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Merge branch 'develop' into spacy.io

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Ines Montani 2019-03-07 12:23:39 +01:00
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106
.github/contributors/danielkingai2.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 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:
* [ ] 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 | Daniel King |
| Company name (if applicable) | Allen Institute for Artificial Intelligence |
| Title or role (if applicable) | Predoctoral Young Investigator |
| Date | 03/06/2019 |
| GitHub username | danielkingai2 |
| Website (optional) | |

6
spacy/lexeme.pyx
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@ -8,6 +8,7 @@ cimport numpy as np
np.import_array()
from libc.string cimport memset
import numpy
from thinc.neural.util import get_array_module
from .typedefs cimport attr_t, flags_t
from .attrs cimport IS_ALPHA, IS_ASCII, IS_DIGIT, IS_LOWER, IS_PUNCT, IS_SPACE
@ -124,8 +125,9 @@ cdef class Lexeme:
if self.vector_norm == 0 or other.vector_norm == 0:
user_warning(Warnings.W008.format(obj='Lexeme'))
return 0.0
return (numpy.dot(self.vector, other.vector) /
(self.vector_norm * other.vector_norm))
vector = self.vector
xp = get_array_module(vector)
return (xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm))
def to_bytes(self):
lex_data = Lexeme.c_to_bytes(self.c)

9
spacy/tokens/doc.pyx
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@ -329,7 +329,9 @@ cdef class Doc:
if self.vector_norm == 0 or other.vector_norm == 0:
user_warning(Warnings.W008.format(obj='Doc'))
return 0.0
return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
vector = self.vector
xp = get_array_module(vector)
return xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
property has_vector:
"""A boolean value indicating whether a word vector is associated with
@ -364,10 +366,7 @@ cdef class Doc:
dtype='f')
return self._vector
elif self.vocab.vectors.data.size > 0:
vector = numpy.zeros((self.vocab.vectors_length,), dtype='f')
for token in self.c[:self.length]:
vector += self.vocab.get_vector(token.lex.orth)
self._vector = vector / len(self)
self._vector = sum(t.vector for t in self) / len(self)
return self._vector
elif self.tensor.size > 0:
self._vector = self.tensor.mean(axis=0)

5
spacy/tokens/span.pyx
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@ -6,6 +6,7 @@ cimport numpy as np
import numpy
import numpy.linalg
from libc.math cimport sqrt
from thinc.neural.util import get_array_module
from .doc cimport token_by_start, token_by_end, get_token_attr, _get_lca_matrix
from .token cimport TokenC
@ -233,7 +234,9 @@ cdef class Span:
if self.vector_norm == 0.0 or other.vector_norm == 0.0:
user_warning(Warnings.W008.format(obj='Span'))
return 0.0
return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
vector = self.vector
xp = get_array_module(vector)
return xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
cpdef np.ndarray to_array(self, object py_attr_ids):
"""Given a list of M attribute IDs, export the tokens to a numpy

6
spacy/tokens/token.pyx
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@ -9,6 +9,7 @@ from cython.view cimport array as cvarray
cimport numpy as np
np.import_array()
import numpy
from thinc.neural.util import get_array_module
from ..typedefs cimport hash_t
from ..lexeme cimport Lexeme
@ -169,8 +170,9 @@ cdef class Token:
if self.vector_norm == 0 or other.vector_norm == 0:
user_warning(Warnings.W008.format(obj='Token'))
return 0.0
return (numpy.dot(self.vector, other.vector) /
(self.vector_norm * other.vector_norm))
vector = self.vector
xp = get_array_module(vector)
return (xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm))
property lex_id:
"""RETURNS (int): Sequential ID of the token's lexical type."""

4
website/docs/api/cli.md
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@ -186,8 +186,8 @@ $ python -m spacy train [lang] [output_path] [train_path] [dev_path]
| ----------------------------------------------------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `lang` | positional | Model language. |
| `output_path` | positional | Directory to store model in. Will be created if it doesn't exist. |
| `train_path` | positional | Location of JSON-formatted training data. |
| `dev_path` | positional | Location of JSON-formatted development data for evaluation. |
| `train_path` | positional | Location of JSON-formatted training data. Can be a file or a directory of files. |
| `dev_path` | positional | Location of JSON-formatted development data for evaluation. Can be a file or a directory of files. |
| `--base-model`, `-b` | option | Optional name of base model to update. Can be any loadable spaCy model. |
| `--pipeline`, `-p` <Tag variant="new">2.1</Tag> | option | Comma-separated names of pipeline components to train. Defaults to `'tagger,parser,ner'`. |
| `--vectors`, `-v` | option | Model to load vectors from. |