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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 | Al Johri |
| Company name (if applicable) | N/A |
| Title or role (if applicable) | N/A |
| Date | December 27th, 2019 |
| GitHub username | AlJohri |
| Website (optional) | http://aljohri.com/ |

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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 | Olamilekan Wahab |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 8/11/2019 |
| GitHub username | Olamyy |
| Website (optional) | |

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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 | Ivan Echevarria |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2019-12-24 |
| GitHub username | iechevarria |
| Website (optional) | https://echevarria.io |

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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 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:
* [ ] 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 | Anastasiia Iurshina |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 28.12.2019 |
| GitHub username | iurshina |
| Website (optional) | |

1
bin/spacy
View File

@ -1 +1,2 @@
#! /bin/sh
python -m spacy "$@"

2
spacy/_align.pyx
View File

@ -30,7 +30,7 @@ S[:i] -> T[:j] (at D[i,j])
S[:i+1] -> T[:j] (at D[i+1,j])
S[:i] -> T[:j+1] (at D[i,j+1])
Further, we now we can tranform:
Further, now we can transform:
S[:i+1] -> S[:i] (DEL) for 1,
T[:j+1] -> T[:j] (INS) for 1.
S[i+1] -> T[j+1] (SUB) for 0 or 1

3
spacy/displacy/__init__.py
View File

@ -55,9 +55,10 @@ def render(
html = RENDER_WRAPPER(html)
if jupyter or (jupyter is None and is_in_jupyter()):
# return HTML rendered by IPython display()
# See #4840 for details on span wrapper to disable mathjax
from IPython.core.display import display, HTML
return display(HTML(html))
return display(HTML('<span class="tex2jax_ignore">{}</span>'.format(html)))
return html

11
spacy/errors.py
View File

@ -78,10 +78,9 @@ class Warnings(object):
W015 = ("As of v2.1.0, the use of keyword arguments to exclude fields from "
"being serialized or deserialized is deprecated. Please use the "
"`exclude` argument instead. For example: exclude=['{arg}'].")
W016 = ("The keyword argument `n_threads` on the is now deprecated, as "
"the v2.x models cannot release the global interpreter lock. "
"Future versions may introduce a `n_process` argument for "
"parallel inference via multiprocessing.")
W016 = ("The keyword argument `n_threads` is now deprecated. As of v2.2.2, "
"the argument `n_process` controls parallel inference via "
"multiprocessing.")
W017 = ("Alias '{alias}' already exists in the Knowledge Base.")
W018 = ("Entity '{entity}' already exists in the Knowledge Base - "
"ignoring the duplicate entry.")
@ -105,6 +104,10 @@ class Warnings(object):
W025 = ("'{name}' requires '{attr}' to be assigned, but none of the "
"previous components in the pipeline declare that they assign it.")
W026 = ("Unable to set all sentence boundaries from dependency parses.")
W027 = ("Found a large training file of {size} bytes. Note that it may "
"be more efficient to split your training data into multiple "
"smaller JSON files instead.")
@add_codes

10
spacy/gold.pyx
View File

@ -13,7 +13,7 @@ import srsly
from .syntax import nonproj
from .tokens import Doc, Span
from .errors import Errors, AlignmentError
from .errors import Errors, AlignmentError, user_warning, Warnings
from .compat import path2str
from . import util
from .util import minibatch, itershuffle
@ -557,12 +557,16 @@ def _json_iterate(loc):
loc = util.ensure_path(loc)
with loc.open("rb") as file_:
py_raw = file_.read()
cdef long file_length = len(py_raw)
if file_length > 2 ** 30:
user_warning(Warnings.W027.format(size=file_length))
raw = <char*>py_raw
cdef int square_depth = 0
cdef int curly_depth = 0
cdef int inside_string = 0
cdef int escape = 0
cdef int start = -1
cdef long start = -1
cdef char c
cdef char quote = ord('"')
cdef char backslash = ord("\\")
@ -570,7 +574,7 @@ def _json_iterate(loc):
cdef char close_square = ord("]")
cdef char open_curly = ord("{")
cdef char close_curly = ord("}")
for i in range(len(py_raw)):
for i in range(file_length):
c = raw[i]
if escape:
escape = False

32
spacy/lang/el/tag_map.py
View File

@ -4249,20 +4249,20 @@ TAG_MAP = {
"Voice": "Act",
"Case": "Nom|Gen|Dat|Acc|Voc",
},
'ADJ': {POS: ADJ},
'ADP': {POS: ADP},
'ADV': {POS: ADV},
'AtDf': {POS: DET},
'AUX': {POS: AUX},
'CCONJ': {POS: CCONJ},
'DET': {POS: DET},
'NOUN': {POS: NOUN},
'NUM': {POS: NUM},
'PART': {POS: PART},
'PRON': {POS: PRON},
'PROPN': {POS: PROPN},
'SCONJ': {POS: SCONJ},
'SYM': {POS: SYM},
'VERB': {POS: VERB},
'X': {POS: X},
"ADJ": {POS: ADJ},
"ADP": {POS: ADP},
"ADV": {POS: ADV},
"AtDf": {POS: DET},
"AUX": {POS: AUX},
"CCONJ": {POS: CCONJ},
"DET": {POS: DET},
"NOUN": {POS: NOUN},
"NUM": {POS: NUM},
"PART": {POS: PART},
"PRON": {POS: PRON},
"PROPN": {POS: PROPN},
"SCONJ": {POS: SCONJ},
"SYM": {POS: SYM},
"VERB": {POS: VERB},
"X": {POS: X},
}

10
spacy/lang/ja/__init__.py
View File

@ -16,7 +16,8 @@ from ...util import DummyTokenizer
# the flow by creating a dummy with the same interface.
DummyNode = namedtuple("DummyNode", ["surface", "pos", "feature"])
DummyNodeFeatures = namedtuple("DummyNodeFeatures", ["lemma"])
DummySpace = DummyNode(' ', ' ', DummyNodeFeatures(' '))
DummySpace = DummyNode(" ", " ", DummyNodeFeatures(" "))
def try_fugashi_import():
"""Fugashi is required for Japanese support, so check for it.
@ -27,8 +28,7 @@ def try_fugashi_import():
return fugashi
except ImportError:
raise ImportError(
"Japanese support requires Fugashi: "
"https://github.com/polm/fugashi"
"Japanese support requires Fugashi: " "https://github.com/polm/fugashi"
)
@ -55,13 +55,14 @@ def resolve_pos(token):
return token.pos + ",ADJ"
return token.pos
def get_words_and_spaces(tokenizer, text):
"""Get the individual tokens that make up the sentence and handle white space.
Japanese doesn't usually use white space, and MeCab's handling of it for
multiple spaces in a row is somewhat awkward.
"""
tokens = tokenizer.parseToNodeList(text)
words = []
@ -76,6 +77,7 @@ def get_words_and_spaces(tokenizer, text):
spaces.append(bool(token.white_space))
return words, spaces
class JapaneseTokenizer(DummyTokenizer):
def __init__(self, cls, nlp=None):
self.vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)

3
spacy/lang/lb/punctuation.py
View File

@ -1,8 +1,7 @@
# coding: utf8
from __future__ import unicode_literals
from ..char_classes import LIST_ELLIPSES, LIST_ICONS
from ..char_classes import CONCAT_QUOTES, ALPHA, ALPHA_LOWER, ALPHA_UPPER
from ..char_classes import LIST_ELLIPSES, LIST_ICONS, ALPHA, ALPHA_LOWER, ALPHA_UPPER
ELISION = " ' ’ ".strip().replace(" ", "")

2
spacy/lang/lb/tokenizer_exceptions.py
View File

@ -20,7 +20,7 @@ for exc_data in [
{ORTH: "asw.", LEMMA: "an sou weider", NORM: "an sou weider"},
{ORTH: "etc.", LEMMA: "et cetera", NORM: "et cetera"},
{ORTH: "bzw.", LEMMA: "bezéiungsweis", NORM: "bezéiungsweis"},
{ORTH: "Jan.", LEMMA: "Januar", NORM: "Januar"}
{ORTH: "Jan.", LEMMA: "Januar", NORM: "Januar"},
]:
_exc[exc_data[ORTH]] = [exc_data]

332
spacy/lang/nb/tag_map.py
View File

@ -467,38 +467,110 @@ TAG_MAP = {
"VERB__VerbForm=Part": {"morph": "VerbForm=Part", POS: VERB},
"VERB___": {"morph": "_", POS: VERB},
"X___": {"morph": "_", POS: X},
'CCONJ___': {"morph": "_", POS: CCONJ},
"CCONJ___": {"morph": "_", POS: CCONJ},
"ADJ__Abbr=Yes": {"morph": "Abbr=Yes", POS: ADJ},
"ADJ__Abbr=Yes|Degree=Pos": {"morph": "Abbr=Yes|Degree=Pos", POS: ADJ},
"ADJ__Case=Gen|Definite=Def|Number=Sing|VerbForm=Part": {"morph": "Case=Gen|Definite=Def|Number=Sing|VerbForm=Part", POS: ADJ},
"ADJ__Definite=Def|Number=Sing|VerbForm=Part": {"morph": "Definite=Def|Number=Sing|VerbForm=Part", POS: ADJ},
"ADJ__Definite=Ind|Gender=Masc|Number=Sing|VerbForm=Part": {"morph": "Definite=Ind|Gender=Masc|Number=Sing|VerbForm=Part", POS: ADJ},
"ADJ__Definite=Ind|Gender=Neut|Number=Sing|VerbForm=Part": {"morph": "Definite=Ind|Gender=Neut|Number=Sing|VerbForm=Part", POS: ADJ},
"ADJ__Definite=Ind|Number=Sing|VerbForm=Part": {"morph": "Definite=Ind|Number=Sing|VerbForm=Part", POS: ADJ},
"ADJ__Case=Gen|Definite=Def|Number=Sing|VerbForm=Part": {
"morph": "Case=Gen|Definite=Def|Number=Sing|VerbForm=Part",
POS: ADJ,
},
"ADJ__Definite=Def|Number=Sing|VerbForm=Part": {
"morph": "Definite=Def|Number=Sing|VerbForm=Part",
POS: ADJ,
},
"ADJ__Definite=Ind|Gender=Masc|Number=Sing|VerbForm=Part": {
"morph": "Definite=Ind|Gender=Masc|Number=Sing|VerbForm=Part",
POS: ADJ,
},
"ADJ__Definite=Ind|Gender=Neut|Number=Sing|VerbForm=Part": {
"morph": "Definite=Ind|Gender=Neut|Number=Sing|VerbForm=Part",
POS: ADJ,
},
"ADJ__Definite=Ind|Number=Sing|VerbForm=Part": {
"morph": "Definite=Ind|Number=Sing|VerbForm=Part",
POS: ADJ,
},
"ADJ__Number=Sing|VerbForm=Part": {"morph": "Number=Sing|VerbForm=Part", POS: ADJ},
"ADJ__VerbForm=Part": {"morph": "VerbForm=Part", POS: ADJ},
"ADP__Abbr=Yes": {"morph": "Abbr=Yes", POS: ADP},
"ADV__Abbr=Yes": {"morph": "Abbr=Yes", POS: ADV},
"DET__Case=Gen|Gender=Masc|Number=Sing|PronType=Art": {"morph": "Case=Gen|Gender=Masc|Number=Sing|PronType=Art", POS: DET},
"DET__Case=Gen|Number=Plur|PronType=Tot": {"morph": "Case=Gen|Number=Plur|PronType=Tot", POS: DET},
"DET__Case=Gen|Gender=Masc|Number=Sing|PronType=Art": {
"morph": "Case=Gen|Gender=Masc|Number=Sing|PronType=Art",
POS: DET,
},
"DET__Case=Gen|Number=Plur|PronType=Tot": {
"morph": "Case=Gen|Number=Plur|PronType=Tot",
POS: DET,
},
"DET__Definite=Def|PronType=Prs": {"morph": "Definite=Def|PronType=Prs", POS: DET},
"DET__Definite=Ind|Gender=Fem|Number=Sing|PronType=Prs": {"morph": "Definite=Ind|Gender=Fem|Number=Sing|PronType=Prs", POS: DET},
"DET__Definite=Ind|Gender=Masc|Number=Sing|PronType=Prs": {"morph": "Definite=Ind|Gender=Masc|Number=Sing|PronType=Prs", POS: DET},
"DET__Definite=Ind|Gender=Neut|Number=Sing|PronType=Prs": {"morph": "Definite=Ind|Gender=Neut|Number=Sing|PronType=Prs", POS: DET},
"DET__Gender=Fem|Number=Sing|PronType=Art": {"morph": "Gender=Fem|Number=Sing|PronType=Art", POS: DET},
"DET__Gender=Fem|Number=Sing|PronType=Ind": {"morph": "Gender=Fem|Number=Sing|PronType=Ind", POS: DET},
"DET__Gender=Fem|Number=Sing|PronType=Prs": {"morph": "Gender=Fem|Number=Sing|PronType=Prs", POS: DET},
"DET__Gender=Fem|Number=Sing|PronType=Tot": {"morph": "Gender=Fem|Number=Sing|PronType=Tot", POS: DET},
"DET__Gender=Masc|Number=Sing|Polarity=Neg|PronType=Neg": {"morph": "Gender=Masc|Number=Sing|Polarity=Neg|PronType=Neg", POS: DET},
"DET__Gender=Masc|Number=Sing|PronType=Art": {"morph": "Gender=Masc|Number=Sing|PronType=Art", POS: DET},
"DET__Gender=Masc|Number=Sing|PronType=Ind": {"morph": "Gender=Masc|Number=Sing|PronType=Ind", POS: DET},
"DET__Gender=Masc|Number=Sing|PronType=Tot": {"morph": "Gender=Masc|Number=Sing|PronType=Tot", POS: DET},
"DET__Gender=Neut|Number=Sing|Polarity=Neg|PronType=Neg": {"morph": "Gender=Neut|Number=Sing|Polarity=Neg|PronType=Neg", POS: DET},
"DET__Gender=Neut|Number=Sing|PronType=Art": {"morph": "Gender=Neut|Number=Sing|PronType=Art", POS: DET},
"DET__Gender=Neut|Number=Sing|PronType=Dem,Ind": {"morph": "Gender=Neut|Number=Sing|PronType=Dem,Ind", POS: DET},
"DET__Gender=Neut|Number=Sing|PronType=Ind": {"morph": "Gender=Neut|Number=Sing|PronType=Ind", POS: DET},
"DET__Gender=Neut|Number=Sing|PronType=Tot": {"morph": "Gender=Neut|Number=Sing|PronType=Tot", POS: DET},
"DET__Number=Plur|Polarity=Neg|PronType=Neg": {"morph": "Number=Plur|Polarity=Neg|PronType=Neg", POS: DET},
"DET__Definite=Ind|Gender=Fem|Number=Sing|PronType=Prs": {
"morph": "Definite=Ind|Gender=Fem|Number=Sing|PronType=Prs",
POS: DET,
},
"DET__Definite=Ind|Gender=Masc|Number=Sing|PronType=Prs": {
"morph": "Definite=Ind|Gender=Masc|Number=Sing|PronType=Prs",
POS: DET,
},
"DET__Definite=Ind|Gender=Neut|Number=Sing|PronType=Prs": {
"morph": "Definite=Ind|Gender=Neut|Number=Sing|PronType=Prs",
POS: DET,
},
"DET__Gender=Fem|Number=Sing|PronType=Art": {
"morph": "Gender=Fem|Number=Sing|PronType=Art",
POS: DET,
},
"DET__Gender=Fem|Number=Sing|PronType=Ind": {
"morph": "Gender=Fem|Number=Sing|PronType=Ind",
POS: DET,
},
"DET__Gender=Fem|Number=Sing|PronType=Prs": {
"morph": "Gender=Fem|Number=Sing|PronType=Prs",
POS: DET,
},
"DET__Gender=Fem|Number=Sing|PronType=Tot": {
"morph": "Gender=Fem|Number=Sing|PronType=Tot",
POS: DET,
},
"DET__Gender=Masc|Number=Sing|Polarity=Neg|PronType=Neg": {
"morph": "Gender=Masc|Number=Sing|Polarity=Neg|PronType=Neg",
POS: DET,
},
"DET__Gender=Masc|Number=Sing|PronType=Art": {
"morph": "Gender=Masc|Number=Sing|PronType=Art",
POS: DET,
},
"DET__Gender=Masc|Number=Sing|PronType=Ind": {
"morph": "Gender=Masc|Number=Sing|PronType=Ind",
POS: DET,
},
"DET__Gender=Masc|Number=Sing|PronType=Tot": {
"morph": "Gender=Masc|Number=Sing|PronType=Tot",
POS: DET,
},
"DET__Gender=Neut|Number=Sing|Polarity=Neg|PronType=Neg": {
"morph": "Gender=Neut|Number=Sing|Polarity=Neg|PronType=Neg",
POS: DET,
},
"DET__Gender=Neut|Number=Sing|PronType=Art": {
"morph": "Gender=Neut|Number=Sing|PronType=Art",
POS: DET,
},
"DET__Gender=Neut|Number=Sing|PronType=Dem,Ind": {
"morph": "Gender=Neut|Number=Sing|PronType=Dem,Ind",
POS: DET,
},
"DET__Gender=Neut|Number=Sing|PronType=Ind": {
"morph": "Gender=Neut|Number=Sing|PronType=Ind",
POS: DET,
},
"DET__Gender=Neut|Number=Sing|PronType=Tot": {
"morph": "Gender=Neut|Number=Sing|PronType=Tot",
POS: DET,
},
"DET__Number=Plur|Polarity=Neg|PronType=Neg": {
"morph": "Number=Plur|Polarity=Neg|PronType=Neg",
POS: DET,
},
"DET__Number=Plur|PronType=Art": {"morph": "Number=Plur|PronType=Art", POS: DET},
"DET__Number=Plur|PronType=Ind": {"morph": "Number=Plur|PronType=Ind", POS: DET},
"DET__Number=Plur|PronType=Prs": {"morph": "Number=Plur|PronType=Prs", POS: DET},
@ -507,57 +579,183 @@ TAG_MAP = {
"DET__PronType=Prs": {"morph": "PronType=Prs", POS: DET},
"NOUN__Abbr=Yes": {"morph": "Abbr=Yes", POS: NOUN},
"NOUN__Abbr=Yes|Case=Gen": {"morph": "Abbr=Yes|Case=Gen", POS: NOUN},
"NOUN__Abbr=Yes|Definite=Def,Ind|Gender=Masc|Number=Plur,Sing": {"morph": "Abbr=Yes|Definite=Def,Ind|Gender=Masc|Number=Plur,Sing", POS: NOUN},
"NOUN__Abbr=Yes|Definite=Def,Ind|Gender=Masc|Number=Sing": {"morph": "Abbr=Yes|Definite=Def,Ind|Gender=Masc|Number=Sing", POS: NOUN},
"NOUN__Abbr=Yes|Definite=Def,Ind|Gender=Neut|Number=Plur,Sing": {"morph": "Abbr=Yes|Definite=Def,Ind|Gender=Neut|Number=Plur,Sing", POS: NOUN},
"NOUN__Abbr=Yes|Definite=Def,Ind|Gender=Masc|Number=Plur,Sing": {
"morph": "Abbr=Yes|Definite=Def,Ind|Gender=Masc|Number=Plur,Sing",
POS: NOUN,
},
"NOUN__Abbr=Yes|Definite=Def,Ind|Gender=Masc|Number=Sing": {
"morph": "Abbr=Yes|Definite=Def,Ind|Gender=Masc|Number=Sing",
POS: NOUN,
},
"NOUN__Abbr=Yes|Definite=Def,Ind|Gender=Neut|Number=Plur,Sing": {
"morph": "Abbr=Yes|Definite=Def,Ind|Gender=Neut|Number=Plur,Sing",
POS: NOUN,
},
"NOUN__Abbr=Yes|Gender=Masc": {"morph": "Abbr=Yes|Gender=Masc", POS: NOUN},
"NUM__Case=Gen|Number=Plur|NumType=Card": {"morph": "Case=Gen|Number=Plur|NumType=Card", POS: NUM},
"NUM__Definite=Def|Number=Sing|NumType=Card": {"morph": "Definite=Def|Number=Sing|NumType=Card", POS: NUM},
"NUM__Case=Gen|Number=Plur|NumType=Card": {
"morph": "Case=Gen|Number=Plur|NumType=Card",
POS: NUM,
},
"NUM__Definite=Def|Number=Sing|NumType=Card": {
"morph": "Definite=Def|Number=Sing|NumType=Card",
POS: NUM,
},
"NUM__Definite=Def|NumType=Card": {"morph": "Definite=Def|NumType=Card", POS: NUM},
"NUM__Gender=Fem|Number=Sing|NumType=Card": {"morph": "Gender=Fem|Number=Sing|NumType=Card", POS: NUM},
"NUM__Gender=Masc|Number=Sing|NumType=Card": {"morph": "Gender=Masc|Number=Sing|NumType=Card", POS: NUM},
"NUM__Gender=Neut|Number=Sing|NumType=Card": {"morph": "Gender=Neut|Number=Sing|NumType=Card", POS: NUM},
"NUM__Gender=Fem|Number=Sing|NumType=Card": {
"morph": "Gender=Fem|Number=Sing|NumType=Card",
POS: NUM,
},
"NUM__Gender=Masc|Number=Sing|NumType=Card": {
"morph": "Gender=Masc|Number=Sing|NumType=Card",
POS: NUM,
},
"NUM__Gender=Neut|Number=Sing|NumType=Card": {
"morph": "Gender=Neut|Number=Sing|NumType=Card",
POS: NUM,
},
"NUM__Number=Plur|NumType=Card": {"morph": "Number=Plur|NumType=Card", POS: NUM},
"NUM__Number=Sing|NumType=Card": {"morph": "Number=Sing|NumType=Card", POS: NUM},
"NUM__NumType=Card": {"morph": "NumType=Card", POS: NUM},
"PART__Polarity=Neg": {"morph": "Polarity=Neg", POS: PART},
"PRON__Animacy=Hum|Case=Acc|Gender=Fem|Number=Sing|Person=3|PronType=Prs": { "morph": "Animacy=Hum|Case=Acc|Gender=Fem|Number=Sing|Person=3|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Acc|Gender=Masc|Number=Sing|Person=3|PronType=Prs": { "morph": "Animacy=Hum|Case=Acc|Gender=Masc|Number=Sing|Person=3|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Acc|Number=Plur|Person=1|PronType=Prs": {"morph": "Animacy=Hum|Case=Acc|Number=Plur|Person=1|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Acc|Number=Plur|Person=2|PronType=Prs": {"morph": "Animacy=Hum|Case=Acc|Number=Plur|Person=2|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Acc|Number=Sing|Person=1|PronType=Prs": {"morph": "Animacy=Hum|Case=Acc|Number=Sing|Person=1|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Acc|Number=Sing|Person=2|PronType=Prs": {"morph": "Animacy=Hum|Case=Acc|Number=Sing|Person=2|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Gen,Nom|Number=Sing|PronType=Art,Prs": {"morph": "Animacy=Hum|Case=Gen,Nom|Number=Sing|PronType=Art,Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Gen|Number=Sing|PronType=Art,Prs": {"morph": "Animacy=Hum|Case=Gen|Number=Sing|PronType=Art,Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Nom|Gender=Fem|Number=Sing|Person=3|PronType=Prs": { "morph": "Animacy=Hum|Case=Nom|Gender=Fem|Number=Sing|Person=3|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Nom|Gender=Masc|Number=Sing|Person=3|PronType=Prs": { "morph": "Animacy=Hum|Case=Nom|Gender=Masc|Number=Sing|Person=3|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Nom|Number=Plur|Person=1|PronType=Prs": {"morph": "Animacy=Hum|Case=Nom|Number=Plur|Person=1|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Nom|Number=Plur|Person=2|PronType=Prs": {"morph": "Animacy=Hum|Case=Nom|Number=Plur|Person=2|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Nom|Number=Sing|Person=1|PronType=Prs": {"morph": "Animacy=Hum|Case=Nom|Number=Sing|Person=1|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Nom|Number=Sing|Person=2|PronType=Prs": {"morph": "Animacy=Hum|Case=Nom|Number=Sing|Person=2|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Case=Nom|Number=Sing|PronType=Prs": {"morph": "Animacy=Hum|Case=Nom|Number=Sing|PronType=Prs", POS: PRON},
"PRON__Animacy=Hum|Number=Plur|PronType=Rcp": {"morph": "Animacy=Hum|Number=Plur|PronType=Rcp", POS: PRON},
"PRON__Animacy=Hum|Number=Sing|PronType=Art,Prs": {"morph": "Animacy=Hum|Number=Sing|PronType=Art,Prs", POS: PRON},
"PRON__Animacy=Hum|Poss=Yes|PronType=Int": {"morph": "Animacy=Hum|Poss=Yes|PronType=Int", POS: PRON},
"PRON__Animacy=Hum|Case=Acc|Gender=Fem|Number=Sing|Person=3|PronType=Prs": {
"morph": "Animacy=Hum|Case=Acc|Gender=Fem|Number=Sing|Person=3|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Acc|Gender=Masc|Number=Sing|Person=3|PronType=Prs": {
"morph": "Animacy=Hum|Case=Acc|Gender=Masc|Number=Sing|Person=3|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Acc|Number=Plur|Person=1|PronType=Prs": {
"morph": "Animacy=Hum|Case=Acc|Number=Plur|Person=1|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Acc|Number=Plur|Person=2|PronType=Prs": {
"morph": "Animacy=Hum|Case=Acc|Number=Plur|Person=2|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Acc|Number=Sing|Person=1|PronType=Prs": {
"morph": "Animacy=Hum|Case=Acc|Number=Sing|Person=1|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Acc|Number=Sing|Person=2|PronType=Prs": {
"morph": "Animacy=Hum|Case=Acc|Number=Sing|Person=2|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Gen,Nom|Number=Sing|PronType=Art,Prs": {
"morph": "Animacy=Hum|Case=Gen,Nom|Number=Sing|PronType=Art,Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Gen|Number=Sing|PronType=Art,Prs": {
"morph": "Animacy=Hum|Case=Gen|Number=Sing|PronType=Art,Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Nom|Gender=Fem|Number=Sing|Person=3|PronType=Prs": {
"morph": "Animacy=Hum|Case=Nom|Gender=Fem|Number=Sing|Person=3|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Nom|Gender=Masc|Number=Sing|Person=3|PronType=Prs": {
"morph": "Animacy=Hum|Case=Nom|Gender=Masc|Number=Sing|Person=3|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Nom|Number=Plur|Person=1|PronType=Prs": {
"morph": "Animacy=Hum|Case=Nom|Number=Plur|Person=1|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Nom|Number=Plur|Person=2|PronType=Prs": {
"morph": "Animacy=Hum|Case=Nom|Number=Plur|Person=2|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Nom|Number=Sing|Person=1|PronType=Prs": {
"morph": "Animacy=Hum|Case=Nom|Number=Sing|Person=1|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Nom|Number=Sing|Person=2|PronType=Prs": {
"morph": "Animacy=Hum|Case=Nom|Number=Sing|Person=2|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Case=Nom|Number=Sing|PronType=Prs": {
"morph": "Animacy=Hum|Case=Nom|Number=Sing|PronType=Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Number=Plur|PronType=Rcp": {
"morph": "Animacy=Hum|Number=Plur|PronType=Rcp",
POS: PRON,
},
"PRON__Animacy=Hum|Number=Sing|PronType=Art,Prs": {
"morph": "Animacy=Hum|Number=Sing|PronType=Art,Prs",
POS: PRON,
},
"PRON__Animacy=Hum|Poss=Yes|PronType=Int": {
"morph": "Animacy=Hum|Poss=Yes|PronType=Int",
POS: PRON,
},
"PRON__Animacy=Hum|PronType=Int": {"morph": "Animacy=Hum|PronType=Int", POS: PRON},
"PRON__Case=Acc|PronType=Prs|Reflex=Yes": {"morph": "Case=Acc|PronType=Prs|Reflex=Yes", POS: PRON},
"PRON__Gender=Fem,Masc|Number=Sing|Person=3|Polarity=Neg|PronType=Neg,Prs": { "morph": "Gender=Fem,Masc|Number=Sing|Person=3|Polarity=Neg|PronType=Neg,Prs", POS: PRON},
"PRON__Gender=Fem,Masc|Number=Sing|Person=3|PronType=Ind,Prs": {"morph": "Gender=Fem,Masc|Number=Sing|Person=3|PronType=Ind,Prs", POS: PRON},
"PRON__Gender=Fem,Masc|Number=Sing|Person=3|PronType=Prs,Tot": {"morph": "Gender=Fem,Masc|Number=Sing|Person=3|PronType=Prs,Tot", POS: PRON},
"PRON__Gender=Fem|Number=Sing|Poss=Yes|PronType=Prs": {"morph": "Gender=Fem|Number=Sing|Poss=Yes|PronType=Prs", POS: PRON},
"PRON__Gender=Masc|Number=Sing|Poss=Yes|PronType=Prs": {"morph": "Gender=Masc|Number=Sing|Poss=Yes|PronType=Prs", POS: PRON},
"PRON__Gender=Neut|Number=Sing|Person=3|PronType=Ind,Prs": {"morph": "Gender=Neut|Number=Sing|Person=3|PronType=Ind,Prs", POS: PRON},
"PRON__Gender=Neut|Number=Sing|Poss=Yes|PronType=Prs": {"morph": "Gender=Neut|Number=Sing|Poss=Yes|PronType=Prs", POS: PRON},
"PRON__Number=Plur|Person=3|Polarity=Neg|PronType=Neg,Prs": {"morph": "Number=Plur|Person=3|Polarity=Neg|PronType=Neg,Prs", POS: PRON},
"PRON__Number=Plur|Person=3|PronType=Ind,Prs": {"morph": "Number=Plur|Person=3|PronType=Ind,Prs", POS: PRON},
"PRON__Number=Plur|Person=3|PronType=Prs,Tot": {"morph": "Number=Plur|Person=3|PronType=Prs,Tot", POS: PRON},
"PRON__Number=Plur|Poss=Yes|PronType=Prs": {"morph": "Number=Plur|Poss=Yes|PronType=Prs", POS: PRON},
"PRON__Number=Plur|Poss=Yes|PronType=Rcp": {"morph": "Number=Plur|Poss=Yes|PronType=Rcp", POS: PRON},
"PRON__Number=Sing|Polarity=Neg|PronType=Neg": {"morph": "Number=Sing|Polarity=Neg|PronType=Neg", POS: PRON},
"PRON__Case=Acc|PronType=Prs|Reflex=Yes": {
"morph": "Case=Acc|PronType=Prs|Reflex=Yes",
POS: PRON,
},
"PRON__Gender=Fem,Masc|Number=Sing|Person=3|Polarity=Neg|PronType=Neg,Prs": {
"morph": "Gender=Fem,Masc|Number=Sing|Person=3|Polarity=Neg|PronType=Neg,Prs",
POS: PRON,
},
"PRON__Gender=Fem,Masc|Number=Sing|Person=3|PronType=Ind,Prs": {
"morph": "Gender=Fem,Masc|Number=Sing|Person=3|PronType=Ind,Prs",
POS: PRON,
},
"PRON__Gender=Fem,Masc|Number=Sing|Person=3|PronType=Prs,Tot": {
"morph": "Gender=Fem,Masc|Number=Sing|Person=3|PronType=Prs,Tot",
POS: PRON,
},
"PRON__Gender=Fem|Number=Sing|Poss=Yes|PronType=Prs": {
"morph": "Gender=Fem|Number=Sing|Poss=Yes|PronType=Prs",
POS: PRON,
},
"PRON__Gender=Masc|Number=Sing|Poss=Yes|PronType=Prs": {
"morph": "Gender=Masc|Number=Sing|Poss=Yes|PronType=Prs",
POS: PRON,
},
"PRON__Gender=Neut|Number=Sing|Person=3|PronType=Ind,Prs": {
"morph": "Gender=Neut|Number=Sing|Person=3|PronType=Ind,Prs",
POS: PRON,
},
"PRON__Gender=Neut|Number=Sing|Poss=Yes|PronType=Prs": {
"morph": "Gender=Neut|Number=Sing|Poss=Yes|PronType=Prs",
POS: PRON,
},
"PRON__Number=Plur|Person=3|Polarity=Neg|PronType=Neg,Prs": {
"morph": "Number=Plur|Person=3|Polarity=Neg|PronType=Neg,Prs",
POS: PRON,
},
"PRON__Number=Plur|Person=3|PronType=Ind,Prs": {
"morph": "Number=Plur|Person=3|PronType=Ind,Prs",
POS: PRON,
},
"PRON__Number=Plur|Person=3|PronType=Prs,Tot": {
"morph": "Number=Plur|Person=3|PronType=Prs,Tot",
POS: PRON,
},
"PRON__Number=Plur|Poss=Yes|PronType=Prs": {
"morph": "Number=Plur|Poss=Yes|PronType=Prs",
POS: PRON,
},
"PRON__Number=Plur|Poss=Yes|PronType=Rcp": {
"morph": "Number=Plur|Poss=Yes|PronType=Rcp",
POS: PRON,
},
"PRON__Number=Sing|Polarity=Neg|PronType=Neg": {
"morph": "Number=Sing|Polarity=Neg|PronType=Neg",
POS: PRON,
},
"PRON__PronType=Prs": {"morph": "PronType=Prs", POS: PRON},
"PRON__PronType=Rel": {"morph": "PronType=Rel", POS: PRON},
"PROPN__Abbr=Yes": {"morph": "Abbr=Yes", POS: PROPN},
"PROPN__Abbr=Yes|Case=Gen": {"morph": "Abbr=Yes|Case=Gen", POS: PROPN},
"VERB__Abbr=Yes|Mood=Ind|Tense=Pres|VerbForm=Fin": {"morph": "Abbr=Yes|Mood=Ind|Tense=Pres|VerbForm=Fin", POS: VERB},
"VERB__Definite=Ind|Number=Sing|VerbForm=Part": {"morph": "Definite=Ind|Number=Sing|VerbForm=Part", POS: VERB},
"VERB__Abbr=Yes|Mood=Ind|Tense=Pres|VerbForm=Fin": {
"morph": "Abbr=Yes|Mood=Ind|Tense=Pres|VerbForm=Fin",
POS: VERB,
},
"VERB__Definite=Ind|Number=Sing|VerbForm=Part": {
"morph": "Definite=Ind|Number=Sing|VerbForm=Part",
POS: VERB,
},
}

24
spacy/lang/yo/__init__.py Normal file
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@ -0,0 +1,24 @@
# coding: utf8
from __future__ import unicode_literals
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from ...language import Language
from ...attrs import LANG
class YorubaDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters.update(LEX_ATTRS)
lex_attr_getters[LANG] = lambda text: "yo"
stop_words = STOP_WORDS
tokenizer_exceptions = BASE_EXCEPTIONS
class Yoruba(Language):
lang = "yo"
Defaults = YorubaDefaults
__all__ = ["Yoruba"]

26
spacy/lang/yo/examples.py Normal file
View File

@ -0,0 +1,26 @@
# coding: utf8
from __future__ import unicode_literals
"""
Example sentences to test spaCy and its language models.
>>> from spacy.lang.yo.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
# 1. https://yo.wikipedia.org/wiki/Wikipedia:%C3%80y%E1%BB%8Dk%C3%A0_p%C3%A0t%C3%A0k%C3%AC
# 2.https://yo.wikipedia.org/wiki/Oj%C3%BAew%C3%A9_%C3%80k%E1%BB%8D%CC%81k%E1%BB%8D%CC%81
# 3. https://www.bbc.com/yoruba
sentences = [
"Ìjọba Tanzania fi Ajìjàgbara Ọmọ Orílẹ̀-èdèe Uganda sí àtìmọ́lé",
"Olúṣẹ́gun Ọbásanjọ́, tí ó jẹ́ Ààrẹ ìjọba ológun àná (láti ọdún 1976 sí 1979), tí ó sì tún ṣe Ààrẹ ìjọba alágbádá tí ìbò gbé wọlé (ní ọdún 1999 sí 2007), kúndùn láti máa bu ẹnu àtẹ́ lu àwọn "
"ètò ìjọba Ààrẹ orílẹ̀-èdè Nàìjíríà tí ó jẹ tẹ̀lé e.",
"Akin Alabi rán ẹnu mọ́ agbárá Adárí Òsìsẹ̀, àwọn ọmọ Nàìjíríà dẹnu bò ó",
"Ta ló leè dúró s'ẹ́gbẹ̀ẹ́ Okunnu láì rẹ́rìín?",
"Dídarapọ̀ mọ́n ìpolongo",
"Bi a se n so, omobinrin ni oruko ni ojo kejo bee naa ni omokunrin ni oruko ni ojo kesan.",
"Oríṣìíríṣìí nǹkan ló le yọrí sí orúkọ tí a sọ ọmọ",
"Gbogbo won ni won ni oriki ti won",
]

115
spacy/lang/yo/lex_attrs.py Normal file
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@ -0,0 +1,115 @@
# coding: utf8
from __future__ import unicode_literals
import unicodedata
from ...attrs import LIKE_NUM
_num_words = [
"ení",
"oókàn",
"ọ̀kanlá",
"ẹ́ẹdọ́gbọ̀n",
"àádọ́fà",
"ẹ̀walélúɡba",
"egbèje",
"ẹgbàárin",
"èjì",
"eéjì",
"èjìlá",
"ọgbọ̀n,",
"ọgọ́fà",
"ọ̀ọ́dúrún",
"ẹgbẹ̀jọ",
"ẹ̀ẹ́dẹ́ɡbàárùn",
"ẹ̀ta",
"ẹẹ́ta",
"ẹ̀talá",
"aárùndílogójì",
"àádóje",
"irinwó",
"ẹgbẹ̀sàn",
"ẹgbàárùn",
"ẹ̀rin",
"ẹẹ́rin",
"ẹ̀rinlá",
"ogójì",
"ogóje",
"ẹ̀ẹ́dẹ́gbẹ̀ta",
"ẹgbàá",
"ẹgbàájọ",
"àrún",
"aárùn",
"ẹ́ẹdógún",
"àádọ́ta",
"àádọ́jọ",
"ẹgbẹ̀ta",
"ẹgboókànlá",
"ẹgbàawǎ",
"ẹ̀fà",
"ẹẹ́fà",
"ẹẹ́rìndílógún",
"ọgọ́ta",
"ọgọ́jọ",
"ọ̀ọ́dẹ́gbẹ̀rin",
"ẹgbẹ́ẹdógún",
"ọkẹ́marun",
"èje",
"etàdílógún",
"àádọ́rin",
"àádọ́sán",
"ẹgbẹ̀rin",
"ẹgbàajì",
"ẹgbẹ̀ẹgbẹ̀rún",
"ẹ̀jọ",
"ẹẹ́jọ",
"eéjìdílógún",
"ọgọ́rin",
"ọgọsàn",
"ẹ̀ẹ́dẹ́gbẹ̀rún",
"ẹgbẹ́ẹdọ́gbọ̀n",
"ọgọ́rùn ọkẹ́",
"ẹ̀sán",
"ẹẹ́sàn",
"oókàndílógún",
"àádọ́rùn",
"ẹ̀wadilúɡba",
"ẹgbẹ̀rún",
"ẹgbàáta",
"ẹ̀wá",
"ẹẹ́wàá",
"ogún",
"ọgọ́rùn",
"igba",
"ẹgbẹ̀fà",
"ẹ̀ẹ́dẹ́ɡbarin",
]
def strip_accents_text(text):
"""
Converts the string to NFD, separates & returns only the base characters
:param text:
:return: input string without diacritic adornments on base characters
"""
return "".join(
c for c in unicodedata.normalize("NFD", text) if unicodedata.category(c) != "Mn"
)
def like_num(text):
text = text.replace(",", "").replace(".", "")
num_markers = ["dí", "dọ", "lé", "dín", "di", "din", "le", "do"]
if any(mark in text for mark in num_markers):
return True
text = strip_accents_text(text)
_num_words_stripped = [strip_accents_text(num) for num in _num_words]
if text.isdigit():
return True
if text in _num_words_stripped or text.lower() in _num_words_stripped:
return True
return False
LEX_ATTRS = {LIKE_NUM: like_num}

12
spacy/lang/yo/stop_words.py Normal file
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@ -0,0 +1,12 @@
# coding: utf8
from __future__ import unicode_literals
# stop words as whitespace-separated list.
# Source: https://raw.githubusercontent.com/dohliam/more-stoplists/master/yo/yo.txt
STOP_WORDS = set(
"a an b bá bí bẹ̀rẹ̀ d e f fún fẹ́ g gbogbo i inú j jù jẹ jẹ́ k kan kì kí kò "
"l láti lè lọ m mi mo máa mọ̀ n ni náà ní nígbà nítorí nǹkan o p padà pé "
"púpọ̀ pẹ̀lú r rẹ̀ s sì sí sínú t ti tí u w wà wá wọn wọ́n y yìí à àti àwọn á "
"è é ì í ò òun ó ù ú ń ńlá ǹ ̀ ́ ̣ ṣ ṣe ṣé ṣùgbọ́n ẹ ẹmọ́ ọ ọjọ́ ọ̀pọ̀lọpọ̀".split()
)

5
spacy/pipeline/entityruler.py
View File

@ -295,10 +295,9 @@ class EntityRuler(object):
deserializers_patterns = {
"patterns": lambda p: self.add_patterns(
srsly.read_jsonl(p.with_suffix(".jsonl"))
)}
deserializers_cfg = {
"cfg": lambda p: cfg.update(srsly.read_json(p))
)
}
deserializers_cfg = {"cfg": lambda p: cfg.update(srsly.read_json(p))}
from_disk(path, deserializers_cfg, {})
self.overwrite = cfg.get("overwrite", False)
self.phrase_matcher_attr = cfg.get("phrase_matcher_attr")

5
spacy/tests/conftest.py
View File

@ -220,6 +220,11 @@ def ur_tokenizer():
return get_lang_class("ur").Defaults.create_tokenizer()
@pytest.fixture(scope="session")
def yo_tokenizer():
return get_lang_class("yo").Defaults.create_tokenizer()
@pytest.fixture(scope="session")
def zh_tokenizer():
pytest.importorskip("jieba")

2
spacy/tests/lang/fi/test_tokenizer.py
View File

@ -15,7 +15,7 @@ ABBREVIATION_TESTS = [
HYPHENATED_TESTS = [
(
"1700-luvulle sijoittuva taide-elokuva",
["1700-luvulle", "sijoittuva", "taide-elokuva"]
["1700-luvulle", "sijoittuva", "taide-elokuva"],
)
]

4
spacy/tests/lang/lb/test_exceptions.py
View File

@ -3,16 +3,19 @@ from __future__ import unicode_literals
import pytest
@pytest.mark.parametrize("text", ["z.B.", "Jan."])
def test_lb_tokenizer_handles_abbr(lb_tokenizer, text):
tokens = lb_tokenizer(text)
assert len(tokens) == 1
@pytest.mark.parametrize("text", ["d'Saach", "d'Kanner", "d’Welt", "d’Suen"])
def test_lb_tokenizer_splits_contractions(lb_tokenizer, text):
tokens = lb_tokenizer(text)
assert len(tokens) == 2
def test_lb_tokenizer_handles_exc_in_text(lb_tokenizer):
text = "Mee 't ass net evident, d'Liewen."
tokens = lb_tokenizer(text)
@ -20,6 +23,7 @@ def test_lb_tokenizer_handles_exc_in_text(lb_tokenizer):
assert tokens[1].text == "'t"
assert tokens[1].lemma_ == "et"
@pytest.mark.parametrize("text,norm", [("dass", "datt"), ("viläicht", "vläicht")])
def test_lb_norm_exceptions(lb_tokenizer, text, norm):
tokens = lb_tokenizer(text)

2
spacy/tests/lang/lb/test_text.py
View File

@ -16,7 +16,7 @@ def test_lb_tokenizer_handles_long_text(lb_tokenizer):
[
("»Wat ass mat mir geschitt?«, huet hie geduecht.", 13),
("“Dëst fréi Opstoen”, denkt hien, “mécht ee ganz duercherneen. ", 15),
("Am Grand-Duché ass d'Liewen schéin, mee 't gëtt ze vill Autoen.", 14)
("Am Grand-Duché ass d'Liewen schéin, mee 't gëtt ze vill Autoen.", 14),
],
)
def test_lb_tokenizer_handles_examples(lb_tokenizer, text, length):

2
spacy/tests/lang/test_initialize.py
View File

@ -11,7 +11,7 @@ from spacy.util import get_lang_class
LANGUAGES = ["af", "ar", "bg", "bn", "ca", "cs", "da", "de", "el", "en", "es",
"et", "fa", "fi", "fr", "ga", "he", "hi", "hr", "hu", "id", "is",
"it", "kn", "lt", "lv", "nb", "nl", "pl", "pt", "ro", "si", "sk",
"sl", "sq", "sr", "sv", "ta", "te", "tl", "tr", "tt", "ur"]
"sl", "sq", "sr", "sv", "ta", "te", "tl", "tr", "tt", "ur", 'yo']
# fmt: on

0
spacy/tests/lang/yo/__init__.py Normal file
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32
spacy/tests/lang/yo/test_text.py Normal file
View File

@ -0,0 +1,32 @@
# coding: utf8
from __future__ import unicode_literals
import pytest
from spacy.lang.yo.lex_attrs import like_num
def test_yo_tokenizer_handles_long_text(yo_tokenizer):
text = """Àwọn ọmọ ìlú tí wọ́n ń ṣàmúlò ayélujára ti bẹ̀rẹ̀ ìkọkúkọ sórí àwòrán ààrẹ Nkurunziza nínú ìfẹ̀hónúhàn pẹ̀lú àmì ìdámọ̀: Nkurunziza àti Burundi:
Ọmọ ilé ẹ̀kọ́ gíga ní ẹ̀wọ̀n fún kíkọ ìkọkúkọ sí orí àwòrán Ààrẹ .
Bí mo bá ṣe èyí ní Burundi , ó ṣe é ṣe kí a fi mí sí àtìmọ́lé
Ìjọba Burundi fi akẹ́kọ̀ọ́bìnrin sí àtìmọ́lé látàrí ẹ̀sùn ìkọkúkọ sí orí àwòrán ààrẹ. A túwíìtì àwòrán ìkọkúkọ wa ní ìbánikẹ́dùn ìṣẹ̀lẹ̀ náà.
Wọ́n ní kí a dán an wò, kí a kọ nǹkan sí orí àwòrán ààrẹ mo sì ṣe bẹ́ẹ̀. Mo ní ìgbóyà wípé ẹnikẹ́ni kò ní mú mi níbí.
Ìfòfinlíle mú àtakò"""
tokens = yo_tokenizer(text)
assert len(tokens) == 121
@pytest.mark.parametrize(
"text,match",
[("ení", True), ("ogun", True), ("mewadinlogun", True), ("ten", False)],
)
def test_lex_attrs_like_number(yo_tokenizer, text, match):
tokens = yo_tokenizer(text)
assert len(tokens) == 1
assert tokens[0].like_num == match
@pytest.mark.parametrize("word", ["eji", "ejila", "ogun", "aárùn"])
def test_yo_lex_attrs_capitals(word):
assert like_num(word)
assert like_num(word.upper())

10
spacy/tests/parser/test_parse.py
View File

@ -151,17 +151,17 @@ def test_parser_arc_eager_finalize_state(en_tokenizer, en_parser):
def test_parser_set_sent_starts(en_vocab):
# fmt: off
words = ['Ein', 'Satz', '.', 'Außerdem', 'ist', 'Zimmer', 'davon', 'überzeugt', ',', 'dass', 'auch', 'epige-', '\n', 'netische', 'Mechanismen', 'eine', 'Rolle', 'spielen', ',', 'also', 'Vorgänge', ',', 'die', '\n', 'sich', 'darauf', 'auswirken', ',', 'welche', 'Gene', 'abgelesen', 'werden', 'und', '\n', 'welche', 'nicht', '.', '\n']
heads = [1, 0, -1, 27, 0, -1, 1, -3, -1, 8, 4, 3, -1, 1, 3, 1, 1, -11, -1, 1, -9, -1, 4, -1, 2, 1, -6, -1, 1, 2, 1, -6, -1, -1, -17, -31, -32, -1]
deps = ['nk', 'ROOT', 'punct', 'mo', 'ROOT', 'sb', 'op', 'pd', 'punct', 'cp', 'mo', 'nk', '', 'nk', 'sb', 'nk', 'oa', 're', 'punct', 'mo', 'app', 'punct', 'sb', '', 'oa', 'op', 'rc', 'punct', 'nk', 'sb', 'oc', 're', 'cd', '', 'oa', 'ng', 'punct', '']
doc = get_doc(
en_vocab, words=words, deps=deps, heads=heads
)
# fmt: on
doc = get_doc(en_vocab, words=words, deps=deps, heads=heads)
for i in range(len(words)):
if i == 0 or i == 3:
assert doc[i].is_sent_start == True
assert doc[i].is_sent_start is True
else:
assert doc[i].is_sent_start == None
assert doc[i].is_sent_start is None
for sent in doc.sents:
for token in sent:
assert token.head in sent

1
spacy/tests/pipeline/test_tagger.py
View File

@ -3,7 +3,6 @@ from __future__ import unicode_literals
import pytest
from spacy.language import Language
from spacy.pipeline import Tagger
def test_label_types():

13
spacy/tests/regression/test_issue4674.py
View File

@ -1,11 +1,12 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest
from spacy.kb import KnowledgeBase
from spacy.util import ensure_path
from spacy.lang.en import English
from spacy.tests.util import make_tempdir
from ..util import make_tempdir
def test_issue4674():
@ -15,7 +16,12 @@ def test_issue4674():
vector1 = [0.9, 1.1, 1.01]
vector2 = [1.8, 2.25, 2.01]
kb.set_entities(entity_list=["Q1", "Q1"], freq_list=[32, 111], vector_list=[vector1, vector2])
with pytest.warns(UserWarning):
kb.set_entities(
entity_list=["Q1", "Q1"],
freq_list=[32, 111],
vector_list=[vector1, vector2],
)
assert kb.get_size_entities() == 1
@ -31,4 +37,3 @@ def test_issue4674():
kb2.load_bulk(str(file_path))
assert kb2.get_size_entities() == 1

4
spacy/tokens/doc.pyx
View File

@ -994,9 +994,9 @@ cdef class Doc:
order, and no span intersection is allowed.
spans (Span[]): Spans to merge, in document order, with all span
intersections empty. Cannot be emty.
intersections empty. Cannot be empty.
attributes (Dictionary[]): Attributes to assign to the merged tokens. By default,
must be the same lenghth as spans, emty dictionaries are allowed.
must be the same length as spans, empty dictionaries are allowed.
attributes are inherited from the syntactic root of the span.
RETURNS (Token): The first newly merged token.
"""

7
website/docs/api/language.md
View File

@ -77,9 +77,9 @@ more efficient than processing texts one-by-one.
Early versions of spaCy used simple statistical models that could be efficiently
multi-threaded, as we were able to entirely release Python's global interpreter
lock. The multi-threading was controlled using the `n_threads` keyword argument
to the `.pipe` method. This keyword argument is now deprecated as of v2.1.0.
Future versions may introduce a `n_process` argument for parallel inference via
multiprocessing.
to the `.pipe` method. This keyword argument is now deprecated as of v2.1.0. A
new keyword argument, `n_process`, was introduced to control parallel inference
via multiprocessing in v2.2.2.
</Infobox>
@ -98,6 +98,7 @@ multiprocessing.
| `batch_size` | int | The number of texts to buffer. |
| `disable` | list | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
| `component_cfg` <Tag variant="new">2.1</Tag> | dict | Config parameters for specific pipeline components, keyed by component name. |
| `n_process` <Tag variant="new">2.2.2</Tag> | int | Number of processors to use, only supported in Python 3. Defaults to `1`. |
| **YIELDS** | `Doc` | Documents in the order of the original text. |
## Language.update {#update tag="method"}

5
website/docs/usage/index.md
View File

@ -124,9 +124,8 @@ interface for GPU arrays.
spaCy can be installed on GPU by specifying `spacy[cuda]`, `spacy[cuda90]`,
`spacy[cuda91]`, `spacy[cuda92]` or `spacy[cuda100]`. If you know your cuda
version, using the more explicit specifier allows cupy to be installed via
wheel, saving some compilation time. The specifiers should install two
libraries: [`cupy`](https://cupy.chainer.org) and
[`thinc_gpu_ops`](https://github.com/explosion/thinc_gpu_ops).
wheel, saving some compilation time. The specifiers should install
[`cupy`](https://cupy.chainer.org).
```bash
$ pip install -U spacy[cuda92]