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# 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 | Daniel Vila-Suero |
| Company name (if applicable) | recogn.ai |
| Title or role (if applicable) | |
| Date | 07-04-2017 |
| GitHub username | dvsrepo |
| Website (optional) | recogn.ai |

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@ -12,6 +12,7 @@ This is a list of everyone who has made significant contributions to spaCy, in a
* Christoph Schwienheer, [@chssch](https://github.com/chssch) * Christoph Schwienheer, [@chssch](https://github.com/chssch)
* Dafne van Kuppevelt, [@dafnevk](https://github.com/dafnevk) * Dafne van Kuppevelt, [@dafnevk](https://github.com/dafnevk)
* Daniel Rapp, [@rappdw](https://github.com/rappdw) * Daniel Rapp, [@rappdw](https://github.com/rappdw)
* Daniel Vila Suero, [@dvsrepo](https://github.com/dvsrepo)
* Dmytro Sadovnychyi, [@sadovnychyi](https://github.com/sadovnychyi) * Dmytro Sadovnychyi, [@sadovnychyi](https://github.com/sadovnychyi)
* Eric Zhao, [@ericzhao28](https://github.com/ericzhao28) * Eric Zhao, [@ericzhao28](https://github.com/ericzhao28)
* Greg Baker, [@solresol](https://github.com/solresol) * Greg Baker, [@solresol](https://github.com/solresol)

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@ -78,7 +78,7 @@ You can run the `keras_parikh_entailment/` directory as a script, which executes
[`keras_parikh_entailment/__main__.py`](__main__.py). The first thing you'll want to do is train the model: [`keras_parikh_entailment/__main__.py`](__main__.py). The first thing you'll want to do is train the model:
```bash ```bash
python keras_parikh_entailment/ train <your_model_dir> <train_directory> <dev_directory> python keras_parikh_entailment/ train <train_directory> <dev_directory>
``` ```
Training takes about 300 epochs for full accuracy, and I haven't rerun the full Training takes about 300 epochs for full accuracy, and I haven't rerun the full

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@ -52,7 +52,7 @@ def train(train_loc, dev_loc, shape, settings):
file_.write(model.to_json()) file_.write(model.to_json())
def evaluate(model_dir, dev_loc): def evaluate(dev_loc):
dev_texts1, dev_texts2, dev_labels = read_snli(dev_loc) dev_texts1, dev_texts2, dev_labels = read_snli(dev_loc)
nlp = spacy.load('en', nlp = spacy.load('en',
create_pipeline=create_similarity_pipeline) create_pipeline=create_similarity_pipeline)

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@ -80,10 +80,10 @@ def get_word_ids(docs, rnn_encode=False, tree_truncate=False, max_length=100, nr
return Xs return Xs
def create_similarity_pipeline(nlp): def create_similarity_pipeline(nlp, max_length=100):
return [ return [
nlp.tagger, nlp.tagger,
nlp.entity, nlp.entity,
nlp.parser, nlp.parser,
KerasSimilarityShim.load(nlp.path / 'similarity', nlp, max_length=10) KerasSimilarityShim.load(nlp.path / 'similarity', nlp, max_length)
] ]

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@ -10,3 +10,4 @@ six
ujson>=1.35 ujson>=1.35
dill>=0.2,<0.3 dill>=0.2,<0.3
requests>=2.13.0,<3.0.0 requests>=2.13.0,<3.0.0
regex>=2.4.120,<3.0.0

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@ -246,7 +246,8 @@ def setup_package():
'pathlib', 'pathlib',
'ujson>=1.35', 'ujson>=1.35',
'dill>=0.2,<0.3', 'dill>=0.2,<0.3',
'requests>=2.13.0,<3.0.0'], 'requests>=2.13.0,<3.0.0',
'regex>=2.4.120,<3.0.0'],
classifiers=[ classifiers=[
'Development Status :: 5 - Production/Stable', 'Development Status :: 5 - Production/Stable',
'Environment :: Console', 'Environment :: Console',

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@ -17,4 +17,5 @@ class Spanish(Language):
lex_attr_getters[LANG] = lambda text: 'es' lex_attr_getters[LANG] = lambda text: 'es'
tokenizer_exceptions = TOKENIZER_EXCEPTIONS tokenizer_exceptions = TOKENIZER_EXCEPTIONS
tag_map = TAG_MAP
stop_words = STOP_WORDS stop_words = STOP_WORDS

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@ -5,6 +5,7 @@ from .. import language_data as base
from ..language_data import update_exc, strings_to_exc from ..language_data import update_exc, strings_to_exc
from ..symbols import ORTH, LEMMA from ..symbols import ORTH, LEMMA
from .tag_map import TAG_MAP
from .stop_words import STOP_WORDS from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS, ORTH_ONLY from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS, ORTH_ONLY
@ -39,7 +40,7 @@ def get_time_exc(hours):
] ]
return exc return exc
TAG_MAP = dict(TAG_MAP)
STOP_WORDS = set(STOP_WORDS) STOP_WORDS = set(STOP_WORDS)
@ -51,4 +52,4 @@ update_exc(TOKENIZER_EXCEPTIONS, strings_to_exc(base.EMOTICONS))
update_exc(TOKENIZER_EXCEPTIONS, strings_to_exc(base.ABBREVIATIONS)) update_exc(TOKENIZER_EXCEPTIONS, strings_to_exc(base.ABBREVIATIONS))
__all__ = ["TOKENIZER_EXCEPTIONS", "STOP_WORDS"] __all__ = ["TOKENIZER_EXCEPTIONS", "TAG_MAP", "STOP_WORDS"]

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@ -1,6 +1,8 @@
from __future__ import unicode_literals from __future__ import unicode_literals
import re # The use of this module turns out to be important, to avoid pathological
# back-tracking. See Issue #957
import regex
# URL validation regex courtesy of: https://mathiasbynens.be/demo/url-regex # URL validation regex courtesy of: https://mathiasbynens.be/demo/url-regex
# A few minor mods to this regex to account for use cases represented in test_urls # A few minor mods to this regex to account for use cases represented in test_urls
@ -23,6 +25,8 @@ _URL_PATTERN = (
# excludes reserved space >= 224.0.0.0 # excludes reserved space >= 224.0.0.0
# excludes network & broadcast addresses # excludes network & broadcast addresses
# (first & last IP address of each class) # (first & last IP address of each class)
# MH: Do we really need this? Seems excessive, and seems to have caused
# Issue #957
r"(?:[1-9]\d?|1\d\d|2[01]\d|22[0-3])" r"(?:[1-9]\d?|1\d\d|2[01]\d|22[0-3])"
r"(?:\.(?:1?\d{1,2}|2[0-4]\d|25[0-5])){2}" r"(?:\.(?:1?\d{1,2}|2[0-4]\d|25[0-5])){2}"
r"(?:\.(?:[1-9]\d?|1\d\d|2[0-4]\d|25[0-4]))" r"(?:\.(?:[1-9]\d?|1\d\d|2[0-4]\d|25[0-4]))"
@ -45,6 +49,6 @@ _URL_PATTERN = (
r"$" r"$"
).strip() ).strip()
TOKEN_MATCH = re.compile(_URL_PATTERN, re.UNICODE).match TOKEN_MATCH = regex.compile(_URL_PATTERN, regex.UNICODE).match
__all__ = ['TOKEN_MATCH'] __all__ = ['TOKEN_MATCH']

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@ -49,6 +49,10 @@ def en_vocab():
def en_parser(): def en_parser():
return English.Defaults.create_parser() return English.Defaults.create_parser()
@pytest.fixture
def es_tokenizer():
return Spanish.Defaults.create_tokenizer()
@pytest.fixture @pytest.fixture
def de_tokenizer(): def de_tokenizer():

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@ -0,0 +1,24 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest
@pytest.mark.parametrize('text,lemma', [("aprox.", "aproximadamente"),
("esq.", "esquina"),
("pág.", "página"),
("p.ej.", "por ejemplo")
])
def test_tokenizer_handles_abbr(es_tokenizer, text, lemma):
tokens = es_tokenizer(text)
assert len(tokens) == 1
assert tokens[0].lemma_ == lemma
def test_tokenizer_handles_exc_in_text(es_tokenizer):
text = "Mariano Rajoy ha corrido aprox. medio kilómetro"
tokens = es_tokenizer(text)
assert len(tokens) == 7
assert tokens[4].text == "aprox."
assert tokens[4].lemma_ == "aproximadamente"

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@ -0,0 +1,35 @@
# coding: utf-8
"""Test that longer and mixed texts are tokenized correctly."""
from __future__ import unicode_literals
import pytest
def test_tokenizer_handles_long_text(es_tokenizer):
text = """Cuando a José Mujica lo invitaron a dar una conferencia
en Oxford este verano, su cabeza hizo "crac". La "más antigua" universidad de habla
inglesa, esa que cobra decenas de miles de euros de matrícula a sus alumnos
y en cuyos salones han disertado desde Margaret Thatcher hasta Stephen Hawking,
reclamaba los servicios de este viejo de 81 años, formado en un colegio público
en Montevideo y que pregona las bondades de la vida austera."""
tokens = es_tokenizer(text)
assert len(tokens) == 90
@pytest.mark.parametrize('text,length', [
("¿Por qué José Mujica?", 6),
("“¿Oh no?”", 6),
("""¡Sí! "Vámonos", contestó José Arcadio Buendía""", 11),
("Corrieron aprox. 10km.", 5),
("Y entonces por qué...", 5)])
def test_tokenizer_handles_cnts(es_tokenizer, text, length):
tokens = es_tokenizer(text)
assert len(tokens) == length

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@ -0,0 +1,16 @@
import pytest
from ... import load as load_spacy
def test_issue913(en_tokenizer):
'''Test that spaCy doesn't hang on many periods.'''
string = '0'
for i in range(1, 100):
string += '.%d' % i
doc = en_tokenizer(string)
# Don't want tests to fail if they haven't installed pytest-timeout plugin
try:
test_issue913 = pytest.mark.timeout(5)(test_issue913)
except NameError:
pass