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Ines Montani 2019-03-20 17:24:28 +01:00
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106
.github/contributors/Bharat123rox.md vendored Normal file
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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 GmbH](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 to 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 | Bharat Raghunathan |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 19 - 03 - 2019 |
| GitHub username | Bharat123rox |
| Website (optional) | |

0
spacy/cli/ud/__init__.py → bin/ud/__init__.py
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0
spacy/cli/ud/conll17_ud_eval.py → bin/ud/conll17_ud_eval.py
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0
spacy/cli/ud/run_eval.py → bin/ud/run_eval.py
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22
spacy/cli/ud/ud_run_test.py → bin/ud/ud_run_test.py
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@ -13,14 +13,14 @@ import srsly
import spacy
import spacy.util
from ...tokens import Token, Doc
from ...gold import GoldParse
from ...util import compounding, minibatch_by_words
from ...syntax.nonproj import projectivize
from ...matcher import Matcher
from spacy.tokens import Token, Doc
from spacy.gold import GoldParse
from spacy.util import compounding, minibatch_by_words
from spacy.syntax.nonproj import projectivize
from spacy.matcher import Matcher
# from ...morphology import Fused_begin, Fused_inside
from ... import displacy
# from spacy.morphology import Fused_begin, Fused_inside
from spacy import displacy
from collections import defaultdict, Counter
from timeit import default_timer as timer
@ -33,10 +33,10 @@ import numpy.random
from . import conll17_ud_eval
from ... import lang
from ...lang import zh
from ...lang import ja
from ...lang import ru
from spacy import lang
from spacy.lang import zh
from spacy.lang import ja
from spacy.lang import ru
################

18
spacy/cli/ud/ud_train.py → bin/ud/ud_train.py
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@ -13,12 +13,12 @@ import json
import spacy
import spacy.util
from ...tokens import Token, Doc
from ...gold import GoldParse
from ...util import compounding, minibatch, minibatch_by_words
from ...syntax.nonproj import projectivize
from ...matcher import Matcher
from ... import displacy
from spacy.tokens import Token, Doc
from spacy.gold import GoldParse
from spacy.util import compounding, minibatch, minibatch_by_words
from spacy.syntax.nonproj import projectivize
from spacy.matcher import Matcher
from spacy import displacy
from collections import defaultdict, Counter
from timeit import default_timer as timer
@ -28,9 +28,9 @@ import numpy.random
from . import conll17_ud_eval
from ... import lang
from ...lang import zh
from ...lang import ja
from spacy import lang
from spacy.lang import zh
from spacy.lang import ja
try:
import torch

12
setup.py
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@ -238,12 +238,12 @@ def setup_package():
],
setup_requires=["wheel"],
extras_require={
"cuda": ["cupy>=4.0"],
"cuda80": ["cupy-cuda80>=4.0"],
"cuda90": ["cupy-cuda90>=4.0"],
"cuda91": ["cupy-cuda91>=4.0"],
"cuda92": ["cupy-cuda92>=4.0"],
"cuda100": ["cupy-cuda100>=4.0"],
"cuda": ["thinc_gpu_ops>=0.0.1,<0.1.0", "cupy>=5.0.0b4"],
"cuda80": ["thinc_gpu_ops>=0.0.1,<0.1.0", "cupy-cuda80>=5.0.0b4"],
"cuda90": ["thinc_gpu_ops>=0.0.1,<0.1.0", "cupy-cuda90>=5.0.0b4"],
"cuda91": ["thinc_gpu_ops>=0.0.1,<0.1.0", "cupy-cuda91>=5.0.0b4"],
"cuda92": ["thinc_gpu_ops>=0.0.1,<0.1.0", "cupy-cuda92>=5.0.0b4"],
"cuda100": ["thinc_gpu_ops>=0.0.1,<0.1.0", "cupy-cuda100>=5.0.0b4"],
# Language tokenizers with external dependencies
"ja": ["mecab-python3==0.7"],
},

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spacy/__init__.py
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@ -1,6 +1,7 @@
# coding: utf8
from __future__ import unicode_literals
import warnings
import sys
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
@ -11,10 +12,14 @@ from thinc.neural.util import prefer_gpu, require_gpu
from .cli.info import info as cli_info
from .glossary import explain
from .about import __version__
from .errors import Warnings, deprecation_warning
from .errors import Errors, Warnings, deprecation_warning
from . import util
if sys.maxunicode == 65535:
raise SystemError(Errors.E130)
def load(name, **overrides):
depr_path = overrides.get("path")
if depr_path not in (True, False, None):

5
spacy/__main__.py
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@ -9,8 +9,7 @@ if __name__ == "__main__":
import sys
from wasabi import Printer
from spacy.cli import download, link, info, package, train, pretrain, convert
from spacy.cli import init_model, profile, evaluate, validate
from spacy.cli import ud_train, ud_evaluate, debug_data
from spacy.cli import init_model, profile, evaluate, validate, debug_data
msg = Printer()
@ -21,9 +20,7 @@ if __name__ == "__main__":
"train": train,
"pretrain": pretrain,
"debug-data": debug_data,
"ud-train": ud_train,
"evaluate": evaluate,
"ud-evaluate": ud_evaluate,
"convert": convert,
"package": package,
"init-model": init_model,

2
spacy/about.py
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@ -4,7 +4,7 @@
# fmt: off
__title__ = "spacy"
__version__ = "2.1.0"
__version__ = "2.1.1"
__summary__ = "Industrial-strength Natural Language Processing (NLP) with Python and Cython"
__uri__ = "https://spacy.io"
__author__ = "Explosion AI"

1
spacy/cli/__init__.py
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@ -10,4 +10,3 @@ from .evaluate import evaluate # noqa: F401
from .convert import convert # noqa: F401
from .init_model import init_model # noqa: F401
from .validate import validate # noqa: F401
from .ud import ud_train, ud_evaluate # noqa: F401

31
spacy/cli/pretrain.py
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@ -9,7 +9,7 @@ from collections import Counter
from pathlib import Path
from thinc.v2v import Affine, Maxout
from thinc.misc import LayerNorm as LN
from thinc.neural.util import prefer_gpu
from thinc.neural.util import prefer_gpu, get_array_module
from wasabi import Printer
import srsly
@ -27,6 +27,7 @@ from .. import util
width=("Width of CNN layers", "option", "cw", int),
depth=("Depth of CNN layers", "option", "cd", int),
embed_rows=("Embedding rows", "option", "er", int),
loss_func=("Loss to use for the objective. L2 or cosine", "option", "L", str),
use_vectors=("Whether to use the static vectors as input features", "flag", "uv"),
dropout=("Dropout", "option", "d", float),
batch_size=("Number of words per training batch", "option", "bs", int),
@ -42,6 +43,7 @@ def pretrain(
width=96,
depth=4,
embed_rows=2000,
loss_func="cosine",
use_vectors=False,
dropout=0.2,
nr_iter=1000,
@ -123,7 +125,7 @@ def pretrain(
max_length=max_length,
min_length=min_length,
)
loss = make_update(model, docs, optimizer, drop=dropout)
loss = make_update(model, docs, optimizer, objective=loss_func, drop=dropout)
progress = tracker.update(epoch, loss, docs)
if progress:
msg.row(progress, **row_settings)
@ -196,11 +198,26 @@ def get_vectors_loss(ops, docs, prediction, objective="L2"):
ids = ops.flatten([doc.to_array(ID).ravel() for doc in docs])
target = docs[0].vocab.vectors.data[ids]
if objective == "L2":
d_scores = prediction - target
loss = (d_scores ** 2).sum()
else:
raise NotImplementedError(objective)
return loss, d_scores
d_target = prediction - target
loss = (d_target ** 2).sum()
elif objective == "cosine":
loss, d_target = get_cossim_loss(prediction, target)
return loss, d_target
def get_cossim_loss(yh, y):
# Add a small constant to avoid 0 vectors
yh = yh + 1e-8
y = y + 1e-8
# https://math.stackexchange.com/questions/1923613/partial-derivative-of-cosine-similarity
xp = get_array_module(yh)
norm_yh = xp.linalg.norm(yh, axis=1, keepdims=True)
norm_y = xp.linalg.norm(y, axis=1, keepdims=True)
mul_norms = norm_yh * norm_y
cosine = (yh * y).sum(axis=1, keepdims=True) / mul_norms
d_yh = (y / mul_norms) - (cosine * (yh / norm_yh**2))
loss = xp.abs(cosine-1).sum()
return loss, -d_yh
def create_pretraining_model(nlp, tok2vec):

4
spacy/errors.py
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@ -367,6 +367,10 @@ class Errors(object):
"Instead, create a new Span object and specify the `label` keyword argument, "
"for example:\nfrom spacy.tokens import Span\n"
"span = Span(doc, start={start}, end={end}, label='{label}')")
E130 = ("You are running a narrow unicode build, which is incompatible "
"with spacy >= 2.1.0. To fix this, reinstall Python and use a wide "
"unicode build instead. You can also rebuild Python and set the "
"--enable-unicode=ucs4 flag.")
@add_codes

2
spacy/lang/fr/lemmatizer/lookup.py
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@ -139784,7 +139784,7 @@ LOOKUP = {
"nomadisant": ("nomadiser",),
"nomadisent": ("nomadiser",),
"nomadismes": ("nomadisme",),
"nombres": ("nombrer",),
"nombres": ("nombre",),
"nombreuse": ("nombreux",),
"nombreuses": ("nombreux",),
"nombrilismes": ("nombrilisme",),

7
spacy/tokens/doc.pyx
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@ -416,8 +416,9 @@ cdef class Doc:
return self.user_hooks["vector"](self)
if self._vector is not None:
return self._vector
elif not len(self):
self._vector = numpy.zeros((self.vocab.vectors_length,), dtype="f")
xp = get_array_module(self.vocab.vectors.data)
if not len(self):
self._vector = xp.zeros((self.vocab.vectors_length,), dtype="f")
return self._vector
elif self.vocab.vectors.data.size > 0:
self._vector = sum(t.vector for t in self) / len(self)
@ -426,7 +427,7 @@ cdef class Doc:
self._vector = self.tensor.mean(axis=0)
return self._vector
else:
return numpy.zeros((self.vocab.vectors_length,), dtype="float32")
return xp.zeros((self.vocab.vectors_length,), dtype="float32")
def __set__(self, value):
self._vector = value

10
spacy/tokens/span.pyx
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@ -420,13 +420,11 @@ cdef class Span:
"""
if "vector_norm" in self.doc.user_span_hooks:
return self.doc.user_span_hooks["vector"](self)
cdef float value
cdef double norm = 0
vector = self.vector
xp = get_array_module(vector)
if self._vector_norm is None:
norm = 0
for value in self.vector:
norm += value * value
self._vector_norm = sqrt(norm) if norm != 0 else 0
total = (vector*vector).sum()
self._vector_norm = xp.sqrt(total) if total != 0. else 0.
return self._vector_norm
@property

4
spacy/tokens/token.pyx
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@ -404,7 +404,9 @@ cdef class Token:
if "vector_norm" in self.doc.user_token_hooks:
return self.doc.user_token_hooks["vector_norm"](self)
vector = self.vector
return numpy.sqrt((vector ** 2).sum())
xp = get_array_module(vector)
total = (vector ** 2).sum()
return xp.sqrt(total) if total != 0. else 0.
@property
def n_lefts(self):

16
website/docs/api/span.md
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@ -18,14 +18,14 @@ Create a Span object from the `slice doc[start : end]`.
> assert [t.text for t in span] == [u"it", u"back", u"!"]
> ```
| Name | Type | Description |
| ----------- | ---------------------------------------- | ------------------------------------------------------- |
| `doc` | `Doc` | The parent document. |
| `start` | int | The index of the first token of the span. |
| `end` | int | The index of the first token after the span. |
| `label` | int | A label to attach to the span, e.g. for named entities. |
| `vector` | `numpy.ndarray[ndim=1, dtype='float32']` | A meaning representation of the span. |
| **RETURNS** | `Span` | The newly constructed object. |
| Name | Type | Description |
| ----------- | ---------------------------------------- | ----------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The parent document. |
| `start` | int | The index of the first token of the span. |
| `end` | int | The index of the first token after the span. |
| `label` | int / unicode | A label to attach to the span, e.g. for named entities. As of v2.1, the label can also be a unicode string. |
| `vector` | `numpy.ndarray[ndim=1, dtype='float32']` | A meaning representation of the span. |
| **RETURNS** | `Span` | The newly constructed object. |
## Span.\_\_getitem\_\_ {#getitem tag="method"}