'en': 'en_core_web_sm', 'de': 'de_core_news_md', 'vectors': 'en_vectors_glove_md'}
__shortcuts__ = 'https://raw.githubusercontent.com/explosion/spacy-models/master/shortcuts.json'

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spacy/cli/download.py
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@ -17,29 +17,37 @@ def download(model=None, direct=False):
if direct:
download_model('{m}/{m}.tar.gz'.format(m=model))
else:
model_name = about.__shortcuts__[model] if model in about.__shortcuts__ else model
model_name = check_shortcut(model)
compatibility = get_compatibility()
version = get_version(model_name, compatibility)
download_model('{m}-{v}/{m}-{v}.tar.gz'.format(m=model_name, v=version))
link_package(model_name, model, force=True)
def get_compatibility():
version = about.__version__
r = requests.get(about.__compatibility__)
def get_json(url, desc):
r = requests.get(url)
if r.status_code != 200:
util.sys_exit(
"Couldn't fetch compatibility table. Please find the right model for "
"your spaCy installation (v{v}), and download it manually:".format(v=version),
"Couldn't fetch {d}. Please find the right model for your spaCy "
"installation (v{v}), and download it manually:".format(d=desc, v=about.__version__),
"python -m spacy.download [full model name + version] --direct",
title="Server error ({c})".format(c=r.status_code))
return r.json()
comp = r.json()['spacy']
def check_shortcut(model):
shortcuts = get_json(about.__shortcuts__, "available shortcuts")
return shortcuts.get(model, model)
def get_compatibility():
version = about.__version__
comp_table = get_json(about.__compatibility__, "compatibility table")
comp = comp_table['spacy']
if version not in comp:
util.sys_exit(
"No compatible models found for v{v} of spaCy.".format(v=version),
title="Compatibility error")
else:
return comp[version]
@ -47,7 +55,7 @@ def get_version(model, comp):
if model not in comp:
util.sys_exit(
"No compatible model found for "
"{m} (spaCy v{v}).".format(m=model, v=about.__version__),
"'{m}' (spaCy v{v}).".format(m=model, v=about.__version__),
title="Compatibility error")
return comp[model][0]

5
spacy/cli/train.py
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@ -2,6 +2,7 @@
from __future__ import unicode_literals, division, print_function
import json
from collections import defaultdict
from ..util import ensure_path
from ..scorer import Scorer
@ -62,10 +63,10 @@ def train_model(Language, train_data, dev_data, output_path, tagger_cfg, parser_
for itn, epoch in enumerate(trainer.epochs(n_iter, augment_data=None)):
for doc, gold in epoch:
trainer.update(doc, gold)
dev_scores = trainer.evaluate(dev_data) if dev_data else []
dev_scores = trainer.evaluate(dev_data).scores if dev_data else defaultdict(float)
print_progress(itn, trainer.nlp.parser.model.nr_weight,
trainer.nlp.parser.model.nr_active_feat,
**dev_scores.scores)
**dev_scores)
def evaluate(Language, gold_tuples, output_path):

5
spacy/compat.py
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@ -23,6 +23,8 @@ is_windows = sys.platform.startswith('win')
is_linux = sys.platform.startswith('linux')
is_osx = sys.platform == 'darwin'
fix_text = ftfy.fix_text
if is_python2:
bytes_ = str
@ -39,9 +41,6 @@ elif is_python3:
json_dumps = lambda data: ujson.dumps(data, indent=2)
fix_text = lambda text: ftfy.fix_text(text)
def symlink_to(orig, dest):
if is_python2 and is_windows:
import subprocess

4
spacy/language.py
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@ -195,7 +195,7 @@ class Language(object):
if directory.exists():
shutil.rmtree(str(directory))
directory.mkdir()
with (directory / 'config.json').open('wb') as file_:
with (directory / 'config.json').open('w') as file_:
data = json_dumps(config)
file_.write(data)
if not (path / 'vocab').exists():
@ -366,6 +366,8 @@ class Language(object):
}
path = util.ensure_path(path)
if not path.exists():
path.mkdir()
self.setup_directory(path, **configs)
strings_loc = path / 'vocab' / 'strings.json'

25
spacy/nb/__init__.py Normal file
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@ -0,0 +1,25 @@
# encoding: utf8
from __future__ import unicode_literals, print_function
from os import path
from ..language import Language
from ..attrs import LANG
# Import language-specific data
from .language_data import *
# create Language subclass
class Norwegian(Language):
lang = 'nb' # ISO code
class Defaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters[LANG] = lambda text: 'nb'
# override defaults
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
#tag_map = TAG_MAP
stop_words = STOP_WORDS

28
spacy/nb/language_data.py Normal file
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@ -0,0 +1,28 @@
# encoding: utf8
from __future__ import unicode_literals
# import base language data
from .. import language_data as base
# import util functions
from ..language_data import update_exc, strings_to_exc, expand_exc
# import language-specific data from files
#from .tag_map import TAG_MAP
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS, ORTH_ONLY
from .morph_rules import MORPH_RULES
TOKENIZER_EXCEPTIONS = dict(TOKENIZER_EXCEPTIONS)
#TAG_MAP = dict(TAG_MAP)
STOP_WORDS = set(STOP_WORDS)
# customize tokenizer exceptions
update_exc(TOKENIZER_EXCEPTIONS, strings_to_exc(ORTH_ONLY))
update_exc(TOKENIZER_EXCEPTIONS, expand_exc(TOKENIZER_EXCEPTIONS, "'", "’"))
update_exc(TOKENIZER_EXCEPTIONS, strings_to_exc(base.EMOTICONS))
# export
__all__ = ["TOKENIZER_EXCEPTIONS", "STOP_WORDS", "MORPH_RULES"]

67
spacy/nb/morph_rules.py Normal file
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@ -0,0 +1,67 @@
# encoding: utf8
# norwegian bokmål
from __future__ import unicode_literals
from ..symbols import *
from ..language_data import PRON_LEMMA
# Used the table of pronouns at https://no.wiktionary.org/wiki/Tillegg:Pronomen_i_norsk
MORPH_RULES = {
"PRP": {
"jeg": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Sing", "Case": "Nom"},
"meg": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Sing", "Case": "Acc"},
"du": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Two", "Number": "Sing", "Case": "Nom"},
"deg": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Two", "Number": "Sing", "Case": "Acc"},
"han": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Sing", "Gender": "Masc", "Case": "Nom"},
"ham": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Sing", "Gender": "Masc", "Case": "Acc"},
"han": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Sing", "Gender": "Masc", "Case": "Acc"},
"hun": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Sing", "Gender": "Fem", "Case": "Nom"},
"henne": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Sing", "Gender": "Fem", "Case": "Acc"},
"den": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Sing", "Gender": "Neut"},
"det": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Sing", "Gender": "Neut"},
"seg": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Sing", "Reflex": "Yes"},
"vi": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Plur", "Case": "Nom"},
"oss": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Plur", "Case": "Acc"},
"dere": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Two", "Number": "Plur", "Case": "Nom"},
"de": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Plur", "Case": "Nom"},
"dem": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Plur", "Case": "Acc"},
"seg": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Plur", "Reflex": "Yes"},
"min": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Sing", "Poss": "Yes", "Gender": "Masc"},
"mi": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Sing", "Poss": "Yes", "Gender": "Fem"},
"mitt": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Sing", "Poss": "Yes", "Gender": "Neu"},
"mine": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Plur", "Poss": "Yes"},
"din": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Two", "Number": "Sing", "Poss": "Yes", "Gender": "Masc"},
"di": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Two", "Number": "Sing", "Poss": "Yes", "Gender": "Fem"},
"ditt": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Two", "Number": "Sing", "Poss": "Yes", "Gender": "Neu"},
"dine": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Two", "Number": "Plur", "Poss": "Yes"},
"hans": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Sing", "Poss": "Yes", "Gender": "Masc"},
"hennes": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Sing", "Poss": "Yes", "Gender": "Fem"},
"dens": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Sing", "Poss": "Yes", "Gender": "Neu"},
"dets": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "Three", "Number": "Sing", "Poss": "Yes", "Gender": "Neu"},
"vår": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Plur", "Poss": "Yes"},
"vårt": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Plur", "Poss": "Yes"},
"våre": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Plur", "Poss": "Yes", "Gender":"Neu"},
"deres": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Plur", "Poss": "Yes", "Gender":"Neu", "Reflex":"Yes"},
"sin": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Sing", "Poss": "Yes", "Gender":"Masc", "Reflex":"Yes"},
"si": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Sing", "Poss": "Yes", "Gender":"Fem", "Reflex":"Yes"},
"sitt": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Sing", "Poss": "Yes", "Gender":"Neu", "Reflex":"Yes"},
"sine": {LEMMA: PRON_LEMMA, "PronType": "Prs", "Person": "One", "Number": "Plur", "Poss": "Yes", "Reflex":"Yes"},
},
"VBZ": {
"er": {LEMMA: "be", "VerbForm": "Fin", "Person": "One", "Tense": "Pres", "Mood": "Ind"},
"er": {LEMMA: "be", "VerbForm": "Fin", "Person": "Two", "Tense": "Pres", "Mood": "Ind"},
"er": {LEMMA: "be", "VerbForm": "Fin", "Person": "Three", "Tense": "Pres", "Mood": "Ind"},
},
"VBP": {
"er": {LEMMA: "be", "VerbForm": "Fin", "Tense": "Pres", "Mood": "Ind"}
},
"VBD": {
"var": {LEMMA: "be", "VerbForm": "Fin", "Tense": "Past", "Number": "Sing"},
"vært": {LEMMA: "be", "VerbForm": "Fin", "Tense": "Past", "Number": "Plur"}
}
}

49
spacy/nb/stop_words.py Normal file
View File

@ -0,0 +1,49 @@
# encoding: utf8
from __future__ import unicode_literals
STOP_WORDS = set("""
alle allerede alt and andre annen annet at av
bak bare bedre beste blant ble bli blir blitt bris by både
da dag de del dem den denne der dermed det dette disse drept du
eller en enn er et ett etter
fem fikk fire fjor flere folk for fortsatt fotball fra fram frankrike fredag funnet få får fått før først første
gang gi gikk gjennom gjorde gjort gjør gjøre god godt grunn gå går
ha hadde ham han hans har hele helt henne hennes her hun hva hvor hvordan hvorfor
i ifølge igjen ikke ingen inn
ja jeg
kamp kampen kan kl klart kom komme kommer kontakt kort kroner kunne kveld kvinner
la laget land landet langt leder ligger like litt løpet lørdag
man mandag mange mannen mars med meg mellom men mener menn mennesker mens mer millioner minutter mot msci mye må mål måtte
ned neste noe noen nok norge norsk norske ntb ny nye nå når
og også om onsdag opp opplyser oslo oss over
personer plass poeng politidistrikt politiet president prosent på
regjeringen runde rundt russland
sa saken samme sammen samtidig satt se seg seks selv senere september ser sett siden sier sin sine siste sitt skal skriver skulle slik som sted stedet stor store står sverige svært så søndag
ta tatt tid tidligere til tilbake tillegg tirsdag to tok torsdag tre tror tyskland
under usa ut uten utenfor
vant var ved veldig vi videre viktig vil ville viser vår være vært
å år
ønsker
""".split())

175
spacy/nb/tokenizer_exceptions.py Normal file
View File

@ -0,0 +1,175 @@
# encoding: utf8
# Norwegian bokmaål
from __future__ import unicode_literals
from ..symbols import *
from ..language_data import PRON_LEMMA
TOKENIZER_EXCEPTIONS = {
"jan.": [
{ORTH: "jan.", LEMMA: "januar"}
],
"feb.": [
{ORTH: "feb.", LEMMA: "februar"}
],
"jul.": [
{ORTH: "jul.", LEMMA: "juli"}
]
}
ORTH_ONLY = ["adm.dir.",
"a.m.",
"Aq.",
"b.c.",
"bl.a.",
"bla.",
"bm.",
"bto.",
"ca.",
"cand.mag.",
"c.c.",
"co.",
"d.d.",
"dept.",
"d.m.",
"dr.philos.",
"dvs.",
"d.y.",
"E. coli",
"eg.",
"ekskl.",
"e.Kr.",
"el.",
"e.l.",
"et.",
"etg.",
"ev.",
"evt.",
"f.",
"f.eks.",
"fhv.",
"fk.",
"f.Kr.",
"f.o.m.",
"foreg.",
"fork.",
"fv.",
"fvt.",
"g.",
"gt.",
"gl.",
"gno.",
"gnr.",
"grl.",
"hhv.",
"hoh.",
"hr.",
"h.r.adv.",
"ifb.",
"ifm.",
"iht.",
"inkl.",
"istf.",
"jf.",
"jr.",
"jun.",
"kfr.",
"kgl.res.",
"kl.",
"komm.",
"kst.",
"lø.",
"ma.",
"mag.art.",
"m.a.o.",
"md.",
"mfl.",
"mill.",
"min.",
"m.m.",
"mnd.",
"moh.",
"Mr.",
"muh.",
"mv.",
"mva.",
"ndf.",
"no.",
"nov.",
"nr.",
"nto.",
"nyno.",
"n.å.",
"o.a.",
"off.",
"ofl.",
"okt.",
"o.l.",
"on.",
"op.",
"osv.",
"ovf.",
"p.",
"p.a.",
"Pb.",
"pga.",
"ph.d.",
"pkt.",
"p.m.",
"pr.",
"pst.",
"p.t.",
"red.anm.",
"ref.",
"res.",
"res.kap.",
"resp.",
"rv.",
"s.",
"s.d.",
"sen.",
"sep.",
"siviling.",
"sms.",
"spm.",
"sr.",
"sst.",
"st.",
"stip.",
"stk.",
"st.meld.",
"st.prp.",
"stud.",
"s.u.",
"sv.",
"sø.",
"s.å.",
"såk.",
"temp.",
"ti.",
"tils.",
"tilsv.",
"tl;dr",
"tlf.",
"to.",
"t.o.m.",
"ult.",
"utg.",
"v.",
"vedk.",
"vedr.",
"vg.",
"vgs.",
"vha.",
"vit.ass.",
"vn.",
"vol.",
"vs.",
"vsa.",
"årg.",
"årh."
]

12
spacy/syntax/iterators.pyx
View File

@ -10,15 +10,22 @@ def english_noun_chunks(obj):
Works on both Doc and Span.
"""
labels = ['nsubj', 'dobj', 'nsubjpass', 'pcomp', 'pobj',
'attr', 'ROOT', 'root']
'attr', 'ROOT']
doc = obj.doc # Ensure works on both Doc and Span.
np_deps = [doc.vocab.strings[label] for label in labels]
conj = doc.vocab.strings['conj']
np_label = doc.vocab.strings['NP']
seen = set()
for i, word in enumerate(obj):
if word.pos not in (NOUN, PROPN, PRON):
continue
# Prevent nested chunks from being produced
if word.i in seen:
continue
if word.dep in np_deps:
if any(w.i in seen for w in word.subtree):
continue
seen.update(j for j in range(word.left_edge.i, word.i+1))
yield word.left_edge.i, word.i+1, np_label
elif word.dep == conj:
head = word.head
@ -26,6 +33,9 @@ def english_noun_chunks(obj):
head = head.head
# If the head is an NP, and we're coordinated to it, we're an NP
if head.dep in np_deps:
if any(w.i in seen for w in word.subtree):
continue
seen.update(j for j in range(word.left_edge.i, word.i+1))
yield word.left_edge.i, word.i+1, np_label

20
spacy/syntax/parser.pyx
View File

@ -11,6 +11,8 @@ import ujson
cimport cython
cimport cython.parallel
import numpy.random
from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
from cpython.exc cimport PyErr_CheckSignals
from libc.stdint cimport uint32_t, uint64_t
@ -146,7 +148,7 @@ cdef class Parser:
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():
for action_name, labels in dict(cfg.get('actions', {})).items():
# We need this to be sorted
if isinstance(labels, dict):
labels = list(sorted(labels.keys()))
@ -303,7 +305,7 @@ cdef class Parser:
free(eg.is_valid)
return 0
def update(self, Doc tokens, GoldParse gold, itn=0):
def update(self, Doc tokens, GoldParse gold, itn=0, double drop=0.0):
"""
Update the statistical model.
@ -325,9 +327,11 @@ cdef class Parser:
nr_feat=self.model.nr_feat)
cdef weight_t loss = 0
cdef Transition action
cdef double dropout_rate = self.cfg.get('dropout', drop)
while not stcls.is_final():
eg.c.nr_feat = self.model.set_featuresC(eg.c.atoms, eg.c.features,
stcls.c)
dropout(eg.c.features, eg.c.nr_feat, dropout_rate)
self.moves.set_costs(eg.c.is_valid, eg.c.costs, stcls, gold)
self.model.set_scoresC(eg.c.scores, eg.c.features, eg.c.nr_feat)
guess = VecVec.arg_max_if_true(eg.c.scores, eg.c.is_valid, eg.c.nr_class)
@ -378,6 +382,18 @@ cdef class Parser:
self.cfg.setdefault('extra_labels', []).append(label)
cdef int dropout(FeatureC* feats, int nr_feat, float prob) except -1:
if prob <= 0 or prob >= 1.:
return 0
cdef double[::1] py_probs = numpy.random.uniform(0., 1., nr_feat)
cdef double* probs = &py_probs[0]
for i in range(nr_feat):
if probs[i] >= prob:
feats[i].value /= prob
else:
feats[i].value = 0.
cdef class StepwiseState:
cdef readonly StateClass stcls
cdef readonly Example eg

7
spacy/tests/conftest.py
View File

@ -13,6 +13,8 @@ from ..hu import Hungarian
from ..fi import Finnish
from ..bn import Bengali
from ..he import Hebrew
from ..nb import Norwegian
from ..tokens import Doc
from ..strings import StringStore
@ -26,7 +28,7 @@ import pytest
LANGUAGES = [English, German, Spanish, Italian, French, Portuguese, Dutch,
Swedish, Hungarian, Finnish, Bengali]
Swedish, Hungarian, Finnish, Bengali, Norwegian]
@pytest.fixture(params=LANGUAGES)
@ -88,6 +90,9 @@ def bn_tokenizer():
def he_tokenizer():
return Hebrew.Defaults.create_tokenizer()
@pytest.fixture
def nb_tokenizer():
return Norwegian.Defaults.create_tokenizer()
@pytest.fixture
def stringstore():

0
spacy/tests/nb/__init__.py Normal file
View File

17
spacy/tests/nb/test_tokenizer.py Normal file
View File

@ -0,0 +1,17 @@
# coding: utf8
from __future__ import unicode_literals
import pytest
NB_TOKEN_EXCEPTION_TESTS = [
('Smørsausen brukes bl.a. til fisk', ['Smørsausen', 'brukes', 'bl.a.', 'til', 'fisk']),
('Jeg kommer først kl. 13 pga. diverse forsinkelser', ['Jeg', 'kommer', 'først', 'kl.', '13', 'pga.', 'diverse', 'forsinkelser'])
]
@pytest.mark.parametrize('text,expected_tokens', NB_TOKEN_EXCEPTION_TESTS)
def test_tokenizer_handles_exception_cases(nb_tokenizer, text, expected_tokens):
tokens = nb_tokenizer(text)
token_list = [token.text for token in tokens if not token.is_space]
assert expected_tokens == token_list

1
spacy/tests/regression/test_issue758.py
View File

@ -1,3 +1,4 @@
from __future__ import unicode_literals
from ... import load as load_spacy
from ...attrs import LEMMA
from ...matcher import merge_phrase

21
spacy/tests/regression/test_issue910.py
View File

@ -70,7 +70,6 @@ def temp_save_model(model):
@pytest.mark.xfail
@pytest.mark.models
def test_issue910(train_data, additional_entity_types):
'''Test that adding entities and resuming training works passably OK.
@ -85,11 +84,10 @@ def test_issue910(train_data, additional_entity_types):
ents_before_train = [(ent.label_, ent.text) for ent in doc.ents]
# Fine tune the ner model
for entity_type in additional_entity_types:
if entity_type not in nlp.entity.cfg['actions']['1']:
nlp.entity.add_label(entity_type)
nlp.entity.learn_rate = 0.001
for itn in range(4):
nlp.entity.model.learn_rate = 0.001
for itn in range(10):
random.shuffle(train_data)
for raw_text, entity_offsets in train_data:
doc = nlp.make_doc(raw_text)
@ -101,13 +99,12 @@ def test_issue910(train_data, additional_entity_types):
# Load the fine tuned model
loaded_ner = EntityRecognizer.load(model_dir, nlp.vocab)
for entity_type in additional_entity_types:
if entity_type not in loaded_ner.cfg['actions']['1']:
loaded_ner.add_label(entity_type)
doc = nlp(u"I am looking for a restaurant in Berlin", entity=False)
for raw_text, entity_offsets in train_data:
doc = nlp.make_doc(raw_text)
nlp.tagger(doc)
loaded_ner(doc)
ents_after_train = [(ent.label_, ent.text) for ent in doc.ents]
assert ents_before_train == ents_after_train
ents = {(ent.start_char, ent.end_char): ent.label_ for ent in doc.ents}
for start, end, label in entity_offsets:
if (start, end) not in ents:
print(ents)
assert ents[(start, end)] == label

22
spacy/tests/regression/test_issue995.py Normal file
View File

@ -0,0 +1,22 @@
from __future__ import unicode_literals
import pytest
from ... import load as load_spacy
@pytest.fixture
def doc():
nlp = load_spacy('en')
return nlp('Does flight number three fifty-four require a connecting flight'
' to get to Boston?')
@pytest.mark.models
def test_issue955(doc):
'''Test that we don't have any nested noun chunks'''
seen_tokens = set()
for np in doc.noun_chunks:
print(np.text, np.root.text, np.root.dep_, np.root.tag_)
for word in np:
key = (word.i, word.text)
assert key not in seen_tokens
seen_tokens.add(key)

78
spacy/tests/regression/test_issue999.py Normal file
View File

@ -0,0 +1,78 @@
from __future__ import unicode_literals
import os
import random
import contextlib
import shutil
import pytest
import tempfile
from pathlib import Path
import pathlib
from ...gold import GoldParse
from ...pipeline import EntityRecognizer
from ...language import Language
try:
unicode
except NameError:
unicode = str
@pytest.fixture
def train_data():
return [
["hey",[]],
["howdy",[]],
["hey there",[]],
["hello",[]],
["hi",[]],
["i'm looking for a place to eat",[]],
["i'm looking for a place in the north of town",[[31,36,"location"]]],
["show me chinese restaurants",[[8,15,"cuisine"]]],
["show me chines restaurants",[[8,14,"cuisine"]]],
]
@contextlib.contextmanager
def temp_save_model(model):
model_dir = Path(tempfile.mkdtemp())
model.save_to_directory(model_dir)
yield model_dir
shutil.rmtree(model_dir.as_posix())
def test_issue999(train_data):
'''Test that adding entities and resuming training works passably OK.
There are two issues here:
1) We have to readd labels. This isn't very nice.
2) There's no way to set the learning rate for the weight update, so we
end up out-of-scale, causing it to learn too fast.
'''
nlp = Language(path=None, entity=False, tagger=False, parser=False)
nlp.entity = EntityRecognizer(nlp.vocab, features=Language.Defaults.entity_features)
for _, offsets in train_data:
for start, end, ent_type in offsets:
nlp.entity.add_label(ent_type)
nlp.entity.model.learn_rate = 0.001
for itn in range(100):
random.shuffle(train_data)
for raw_text, entity_offsets in train_data:
doc = nlp.make_doc(raw_text)
gold = GoldParse(doc, entities=entity_offsets)
loss = nlp.entity.update(doc, gold)
with temp_save_model(nlp) as model_dir:
nlp2 = Language(path=model_dir)
for raw_text, entity_offsets in train_data:
doc = nlp2(raw_text)
ents = {(ent.start_char, ent.end_char): ent.label_ for ent in doc.ents}
for start, end, label in entity_offsets:
if (start, end) in ents:
assert ents[(start, end)] == label
break
else:
if entity_offsets:
raise Exception(ents)

12
spacy/tests/spans/test_span.py
View File

@ -77,3 +77,15 @@ def test_spans_override_sentiment(en_tokenizer):
assert doc[:2].sentiment == 10.0
assert doc[-2:].sentiment == 10.0
assert doc[:-1].sentiment == 10.0
def test_spans_are_hashable(en_tokenizer):
"""Test spans can be hashed."""
text = "good stuff bad stuff"
tokens = en_tokenizer(text)
span1 = tokens[:2]
span2 = tokens[2:4]
assert hash(span1) != hash(span2)
span3 = tokens[0:2]
assert hash(span3) == hash(span1)

7
spacy/tests/test_download.py → spacy/tests/test_cli.py
View File

@ -2,17 +2,18 @@
from __future__ import unicode_literals
from ..cli.download import download, get_compatibility, get_version, check_error_depr
import pytest
@pytest.mark.parametrize('model', ['en_core_web_md'])
def test_download_get_matching_version_succeeds(model):
def test_cli_download_get_matching_version_succeeds(model):
comp = { model: ['1.7.0', '0.100.0'] }
assert get_version(model, comp)
@pytest.mark.parametrize('model', ['en_core_web_md'])
def test_download_get_matching_version_fails(model):
def test_cli_download_get_matching_version_fails(model):
diff_model = 'test_' + model
comp = { diff_model: ['1.7.0', '0.100.0'] }
with pytest.raises(SystemExit):
@ -20,6 +21,6 @@ def test_download_get_matching_version_fails(model):
@pytest.mark.parametrize('model', [False, None, '', 'all'])
def test_download_no_model_depr_error(model):
def test_cli_download_no_model_depr_error(model):
with pytest.raises(SystemExit):
check_error_depr(model)

13
spacy/tests/test_misc.py Normal file
View File

@ -0,0 +1,13 @@
# coding: utf-8
from __future__ import unicode_literals
from ..util import ensure_path
from pathlib import Path
import pytest
@pytest.mark.parametrize('text', ['hello/world', 'hello world'])
def test_util_ensure_path_succeeds(text):
path = ensure_path(text)
assert isinstance(path, Path)

4
spacy/tokens/span.pyx
View File

@ -66,6 +66,10 @@ cdef class Span:
elif op == 5:
return self.start_char >= other.start_char
def __hash__(self):
return hash((self.doc, self.label, self.start_char, self.end_char))
def __len__(self):
self._recalculate_indices()
if self.end < self.start:

2
website/_harp.json
View File

@ -55,7 +55,7 @@
}
},
"V_CSS": "1.4",
"V_CSS": "1.5",
"V_JS": "1.2",
"DEFAULT_SYNTAX": "python",
"ANALYTICS": "UA-58931649-1",

22
website/_includes/_mixins.jade
View File

@ -198,8 +198,8 @@ mixin table(head)
//- Table row (only used within +table)
mixin row()
tr.c-table__row&attributes(attributes)
mixin row(...style)
tr.c-table__row(class=prefixArgs(style, "c-table__row"))&attributes(attributes)
block
@ -283,3 +283,21 @@ mixin card-item(title, details)
if details.author
br
span.u-text-small.u-color-subtle by #{details.author}
//- Model row for models table
mixin model-row(name, lang, procon, size, license, default_model, divider)
- var licenses = { "CC BY-SA": "https://creativecommons.org/licenses/by-sa/3.0/", "CC BY-NC": "https://creativecommons.org/licenses/by-nc/3.0/" }
+row(divider ? "divider": null)
+cell #[code=name]
if default_model
| #[span.u-color-theme(title="default model") #[+icon("star", 16)]]
+cell=lang
each icon in procon
+cell.u-text-center #[+procon(icon ? "pro" : "con")]
+cell.u-text-right=size
+cell
if license in licenses
+a(licenses[license])=license

3
website/assets/css/_components/_tables.sass
View File

@ -20,6 +20,9 @@
@extend .u-text-label
color: $color-theme
&.c-table__row--divider
border-top: 2px solid $color-theme
//- Table cell

3
website/assets/img/icons.svg
View File

@ -24,5 +24,8 @@
<symbol id="chat" viewBox="0 0 24 24">
<path d="M18 8.016v-2.016h-12v2.016h12zM18 11.016v-2.016h-12v2.016h12zM18 14.016v-2.016h-12v2.016h12zM21.984 3.984v18l-3.984-3.984h-14.016c-1.078 0-1.969-0.938-1.969-2.016v-12c0-1.078 0.891-1.969 1.969-1.969h16.031c1.078 0 1.969 0.891 1.969 1.969z"></path>
</symbol>
<symbol id="star" viewBox="0 0 24 24">
<path d="M12 17.25l-6.188 3.75 1.641-7.031-5.438-4.734 7.172-0.609 2.813-6.609 2.813 6.609 7.172 0.609-5.438 4.734 1.641 7.031z"></path>
</symbol>
</defs>
</svg>

Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 4.6 KiB

After

Width:  |  Height:  |  Size: 4.9 KiB

11
website/docs/api/language-models.jade
View File

@ -7,6 +7,7 @@ p spaCy currently supports the following languages and capabilities:
+aside-code("Download language models", "bash").
python -m spacy download en
python -m spacy download de
python -m spacy download fr
+table([ "Language", "Token", "SBD", "Lemma", "POS", "NER", "Dep", "Vector", "Sentiment"])
+row
@ -19,6 +20,14 @@ p spaCy currently supports the following languages and capabilities:
each icon in [ "pro", "pro", "con", "pro", "pro", "pro", "pro", "con" ]
+cell.u-text-center #[+procon(icon)]
+row
+cell French #[code fr]
each icon in [ "pro", "pro", "con", "pro", "con", "pro", "pro", "con" ]
+cell.u-text-center #[+procon(icon)]
+h(2, "available") Available models
include ../usage/_models-list
+h(2, "alpha-support") Alpha support
@ -27,7 +36,7 @@ p
| the existing language data and extending the tokenization patterns.
+table([ "Language", "Source" ])
each language, code in { zh: "Chinese", es: "Spanish", it: "Italian", fr: "French", pt: "Portuguese", nl: "Dutch", sv: "Swedish", fi: "Finnish", hu: "Hungarian", bn: "Bengali", he: "Hebrew" }
each language, code in { zh: "Chinese", es: "Spanish", it: "Italian", pt: "Portuguese", nl: "Dutch", sv: "Swedish", fi: "Finnish", nb: "Norwegian Bokmål", hu: "Hungarian", bn: "Bengali", he: "Hebrew" }
+row
+cell #{language} #[code=code]
+cell

27
website/docs/usage/_models-list.jade Normal file
View File

@ -0,0 +1,27 @@
//- 💫 DOCS > USAGE > MODELS LIST
include ../../_includes/_mixins
p
| Model differences are mostly statistical. In general, we do expect larger
| models to be "better" and more accurate overall. Ultimately, it depends on
| your use case and requirements, and we recommend starting with the default
| models (marked with a star below).
+aside
| Models are now available as #[code .tar.gz] archives #[+a(gh("spacy-models")) from GitHub],
| attached to individual releases. They can be downloaded and loaded manually,
| or using spaCy's #[code download] and #[code link] commands. All models
| follow the naming convention of #[code [language]_[type]_[genre]_[size]].
| #[br]#[br]
+button(gh("spacy-models"), true, "primary").u-text-tag
| View model releases
+table(["Name", "Language", "Voc", "Dep", "Ent", "Vec", "Size", "License"])
+model-row("en_core_web_sm", "English", [1, 1, 1, 1], "50 MB", "CC BY-SA", true)
+model-row("en_core_web_md", "English", [1, 1, 1, 1], "1 GB", "CC BY-SA")
+model-row("en_depent_web_md", "English", [1, 1, 1, 0], "328 MB", "CC BY-SA")
+model-row("en_vectors_glove_md", "English", [1, 0, 0, 1], "727 MB", "CC BY-SA")
+model-row("de_core_news_md", "German", [1, 1, 1, 1], "645 MB", "CC BY-SA", true, true)
+model-row("fr_depvec_web_lg", "French", [1, 1, 0, 1], "1.33 GB", "CC BY-NC", true, true)

176
website/docs/usage/adding-languages.jade
View File

@ -27,9 +27,10 @@ p
| #[a(href="#brown-clusters") Brown clusters] and
| #[a(href="#word-vectors") word vectors].
+item
| #[strong Set up] a #[a(href="#model-directory") model direcory] and #[strong train] the #[a(href="#train-tagger-parser") tagger and parser].
p
| Once you have the tokenizer and vocabulary, you can
| #[+a("/docs/usage/training") train the tagger, parser and entity recognizer].
| For some languages, you may also want to develop a solution for
| lemmatization and morphological analysis.
@ -98,6 +99,17 @@ p
| so that Python functions can be used to help you generalise and combine
| the data as you require.
+infobox("For languages with non-latin characters")
| In order for the tokenizer to split suffixes, prefixes and infixes, spaCy
| needs to know the language's character set. If the language you're adding
| uses non-latin characters, you might need to add the required character
| classes to the global
| #[+src(gh("spacy", "spacy/language_data/punctuation.py")) punctuation.py].
| spaCy uses the #[+a("https://pypi.python.org/pypi/regex/") #[code regex] library]
| to keep this simple and readable. If the language requires very specific
| punctuation rules, you should consider overwriting the default regular
| expressions with your own in the language's #[code Defaults].
+h(3, "stop-words") Stop words
p
@ -395,12 +407,111 @@ p
| by linear models, while the word vectors are useful for lexical
| similarity models and deep learning.
+h(3, "word-frequencies") Word frequencies
p
| To generate the word frequencies from a large, raw corpus, you can use the
| #[+src(gh("spacy-dev-resources", "training/word_freqs.py")) word_freqs.py]
| script from the spaCy developer resources. Note that your corpus should
| not be preprocessed (i.e. you need punctuation for example). The
| #[+a("/docs/usage/cli#model") #[code model] command] expects a
| tab-separated word frequencies file with three columns:
+list("numbers")
+item The number of times the word occurred in your language sample.
+item The number of distinct documents the word occurred in.
+item The word itself.
p
| An example word frequencies file could look like this:
+code("es_word_freqs.txt", "text").
6361109 111 Aunque
23598543 111 aunque
10097056 111 claro
193454 111 aro
7711123 111 viene
12812323 111 mal
23414636 111 momento
2014580 111 felicidad
233865 111 repleto
15527 111 eto
235565 111 deliciosos
17259079 111 buena
71155 111 Anímate
37705 111 anímate
33155 111 cuéntanos
2389171 111 cuál
961576 111 típico
p
| You should make sure you use the spaCy tokenizer for your
| language to segment the text for your word frequencies. This will ensure
| that the frequencies refer to the same segmentation standards you'll be
| using at run-time. For instance, spaCy's English tokenizer segments
| "can't" into two tokens. If we segmented the text by whitespace to
| produce the frequency counts, we'll have incorrect frequency counts for
| the tokens "ca" and "n't".
+h(3, "brown-clusters") Training the Brown clusters
p
| spaCy's tagger, parser and entity recognizer are designed to use
| distributional similarity features provided by the
| #[+a("https://github.com/percyliang/brown-cluster") Brown clustering algorithm].
| You should train a model with between 500 and 1000 clusters. A minimum
| frequency threshold of 10 usually works well.
p
| An example clusters file could look like this:
+code("es_clusters.data", "text").
0000 Vestigial 1
0000 Vesturland 1
0000 Veyreau 1
0000 Veynes 1
0000 Vexilografía 1
0000 Vetrigne 1
0000 Vetónica 1
0000 Asunden 1
0000 Villalambrús 1
0000 Vichuquén 1
0000 Vichtis 1
0000 Vichigasta 1
0000 VAAH 1
0000 Viciebsk 1
0000 Vicovaro 1
0000 Villardeveyo 1
0000 Vidala 1
0000 Videoguard 1
0000 Vedás 1
0000 Videocomunicado 1
0000 VideoCrypt 1
+h(3, "word-vectors") Training the word vectors
p
| #[+a("https://en.wikipedia.org/wiki/Word2vec") Word2vec] and related
| algorithms let you train useful word similarity models from unlabelled
| text. This is a key part of using
| #[+a("/docs/usage/deep-learning") deep learning] for NLP with limited
| labelled data. The vectors are also useful by themselves – they power
| the #[code .similarity()] methods in spaCy. For best results, you should
| pre-process the text with spaCy before training the Word2vec model. This
| ensures your tokenization will match.
p
| You can use our
| #[+src(gh("spacy-dev-resources", "training/word_vectors.py")) word vectors training script],
| which pre-processes the text with your language-specific tokenizer and
| trains the model using #[+a("https://radimrehurek.com/gensim/") Gensim].
| The #[code vectors.bin] file should consist of one word and vector per line.
+h(2, "model-directory") Setting up a model directory
p
| Once you've collected the word frequencies, Brown clusters and word
| 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], or use the new
| #[+a("/docs/usage/cli#model") #[code model] command] to create a data
| directory:
@ -427,49 +538,22 @@ p
| loaded. By default, the command expects to be able to find your language
| class using #[code spacy.util.get_lang_class(lang_id)].
+h(3, "word-frequencies") Word frequencies
+h(2, "train-tagger-parser") Training the tagger and parser
p
| The #[+a("/docs/usage/cli#model") #[code model] command] expects a
| tab-separated word frequencies file with three columns:
+list("numbers")
+item The number of times the word occurred in your language sample.
+item The number of distinct documents the word occurred in.
+item The word itself.
| You can now train the model using a corpus for your language annotated
| with #[+a("http://universaldependencies.org/") Universal Dependencies].
| If your corpus uses the
| #[+a("http://universaldependencies.org/docs/format.html") CoNLL-U] format,
| i.e. files with the extension #[code .conllu], you can use the
| #[+a("/docs/usage/cli#convert") #[code convert] command] to convert it to
| spaCy's #[+a("/docs/api/annotation#json-input") JSON format] for training.
p
| You should make sure you use the spaCy tokenizer for your
| language to segment the text for your word frequencies. This will ensure
| that the frequencies refer to the same segmentation standards you'll be
| using at run-time. For instance, spaCy's English tokenizer segments
| "can't" into two tokens. If we segmented the text by whitespace to
| produce the frequency counts, we'll have incorrect frequency counts for
| the tokens "ca" and "n't".
| Once you have your UD corpus transformed into JSON, you can train your
| model use the using spaCy's
| #[+a("/docs/usage/cli#train") #[code train] command]:
+h(3, "brown-clusters") Training the Brown clusters
p
| spaCy's tagger, parser and entity recognizer are designed to use
| distributional similarity features provided by the
| #[+a("https://github.com/percyliang/brown-cluster") Brown clustering algorithm].
| You should train a model with between 500 and 1000 clusters. A minimum
| frequency threshold of 10 usually works well.
+h(3, "word-vectors") Training the word vectors
p
| #[+a("https://en.wikipedia.org/wiki/Word2vec") Word2vec] and related
| algorithms let you train useful word similarity models from unlabelled
| text. This is a key part of using
| #[+a("/docs/usage/deep-learning") deep learning] for NLP with limited
| labelled data. The vectors are also useful by themselves – they power
| the #[code .similarity()] methods in spaCy. For best results, you should
| pre-process the text with spaCy before training the Word2vec model. This
| ensures your tokenization will match.
p
| You can use our
| #[+src(gh("spacy-dev-resources", "training/word_vectors.py")) word vectors training script],
| which pre-processes the text with your language-specific tokenizer and
| trains the model using #[+a("https://radimrehurek.com/gensim/") Gensim].
+code(false, "bash").
python -m spacy train [lang] [output_dir] [train_data] [dev_data] [--n_iter] [--parser_L1] [--no_tagger] [--no_parser] [--no_ner]

6
website/docs/usage/deep-learning.jade
View File

@ -4,9 +4,9 @@ include ../../_includes/_mixins
p
| In this example, we'll be using #[+a("https://keras.io/") Keras], as
| it's the most popular deep learning library for Python. Let's assume
| you've written a custom sentiment analysis model that predicts whether a
| document is positive or negative. Now you want to find which entities
| it's the most popular deep learning library for Python. Using Keras,
| we will write a custom sentiment analysis model that predicts whether a
| document is positive or negative. Then, we will use it to find which entities
| are commonly associated with positive or negative documents. Here's a
| quick example of how that can look at runtime.

53
website/docs/usage/models.jade
View File

@ -13,14 +13,6 @@ p
| internal alias that tells spaCy where to find the data files for a specific
| model name.
+infobox("Important note")
| Due to improvements in the English lemmatizer in v1.7.0, you need to
| #[strong download the new English models]. The German model is still
| compatible. If you've trained statistical models that use spaCy's
| annotations, you should #[strong retrain your models after updating spaCy].
| If you don't retrain your models, you may suffer train/test skew, which
| might decrease your accuracy.
+aside-code("Quickstart").
# Install spaCy and download English model
pip install spacy
@ -31,43 +23,17 @@ p
nlp = spacy.load('en')
doc = nlp(u'This is a sentence.')
+infobox("Important note")
| Due to improvements in the English lemmatizer in v1.7.0, you need to
| #[strong download the new English models]. The German model is still
| compatible. If you've trained statistical models that use spaCy's
| annotations, you should #[strong retrain your models after updating spaCy].
| If you don't retrain your models, you may suffer train/test skew, which
| might decrease your accuracy.
+h(2, "available") Available models
+table(["Name", "Size", "Description"])
+row
+cell #[code en_core_web_sm]
+cell 50 MB
+cell Vocab, syntax, entities, word vectors #[+tag default]
+row
+cell #[code en_core_web_md]
+cell 1 GB
+cell Vocab, syntax, entities, word vectors
+row
+cell #[code en_depent_web_md]
+cell 328 MB
+cell Vocab, syntax, entities
+row
+cell #[code en_vectors_glove_md]
+cell 727 MB
+cell
| #[+a("http://nlp.stanford.edu/projects/glove/") GloVe] Common
| Crawl vectors
+row
+cell #[code de_core_news_md]
+cell 645 MB
+cell Vocab, syntax, entities, word vectors #[+tag default]
p
| Models are now available as #[code .tar.gz] archives #[+a(gh("spacy-models")) from GitHub],
| attached to individual releases. They can be downloaded and loaded manually,
| or using spaCy's #[code download] and #[code link] commands. All models
| follow the naming convention of #[code [language]_[type]_[genre]_[size]].
+button(gh("spacy-models") + "/releases", true, "primary") View models
include _models-list
+h(2, "download") Downloading models
@ -95,6 +61,7 @@ p
# out-of-the-box: download best-matching default model
python -m spacy download en
python -m spacy download de
python -m spacy download fr
# download best-matching version of specific model for your spaCy installation
python -m spacy download en_core_web_md

2
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@ -61,7 +61,7 @@ p
| #[+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 [language]_[name]] and #[code [language]_[name]-[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.