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Merge branch 'master' into tokenizer_exceptions

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106
.github/contributors/wallinm1.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 | Michael Wallin |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2017-02-04 |
| GitHub username | wallinm1 |
| Website (optional) | |

2
CONTRIBUTING.md
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@ -19,7 +19,7 @@ First, [do a quick search](https://github.com/issues?q=+is%3Aissue+user%3Aexplos
If you're looking for help with your code, consider posting a question on [StackOverflow](http://stackoverflow.com/questions/tagged/spacy) instead. If you tag it `spacy` and `python`, more people will see it and hopefully be able to help.
When opening an issue, use a descriptive title and include your environment (operating system, Python version, spaCy version). Our [issue template](https://github.com/explosion/spaCy/issues/new) helps you remember the most important details to include.
When opening an issue, use a descriptive title and include your environment (operating system, Python version, spaCy version). Our [issue template](https://github.com/explosion/spaCy/issues/new) helps you remember the most important details to include. **Pro tip:** If you need to share long blocks of code or logs, you can wrap them in `<details>` and `</details>`. This [collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details) so it only becomes visible on click, making the issue easier to read and follow.
If you've discovered a bug, you can also submit a [regression test](#fixing-bugs) straight away. When you're opening an issue to report the bug, simply refer to your pull request in the issue body.

2
CONTRIBUTORS.md
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@ -22,12 +22,14 @@ This is a list of everyone who has made significant contributions to spaCy, in a
* Mark Amery, [@ExplodingCabbage](https://github.com/ExplodingCabbage)
* Matthew Honnibal, [@honnibal](https://github.com/honnibal)
* Maxim Samsonov, [@maxirmx](https://github.com/maxirmx)
* Michael Wallin, [@wallinm1](https://github.com/wallinm1)
* Oleg Zd, [@olegzd](https://github.com/olegzd)
* Pokey Rule, [@pokey](https://github.com/pokey)
* Raphaël Bournhonesque, [@raphael0202](https://github.com/raphael0202)
* Rob van Nieuwpoort, [@RvanNieuwpoort](https://github.com/RvanNieuwpoort)
* Sam Bozek, [@sambozek](https://github.com/sambozek)
* Sasho Savkov [@savkov](https://github.com/savkov)
* Thomas Tanon, [@Tpt](https://github.com/Tpt)
* Tiago Rodrigues, [@TiagoMRodrigues](https://github.com/TiagoMRodrigues)
* Vsevolod Solovyov, [@vsolovyov](https://github.com/vsolovyov)
* Wah Loon Keng, [@kengz](https://github.com/kengz)

4
README.rst
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@ -5,8 +5,8 @@ spaCy is a library for advanced natural language processing in Python and
Cython. spaCy is built on the very latest research, but it isn't researchware.
It was designed from day one to be used in real products. spaCy currently supports
English and German, as well as tokenization for Chinese, Spanish, Italian, French,
Portuguese, Dutch, Swedish and Hungarian. It's commercial open-source software,
released under the MIT license.
Portuguese, Dutch, Swedish, Finnish and Hungarian. It's commercial open-source
software, released under the MIT license.
💫 **Version 1.6 out now!** `Read the release notes here. <https://github.com/explosion/spaCy/releases/>`_

49
examples/keras_parikh_entailment/__main__.py
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@ -12,17 +12,23 @@ from spacy_hook import create_similarity_pipeline
from keras_decomposable_attention import build_model
try:
import cPickle as pickle
except ImportError:
import pickle
def train(model_dir, train_loc, dev_loc, shape, settings):
train_texts1, train_texts2, train_labels = read_snli(train_loc)
dev_texts1, dev_texts2, dev_labels = read_snli(dev_loc)
print("Loading spaCy")
nlp = spacy.load('en')
assert nlp.path is not None
print("Compiling network")
model = build_model(get_embeddings(nlp.vocab), shape, settings)
print("Processing texts...")
Xs = []
Xs = []
for texts in (train_texts1, train_texts2, dev_texts1, dev_texts2):
Xs.append(get_word_ids(list(nlp.pipe(texts, n_threads=20, batch_size=20000)),
max_length=shape[0],
@ -36,35 +42,41 @@ def train(model_dir, train_loc, dev_loc, shape, settings):
validation_data=([dev_X1, dev_X2], dev_labels),
nb_epoch=settings['nr_epoch'],
batch_size=settings['batch_size'])
if not (nlp.path / 'similarity').exists():
(nlp.path / 'similarity').mkdir()
print("Saving to", model_dir / 'similarity')
weights = model.get_weights()
with (nlp.path / 'similarity' / 'model').open('wb') as file_:
pickle.dump(weights[1:], file_)
with (nlp.path / 'similarity' / 'config.json').open('wb') as file_:
file_.write(model.to_json())
def evaluate(model_dir, dev_loc):
nlp = spacy.load('en', path=model_dir,
tagger=False, parser=False, entity=False, matcher=False,
dev_texts1, dev_texts2, dev_labels = read_snli(dev_loc)
nlp = spacy.load('en',
create_pipeline=create_similarity_pipeline)
n = 0
correct = 0
for (text1, text2), label in zip(dev_texts, dev_labels):
total = 0.
correct = 0.
for text1, text2, label in zip(dev_texts1, dev_texts2, dev_labels):
doc1 = nlp(text1)
doc2 = nlp(text2)
sim = doc1.similarity(doc2)
if bool(sim >= 0.5) == label:
if sim.argmax() == label.argmax():
correct += 1
n += 1
total += 1
return correct, total
def demo(model_dir):
nlp = spacy.load('en', path=model_dir,
tagger=False, parser=False, entity=False, matcher=False,
create_pipeline=create_similarity_pipeline)
doc1 = nlp(u'Worst fries ever! Greasy and horrible...')
doc2 = nlp(u'The milkshakes are good. The fries are bad.')
print('doc1.similarity(doc2)', doc1.similarity(doc2))
sent1a, sent1b = doc1.sents
print('sent1a.similarity(sent1b)', sent1a.similarity(sent1b))
print('sent1a.similarity(doc2)', sent1a.similarity(doc2))
print('sent1b.similarity(doc2)', sent1b.similarity(doc2))
doc1 = nlp(u'What were the best crime fiction books in 2016?')
doc2 = nlp(
u'What should I read that was published last year? I like crime stories.')
print(doc1)
print(doc2)
print("Similarity", doc1.similarity(doc2))
LABELS = {'entailment': 0, 'contradiction': 1, 'neutral': 2}
@ -119,7 +131,8 @@ def main(mode, model_dir, train_loc, dev_loc,
if mode == 'train':
train(model_dir, train_loc, dev_loc, shape, settings)
elif mode == 'evaluate':
evaluate(model_dir, dev_loc)
correct, total = evaluate(model_dir, dev_loc)
print(correct, '/', total, correct / total)
else:
demo(model_dir)

30
examples/keras_parikh_entailment/keras_decomposable_attention.py
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@ -12,6 +12,8 @@ from keras.models import Sequential, Model, model_from_json
from keras.regularizers import l2
from keras.optimizers import Adam
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import GlobalAveragePooling1D, GlobalMaxPooling1D
from keras.layers import Merge
def build_model(vectors, shape, settings):
@ -29,11 +31,11 @@ def build_model(vectors, shape, settings):
align = _SoftAlignment(max_length, nr_hidden)
compare = _Comparison(max_length, nr_hidden, dropout=settings['dropout'])
entail = _Entailment(nr_hidden, nr_class, dropout=settings['dropout'])
# Declare the model as a computational graph.
sent1 = embed(ids1) # Shape: (i, n)
sent2 = embed(ids2) # Shape: (j, n)
if settings['gru_encode']:
sent1 = encode(sent1)
sent2 = encode(sent2)
@ -42,12 +44,12 @@ def build_model(vectors, shape, settings):
align1 = align(sent2, attention)
align2 = align(sent1, attention, transpose=True)
feats1 = compare(sent1, align1)
feats2 = compare(sent2, align2)
scores = entail(feats1, feats2)
# Now that we have the input/output, we can construct the Model object...
model = Model(input=[ids1, ids2], output=[scores])
@ -93,7 +95,7 @@ class _StaticEmbedding(object):
def get_output_shape(shapes):
print(shapes)
return shapes[0]
mod_sent = self.mod_ids(sentence)
mod_sent = self.mod_ids(sentence)
tuning = self.tune(mod_sent)
#tuning = merge([tuning, mod_sent],
# mode=lambda AB: AB[0] * (K.clip(K.cast(AB[1], 'float32'), 0, 1)),
@ -129,7 +131,7 @@ class _Attention(object):
self.model.add(Dense(nr_hidden, name='attend2',
init='he_normal', W_regularizer=l2(L2), activation='relu'))
self.model = TimeDistributed(self.model)
def __call__(self, sent1, sent2):
def _outer(AB):
att_ji = K.batch_dot(AB[1], K.permute_dimensions(AB[0], (0, 2, 1)))
@ -158,7 +160,7 @@ class _SoftAlignment(object):
return K.batch_dot(sm_att, mat)
return merge([attention, sentence], mode=_normalize_attention,
output_shape=(self.max_length, self.nr_hidden)) # Shape: (i, n)
class _Comparison(object):
def __init__(self, words, nr_hidden, L2=0.0, dropout=0.0):
@ -176,10 +178,12 @@ class _Comparison(object):
def __call__(self, sent, align, **kwargs):
result = self.model(merge([sent, align], mode='concat')) # Shape: (i, n)
result = _GlobalSumPooling1D()(result, mask=self.words)
result = BatchNormalization()(result)
avged = GlobalAveragePooling1D()(result, mask=self.words)
maxed = GlobalMaxPooling1D()(result, mask=self.words)
merged = merge([avged, maxed])
result = BatchNormalization()(merged)
return result
class _Entailment(object):
def __init__(self, nr_hidden, nr_out, dropout=0.0, L2=0.0):
@ -251,7 +255,7 @@ def test_fit_model():
shape = (10, 16, 3)
settings = {'lr': 0.001, 'dropout': 0.2, 'gru_encode':True}
model = build_model(vectors, shape, settings)
train_X = _generate_X(20, shape[0], vectors.shape[1])
train_Y = _generate_Y(20, shape[2])
dev_X = _generate_X(15, shape[0], vectors.shape[1])
@ -261,6 +265,4 @@ def test_fit_model():
batch_size=4)
__all__ = [build_model]

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

1
setup.py
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@ -31,6 +31,7 @@ PACKAGES = [
'spacy.pt',
'spacy.nl',
'spacy.sv',
'spacy.fi',
'spacy.language_data',
'spacy.serialize',
'spacy.syntax',

4
spacy/__init__.py
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@ -13,7 +13,7 @@ from . import fr
from . import pt
from . import nl
from . import sv
from . import fi
try:
basestring
@ -31,6 +31,8 @@ set_lang_class(hu.Hungarian.lang, hu.Hungarian)
set_lang_class(zh.Chinese.lang, zh.Chinese)
set_lang_class(nl.Dutch.lang, nl.Dutch)
set_lang_class(sv.Swedish.lang, sv.Swedish)
set_lang_class(fi.Finnish.lang, fi.Finnish)
def load(name, **overrides):

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

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

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

202
spacy/fi/tokenizer_exceptions.py Normal file
View File

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

1
spacy/language_data/emoticons.py
View File

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

4
spacy/language_data/punctuation.py
View File

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

6
spacy/sv/language_data.py
View File

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

45
spacy/sv/lemma_rules.py Normal file
View File

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

88
spacy/sv/tokenizer_exceptions.py
View File

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

13
spacy/tests/conftest.py
View File

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

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

18
spacy/tests/fi/test_tokenizer.py Normal file
View File

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

12
spacy/tests/regression/test_issue792.py Normal file
View File

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

19
spacy/tests/regression/test_issue801.py Normal file
View File

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

15
spacy/tests/regression/test_issue805.py Normal file
View File

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

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

24
spacy/tests/sv/test_tokenizer.py Normal file
View File

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

6
spacy/tokens/doc.pyx
View File

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

2
website/_harp.json
View File

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

4
website/assets/js/main.js
View File

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

22
website/docs/api/language-models.jade
View File

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

2
website/docs/usage/entity-recognition.jade
View File

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

2
website/docs/usage/rule-based-matching.jade
View File

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