mirror of https://github.com/explosion/spaCy.git
Merge branch 'master' into develop
This commit is contained in:
Ines Montani
commit
a8f1efd2f5
|
|
@ -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:
|
||||
|
||||
* [ ] 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) | |
|
||||
|
|
@ -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
|
||||
|
|
@ -124,7 +125,10 @@ 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) /
|
||||
|
||||
vector = self.vector
|
||||
xp = get_array_module(vector)
|
||||
return (xp.dot(self.vector, other.vector) /
|
||||
(self.vector_norm * other.vector_norm))
|
||||
|
||||
def to_bytes(self):
|
||||
|
|
|
|||
|
|
@ -329,7 +329,10 @@ 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(self.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 +367,7 @@ 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)
|
||||
|
|
|
|||
|
|
@ -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 +234,10 @@ 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(self.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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -169,7 +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) /
|
||||
vector = self.vector
|
||||
xp = get_array_module(vector)
|
||||
return (xp.dot(vector, other.vector) /
|
||||
(self.vector_norm * other.vector_norm))
|
||||
|
||||
property lex_id:
|
||||
|
|
|
|||
Loading…
Reference in New Issue