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.github/contributors/aajanki.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 | Antti Ajanki |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2019-11-30 |
| GitHub username | aajanki |
| Website (optional) | |

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## 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 | Nicolai Bjerre Pedersen |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | 2019-12-06 |
| GitHub username | mr_bjerre |
| Website (optional) | |

3
spacy/errors.py
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@ -72,7 +72,7 @@ class Warnings(object):
"instead.")
W014 = ("As of v2.1.0, the `disable` keyword argument on the serialization "
"methods is and should be replaced with `exclude`. This makes it "
"consistent with the other objects serializable.")
"consistent with the other serializable objects.")
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}'].")
@ -101,6 +101,7 @@ class Warnings(object):
"the Knowledge Base.")
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.")
@add_codes

5
spacy/lang/fi/__init__.py
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@ -3,6 +3,8 @@ from __future__ import unicode_literals
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_SUFFIXES
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from ..norm_exceptions import BASE_NORMS
@ -13,10 +15,13 @@ from ...util import update_exc, add_lookups
class FinnishDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters.update(LEX_ATTRS)
lex_attr_getters[LANG] = lambda text: "fi"
lex_attr_getters[NORM] = add_lookups(
Language.Defaults.lex_attr_getters[NORM], BASE_NORMS
)
infixes = TOKENIZER_INFIXES
suffixes = TOKENIZER_SUFFIXES
tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
stop_words = STOP_WORDS

3
spacy/lang/fi/lex_attrs.py
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@ -18,7 +18,8 @@ _num_words = [
"kymmenen",
"yksitoista",
"kaksitoista",
"kolmetoista" "neljätoista",
"kolmetoista",
"neljätoista",
"viisitoista",
"kuusitoista",
"seitsemäntoista",

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spacy/lang/fi/punctuation.py Normal file
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@ -0,0 +1,33 @@
# 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 ..punctuation import TOKENIZER_SUFFIXES
_quotes = CONCAT_QUOTES.replace("'", "")
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[{al}])\.(?=[{au}])".format(al=ALPHA_LOWER, au=ALPHA_UPPER),
r"(?<=[{a}])[,!?](?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])[:<>=](?=[{a}])".format(a=ALPHA),
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])([{q}\)\]\(\[])(?=[{a}])".format(a=ALPHA, q=_quotes),
r"(?<=[{a}])--(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
]
)
_suffixes = [
suffix
for suffix in TOKENIZER_SUFFIXES
if suffix not in ["'s", "'S", "’s", "’S", r"\'"]
]
TOKENIZER_INFIXES = _infixes
TOKENIZER_SUFFIXES = _suffixes

2
spacy/lang/lb/norm_exceptions.py
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@ -6,7 +6,7 @@ from __future__ import unicode_literals
# variants (vläicht = vlaicht, vleicht, viläicht, viläischt, etc. etc.)
# here one could include the most common spelling mistakes
_exc = {"datt": "dass", "wgl.": "weg.", "vläicht": "viläicht"}
_exc = {"dass": "datt", "viläicht": "vläicht"}
NORM_EXCEPTIONS = {}

23
spacy/lang/lb/punctuation.py
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@ -1,16 +1,23 @@
# coding: utf8
from __future__ import unicode_literals
from ..punctuation import TOKENIZER_INFIXES
from ..char_classes import ALPHA
from ..char_classes import LIST_ELLIPSES, LIST_ICONS
from ..char_classes import CONCAT_QUOTES, ALPHA, ALPHA_LOWER, ALPHA_UPPER
ELISION = " ' ’ ".strip().replace(" ", "")
HYPHENS = r"- – — ‐ ‑".strip().replace(" ", "")
_infixes = TOKENIZER_INFIXES + [
r"(?<=[{a}][{el}])(?=[{a}])".format(a=ALPHA, el=ELISION)
]
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[{a}][{el}])(?=[{a}])".format(a=ALPHA, el=ELISION),
r"(?<=[{al}])\.(?=[{au}])".format(al=ALPHA_LOWER, au=ALPHA_UPPER),
r"(?<=[{a}])[,!?](?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])[:<>=](?=[{a}])".format(a=ALPHA),
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}])--(?=[{a}])".format(a=ALPHA),
r"(?<=[0-9])-(?=[0-9])",
]
)
TOKENIZER_INFIXES = _infixes

6
spacy/lang/lb/tokenizer_exceptions.py
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@ -10,7 +10,9 @@ _exc = {}
# translate / delete what is not necessary
for exc_data in [
{ORTH: "wgl.", LEMMA: "wann ech gelift", NORM: "wann ech gelieft"},
{ORTH: "'t", LEMMA: "et", NORM: "et"},
{ORTH: "'T", LEMMA: "et", NORM: "et"},
{ORTH: "wgl.", LEMMA: "wannechgelift", NORM: "wannechgelift"},
{ORTH: "M.", LEMMA: "Monsieur", NORM: "Monsieur"},
{ORTH: "Mme.", LEMMA: "Madame", NORM: "Madame"},
{ORTH: "Dr.", LEMMA: "Dokter", NORM: "Dokter"},
@ -18,7 +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]

99
spacy/lang/nb/tag_map.py
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@ -1,12 +1,12 @@
# coding: utf8
from __future__ import unicode_literals
from ...symbols import POS, PUNCT, ADJ, CONJ, SCONJ, SYM, NUM, DET, ADV, ADP, X
from ...symbols import POS, PUNCT, ADJ, CONJ, CCONJ, SCONJ, SYM, NUM, DET, ADV, ADP, X
from ...symbols import VERB, NOUN, PROPN, PART, INTJ, PRON, AUX
# Tags are a combination of POS and morphological features from a yet
# unpublished dataset developed by Schibsted, Nasjonalbiblioteket and LTG. The
# Tags are a combination of POS and morphological features from a
# https://github.com/ltgoslo/norne developed by Schibsted, Nasjonalbiblioteket and LTG. The
# data format is .conllu and follows the Universal Dependencies annotation.
# (There are some annotation differences compared to this dataset:
# https://github.com/UniversalDependencies/UD_Norwegian-Bokmaal
@ -467,4 +467,97 @@ TAG_MAP = {
"VERB__VerbForm=Part": {"morph": "VerbForm=Part", POS: VERB},
"VERB___": {"morph": "_", POS: VERB},
"X___": {"morph": "_", POS: X},
'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__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__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__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},
"DET__Number=Plur|PronType=Tot": {"morph": "Number=Plur|PronType=Tot", POS: DET},
"DET__PronType=Ind": {"morph": "PronType=Ind", POS: DET},
"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|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__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__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|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__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},
}

4
spacy/matcher/matcher.pyx
View File

@ -677,7 +677,9 @@ def _get_attr_values(spec, string_store):
value = string_store.add(value)
elif isinstance(value, bool):
value = int(value)
elif isinstance(value, (dict, int)):
elif isinstance(value, int):
pass
elif isinstance(value, dict):
continue
else:
raise ValueError(Errors.E153.format(vtype=type(value).__name__))

66
spacy/pipeline/pipes.pyx
View File

@ -1302,7 +1302,7 @@ class EntityLinker(Pipe):
if len(doc) > 0:
# Looping through each sentence and each entity
# This may go wrong if there are entities across sentences - because they might not get a KB ID
for sent in doc.ents:
for sent in doc.sents:
sent_doc = sent.as_doc()
# currently, the context is the same for each entity in a sentence (should be refined)
sentence_encoding = self.model([sent_doc])[0]
@ -1464,21 +1464,59 @@ class Sentencizer(object):
DOCS: https://spacy.io/api/sentencizer#call
"""
start = 0
seen_period = False
for i, token in enumerate(doc):
is_in_punct_chars = token.text in self.punct_chars
token.is_sent_start = i == 0
if seen_period and not token.is_punct and not is_in_punct_chars:
doc[start].is_sent_start = True
start = token.i
seen_period = False
elif is_in_punct_chars:
seen_period = True
if start < len(doc):
doc[start].is_sent_start = True
tags = self.predict([doc])
self.set_annotations([doc], tags)
return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
for docs in util.minibatch(stream, size=batch_size):
docs = list(docs)
tag_ids = self.predict(docs)
self.set_annotations(docs, tag_ids)
yield from docs
def predict(self, docs):
"""Apply the pipeline's model to a batch of docs, without
modifying them.
"""
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
guesses = [[] for doc in docs]
return guesses
guesses = []
for doc in docs:
start = 0
seen_period = False
doc_guesses = [False] * len(doc)
doc_guesses[0] = True
for i, token in enumerate(doc):
is_in_punct_chars = token.text in self.punct_chars
if seen_period and not token.is_punct and not is_in_punct_chars:
doc_guesses[start] = True
start = token.i
seen_period = False
elif is_in_punct_chars:
seen_period = True
if start < len(doc):
doc_guesses[start] = True
guesses.append(doc_guesses)
return guesses
def set_annotations(self, docs, batch_tag_ids, tensors=None):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
cdef int idx = 0
for i, doc in enumerate(docs):
doc_tag_ids = batch_tag_ids[i]
for j, tag_id in enumerate(doc_tag_ids):
# Don't clobber existing sentence boundaries
if doc.c[j].sent_start == 0:
if tag_id:
doc.c[j].sent_start = 1
else:
doc.c[j].sent_start = -1
def to_bytes(self, **kwargs):
"""Serialize the sentencizer to a bytestring.

27
spacy/tests/lang/fi/test_text.py Normal file
View File

@ -0,0 +1,27 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest
@pytest.mark.parametrize(
"text,match",
[
("10", True),
("1", True),
("10000", True),
("10,00", True),
("-999,0", True),
("yksi", True),
("kolmetoista", True),
("viisikymmentä", True),
("tuhat", True),
("1/2", True),
("hevonen", False),
(",", False),
],
)
def test_fi_lex_attrs_like_number(fi_tokenizer, text, match):
tokens = fi_tokenizer(text)
assert len(tokens) == 1
assert tokens[0].like_num == match

16
spacy/tests/lang/fi/test_tokenizer.py
View File

@ -12,9 +12,23 @@ ABBREVIATION_TESTS = [
("Paino on n. 2.2 kg", ["Paino", "on", "n.", "2.2", "kg"]),
]
HYPHENATED_TESTS = [
(
"1700-luvulle sijoittuva taide-elokuva",
["1700-luvulle", "sijoittuva", "taide-elokuva"]
)
]
@pytest.mark.parametrize("text,expected_tokens", ABBREVIATION_TESTS)
def test_fi_tokenizer_handles_testcases(fi_tokenizer, text, expected_tokens):
def test_fi_tokenizer_abbreviations(fi_tokenizer, text, expected_tokens):
tokens = fi_tokenizer(text)
token_list = [token.text for token in tokens if not token.is_space]
assert expected_tokens == token_list
@pytest.mark.parametrize("text,expected_tokens", HYPHENATED_TESTS)
def test_fi_tokenizer_hyphenated_words(fi_tokenizer, text, expected_tokens):
tokens = fi_tokenizer(text)
token_list = [token.text for token in tokens if not token.is_space]
assert expected_tokens == token_list

18
spacy/tests/lang/lb/test_exceptions.py
View File

@ -3,8 +3,24 @@ 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)
assert len(tokens) == 9
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)
assert tokens[0].norm_ == norm

1
spacy/tests/lang/lb/test_text.py
View File

@ -16,6 +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)
],
)
def test_lb_tokenizer_handles_examples(lb_tokenizer, text, length):

17
spacy/tests/parser/test_parse.py
View File

@ -148,3 +148,20 @@ def test_parser_arc_eager_finalize_state(en_tokenizer, en_parser):
assert tokens[4].left_edge.i == 0
assert tokens[4].right_edge.i == 4
assert tokens[4].head.i == 4
def test_parser_set_sent_starts(en_vocab):
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
)
for i in range(len(words)):
if i == 0 or i == 3:
assert doc[i].is_sent_start == True
else:
assert doc[i].is_sent_start == None
for sent in doc.sents:
for token in sent:
assert token.head in sent

12
spacy/tests/pipeline/test_sentencizer.py
View File

@ -5,6 +5,7 @@ import pytest
import spacy
from spacy.pipeline import Sentencizer
from spacy.tokens import Doc
from spacy.lang.en import English
def test_sentencizer(en_vocab):
@ -17,6 +18,17 @@ def test_sentencizer(en_vocab):
assert len(list(doc.sents)) == 2
def test_sentencizer_pipe():
texts = ["Hello! This is a test.", "Hi! This is a test."]
nlp = English()
nlp.add_pipe(nlp.create_pipe("sentencizer"))
for doc in nlp.pipe(texts):
assert doc.is_sentenced
sent_starts = [t.is_sent_start for t in doc]
assert sent_starts == [True, False, True, False, False, False, False]
assert len(list(doc.sents)) == 2
@pytest.mark.parametrize(
"words,sent_starts,n_sents",
[

23
spacy/tests/regression/test_issue4707.py Normal file
View File

@ -0,0 +1,23 @@
# coding: utf8
from __future__ import unicode_literals
from spacy.util import load_model_from_path
from spacy.lang.en import English
from ..util import make_tempdir
def test_issue4707():
"""Tests that disabled component names are also excluded from nlp.from_disk
by default when loading a model.
"""
nlp = English()
nlp.add_pipe(nlp.create_pipe("sentencizer"))
nlp.add_pipe(nlp.create_pipe("entity_ruler"))
assert nlp.pipe_names == ["sentencizer", "entity_ruler"]
exclude = ["tokenizer", "sentencizer"]
with make_tempdir() as tmpdir:
nlp.to_disk(tmpdir, exclude=exclude)
new_nlp = load_model_from_path(tmpdir, disable=exclude)
assert "sentencizer" not in new_nlp.pipe_names
assert "entity_ruler" in new_nlp.pipe_names

8
spacy/tests/serialize/test_serialize_doc.py
View File

@ -24,6 +24,7 @@ def test_serialize_empty_doc(en_vocab):
def test_serialize_doc_roundtrip_bytes(en_vocab):
doc = Doc(en_vocab, words=["hello", "world"])
doc.cats = {"A": 0.5}
doc_b = doc.to_bytes()
new_doc = Doc(en_vocab).from_bytes(doc_b)
assert new_doc.to_bytes() == doc_b
@ -66,12 +67,17 @@ def test_serialize_doc_exclude(en_vocab):
def test_serialize_doc_bin():
doc_bin = DocBin(attrs=["LEMMA", "ENT_IOB", "ENT_TYPE"], store_user_data=True)
texts = ["Some text", "Lots of texts...", "..."]
cats = {"A": 0.5}
nlp = English()
for doc in nlp.pipe(texts):
doc.cats = cats
doc_bin.add(doc)
bytes_data = doc_bin.to_bytes()
# Deserialize later, e.g. in a new process
nlp = spacy.blank("en")
doc_bin = DocBin().from_bytes(bytes_data)
list(doc_bin.get_docs(nlp.vocab))
reloaded_docs = list(doc_bin.get_docs(nlp.vocab))
for i, doc in enumerate(reloaded_docs):
assert doc.text == texts[i]
assert doc.cats == cats

6
spacy/tokens/_serialize.py
View File

@ -58,6 +58,7 @@ class DocBin(object):
self.attrs.insert(0, ORTH) # Ensure ORTH is always attrs[0]
self.tokens = []
self.spaces = []
self.cats = []
self.user_data = []
self.strings = set()
self.store_user_data = store_user_data
@ -82,6 +83,7 @@ class DocBin(object):
spaces = spaces.reshape((spaces.shape[0], 1))
self.spaces.append(numpy.asarray(spaces, dtype=bool))
self.strings.update(w.text for w in doc)
self.cats.append(doc.cats)
if self.store_user_data:
self.user_data.append(srsly.msgpack_dumps(doc.user_data))
@ -102,6 +104,7 @@ class DocBin(object):
words = [vocab.strings[orth] for orth in tokens[:, orth_col]]
doc = Doc(vocab, words=words, spaces=spaces)
doc = doc.from_array(self.attrs, tokens)
doc.cats = self.cats[i]
if self.store_user_data:
user_data = srsly.msgpack_loads(self.user_data[i], use_list=False)
doc.user_data.update(user_data)
@ -121,6 +124,7 @@ class DocBin(object):
self.tokens.extend(other.tokens)
self.spaces.extend(other.spaces)
self.strings.update(other.strings)
self.cats.extend(other.cats)
if self.store_user_data:
self.user_data.extend(other.user_data)
@ -140,6 +144,7 @@ class DocBin(object):
"spaces": numpy.vstack(self.spaces).tobytes("C"),
"lengths": numpy.asarray(lengths, dtype="int32").tobytes("C"),
"strings": list(self.strings),
"cats": self.cats,
}
if self.store_user_data:
msg["user_data"] = self.user_data
@ -164,6 +169,7 @@ class DocBin(object):
flat_spaces = flat_spaces.reshape((flat_spaces.size, 1))
self.tokens = NumpyOps().unflatten(flat_tokens, lengths)
self.spaces = NumpyOps().unflatten(flat_spaces, lengths)
self.cats = msg["cats"]
if self.store_user_data and "user_data" in msg:
self.user_data = list(msg["user_data"])
for tokens in self.tokens:

3
spacy/tokens/doc.pxd
View File

@ -21,6 +21,9 @@ ctypedef fused LexemeOrToken:
cdef int set_children_from_heads(TokenC* tokens, int length) except -1
cdef int _set_lr_kids_and_edges(TokenC* tokens, int length, int loop_count) except -1
cdef int token_by_start(const TokenC* tokens, int length, int start_char) except -2

84
spacy/tokens/doc.pyx
View File

@ -887,6 +887,7 @@ cdef class Doc:
"array_body": lambda: self.to_array(array_head),
"sentiment": lambda: self.sentiment,
"tensor": lambda: self.tensor,
"cats": lambda: self.cats,
}
for key in kwargs:
if key in serializers or key in ("user_data", "user_data_keys", "user_data_values"):
@ -916,6 +917,7 @@ cdef class Doc:
"array_body": lambda b: None,
"sentiment": lambda b: None,
"tensor": lambda b: None,
"cats": lambda b: None,
"user_data_keys": lambda b: None,
"user_data_values": lambda b: None,
}
@ -937,6 +939,8 @@ cdef class Doc:
self.sentiment = msg["sentiment"]
if "tensor" not in exclude and "tensor" in msg:
self.tensor = msg["tensor"]
if "cats" not in exclude and "cats" in msg:
self.cats = msg["cats"]
start = 0
cdef const LexemeC* lex
cdef unicode orth_
@ -1153,35 +1157,69 @@ cdef int set_children_from_heads(TokenC* tokens, int length) except -1:
tokens[i].r_kids = 0
tokens[i].l_edge = i
tokens[i].r_edge = i
# Three times, for non-projectivity. See issue #3170. This isn't a very
# satisfying fix, but I think it's sufficient.
for loop_count in range(3):
# Set left edges
for i in range(length):
child = &tokens[i]
head = &tokens[i + child.head]
if child < head and loop_count == 0:
head.l_kids += 1
if child.l_edge < head.l_edge:
head.l_edge = child.l_edge
if child.r_edge > head.r_edge:
head.r_edge = child.r_edge
# Set right edges - same as above, but iterate in reverse
for i in range(length-1, -1, -1):
child = &tokens[i]
head = &tokens[i + child.head]
if child > head and loop_count == 0:
head.r_kids += 1
if child.r_edge > head.r_edge:
head.r_edge = child.r_edge
if child.l_edge < head.l_edge:
head.l_edge = child.l_edge
cdef int loop_count = 0
cdef bint heads_within_sents = False
# Try up to 10 iterations of adjusting lr_kids and lr_edges in order to
# handle non-projective dependency parses, stopping when all heads are
# within their respective sentence boundaries. We have documented cases
# that need at least 4 iterations, so this is to be on the safe side
# without risking getting stuck in an infinite loop if something is
# terribly malformed.
while not heads_within_sents:
heads_within_sents = _set_lr_kids_and_edges(tokens, length, loop_count)
if loop_count > 10:
user_warning(Warnings.W026)
loop_count += 1
# Set sentence starts
for i in range(length):
if tokens[i].head == 0 and tokens[i].dep != 0:
tokens[tokens[i].l_edge].sent_start = True
cdef int _set_lr_kids_and_edges(TokenC* tokens, int length, int loop_count) except -1:
# May be called multiple times due to non-projectivity. See issues #3170
# and #4688.
# Set left edges
cdef TokenC* head
cdef TokenC* child
cdef int i, j
for i in range(length):
child = &tokens[i]
head = &tokens[i + child.head]
if child < head and loop_count == 0:
head.l_kids += 1
if child.l_edge < head.l_edge:
head.l_edge = child.l_edge
if child.r_edge > head.r_edge:
head.r_edge = child.r_edge
# Set right edges - same as above, but iterate in reverse
for i in range(length-1, -1, -1):
child = &tokens[i]
head = &tokens[i + child.head]
if child > head and loop_count == 0:
head.r_kids += 1
if child.r_edge > head.r_edge:
head.r_edge = child.r_edge
if child.l_edge < head.l_edge:
head.l_edge = child.l_edge
# Get sentence start positions according to current state
sent_starts = set()
for i in range(length):
if tokens[i].head == 0 and tokens[i].dep != 0:
sent_starts.add(tokens[i].l_edge)
cdef int curr_sent_start = 0
cdef int curr_sent_end = 0
# Check whether any heads are not within the current sentence
for i in range(length):
if (i > 0 and i in sent_starts) or i == length - 1:
curr_sent_end = i
for j in range(curr_sent_start, curr_sent_end):
if tokens[j].head + j < curr_sent_start or tokens[j].head + j >= curr_sent_end + 1:
return False
curr_sent_start = i
return True
cdef int _get_tokens_lca(Token token_j, Token token_k):
"""Given two tokens, returns the index of the lowest common ancestor
(LCA) among the two. If they have no common ancestor, -1 is returned.

2
spacy/util.py
View File

@ -208,7 +208,7 @@ def load_model_from_path(model_path, meta=False, **overrides):
factory = factories.get(name, name)
component = nlp.create_pipe(factory, config=config)
nlp.add_pipe(component, name=name)
return nlp.from_disk(model_path)
return nlp.from_disk(model_path, exclude=disable)
def load_model_from_init_py(init_file, **overrides):

3
website/docs/api/cli.md
View File

@ -166,14 +166,13 @@ All output files generated by this command are compatible with
### Converter options
<!-- TODO: document jsonl option – maybe update it? -->
| ID | Description |
| ------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `auto` | Automatically pick converter based on file extension and file content (default). |
| `conll`, `conllu`, `conllubio` | Universal Dependencies `.conllu` or `.conll` format. |
| `ner` | NER with IOB/IOB2 tags, one token per line with columns separated by whitespace. The first column is the token and the final column is the IOB tag. Sentences are separated by blank lines and documents are separated by the line `-DOCSTART- -X- O O`. Supports CoNLL 2003 NER format. See [sample data](https://github.com/explosion/spaCy/tree/master/examples/training/ner_example_data). |
| `iob` | NER with IOB/IOB2 tags, one sentence per line with tokens separated by whitespace and annotation separated by `|`, either `word|B-ENT` or `word|POS|B-ENT`. See [sample data](https://github.com/explosion/spaCy/tree/master/examples/training/ner_example_data). |
| `jsonl` | NER data formatted as JSONL with one dict per line and a `"text"` and `"spans"` key. This is also the format exported by the [Prodigy](https://prodi.gy) annotation tool. See [sample data](https://raw.githubusercontent.com/explosion/projects/master/ner-fashion-brands/fashion_brands_training.jsonl). |
## Debug data {#debug-data new="2.2"}

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@ -122,44 +122,44 @@ The L2 norm of the lexeme's vector representation.
## Attributes {#attributes}
| Name | Type | Description |
| -------------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------------ |
| `vocab` | `Vocab` | The lexeme's vocabulary. |
| `text` | unicode | Verbatim text content. |
| `orth` | int | ID of the verbatim text content. |
| `orth_` | unicode | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. |
| `rank` | int | Sequential ID of the lexemes's lexical type, used to index into tables, e.g. for word vectors. |
| `flags` | int | Container of the lexeme's binary flags. |
| `norm` | int | The lexemes's norm, i.e. a normalized form of the lexeme text. |
| `norm_` | unicode | The lexemes's norm, i.e. a normalized form of the lexeme text. |
| `lower` | int | Lowercase form of the word. |
| `lower_` | unicode | Lowercase form of the word. |
| `shape` | int | Transform of the word's string, to show orthographic features. |
| `shape_` | unicode | Transform of the word's string, to show orthographic features. |
| `prefix` | int | Length-N substring from the start of the word. Defaults to `N=1`. |
| `prefix_` | unicode | Length-N substring from the start of the word. Defaults to `N=1`. |
| `suffix` | int | Length-N substring from the end of the word. Defaults to `N=3`. |
| `suffix_` | unicode | Length-N substring from the start of the word. Defaults to `N=3`. |
| `is_alpha` | bool | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. |
| `is_ascii` | bool | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. |
| `is_digit` | bool | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. |
| `is_lower` | bool | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. |
| `is_upper` | bool | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. |
| `is_title` | bool | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. |
| `is_punct` | bool | Is the lexeme punctuation? |
| `is_left_punct` | bool | Is the lexeme a left punctuation mark, e.g. `(`? |
| `is_right_punct` | bool | Is the lexeme a right punctuation mark, e.g. `)`? |
| `is_space` | bool | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. |
| `is_bracket` | bool | Is the lexeme a bracket? |
| `is_quote` | bool | Is the lexeme a quotation mark? |
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the lexeme a currency symbol? |
| `like_url` | bool | Does the lexeme resemble a URL? |
| `like_num` | bool | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. |
| `like_email` | bool | Does the lexeme resemble an email address? |
| `is_oov` | bool | Is the lexeme out-of-vocabulary? |
| `is_stop` | bool | Is the lexeme part of a "stop list"? |
| `lang` | int | Language of the parent vocabulary. |
| `lang_` | unicode | Language of the parent vocabulary. |
| `prob` | float | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). |
| `cluster` | int | Brown cluster ID. |
| `sentiment` | float | A scalar value indicating the positivity or negativity of the lexeme. |
| Name | Type | Description |
| -------------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `vocab` | `Vocab` | The lexeme's vocabulary. |
| `text` | unicode | Verbatim text content. |
| `orth` | int | ID of the verbatim text content. |
| `orth_` | unicode | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. |
| `rank` | int | Sequential ID of the lexemes's lexical type, used to index into tables, e.g. for word vectors. |
| `flags` | int | Container of the lexeme's binary flags. |
| `norm` | int | The lexemes's norm, i.e. a normalized form of the lexeme text. |
| `norm_` | unicode | The lexemes's norm, i.e. a normalized form of the lexeme text. |
| `lower` | int | Lowercase form of the word. |
| `lower_` | unicode | Lowercase form of the word. |
| `shape` | int | Transform of the words's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
| `shape_` | unicode | Transform of the word's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
| `prefix` | int | Length-N substring from the start of the word. Defaults to `N=1`. |
| `prefix_` | unicode | Length-N substring from the start of the word. Defaults to `N=1`. |
| `suffix` | int | Length-N substring from the end of the word. Defaults to `N=3`. |
| `suffix_` | unicode | Length-N substring from the start of the word. Defaults to `N=3`. |
| `is_alpha` | bool | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. |
| `is_ascii` | bool | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. |
| `is_digit` | bool | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. |
| `is_lower` | bool | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. |
| `is_upper` | bool | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. |
| `is_title` | bool | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. |
| `is_punct` | bool | Is the lexeme punctuation? |
| `is_left_punct` | bool | Is the lexeme a left punctuation mark, e.g. `(`? |
| `is_right_punct` | bool | Is the lexeme a right punctuation mark, e.g. `)`? |
| `is_space` | bool | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. |
| `is_bracket` | bool | Is the lexeme a bracket? |
| `is_quote` | bool | Is the lexeme a quotation mark? |
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the lexeme a currency symbol? |
| `like_url` | bool | Does the lexeme resemble a URL? |
| `like_num` | bool | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. |
| `like_email` | bool | Does the lexeme resemble an email address? |
| `is_oov` | bool | Is the lexeme out-of-vocabulary? |
| `is_stop` | bool | Is the lexeme part of a "stop list"? |
| `lang` | int | Language of the parent vocabulary. |
| `lang_` | unicode | Language of the parent vocabulary. |
| `prob` | float | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). |
| `cluster` | int | Brown cluster ID. |
| `sentiment` | float | A scalar value indicating the positivity or negativity of the lexeme. |

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@ -408,71 +408,71 @@ The L2 norm of the token's vector representation.
## Attributes {#attributes}
| Name | Type | Description |
| -------------------------------------------- | ------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The parent document. |
| `sent` <Tag variant="new">2.0.12</Tag> | `Span` | The sentence span that this token is a part of. |
| `text` | unicode | Verbatim text content. |
| `text_with_ws` | unicode | Text content, with trailing space character if present. |
| `whitespace_` | unicode | Trailing space character if present. |
| `orth` | int | ID of the verbatim text content. |
| `orth_` | unicode | Verbatim text content (identical to `Token.text`). Exists mostly for consistency with the other attributes. |
| `vocab` | `Vocab` | The vocab object of the parent `Doc`. |
| `tensor` <Tag variant="new">2.1.7</Tag> | `ndarray` | The tokens's slice of the parent `Doc`'s tensor. |
| `head` | `Token` | The syntactic parent, or "governor", of this token. |
| `left_edge` | `Token` | The leftmost token of this token's syntactic descendants. |
| `right_edge` | `Token` | The rightmost token of this token's syntactic descendants. |
| `i` | int | The index of the token within the parent document. |
| `ent_type` | int | Named entity type. |
| `ent_type_` | unicode | Named entity type. |
| `ent_iob` | int | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set. |
| `ent_iob_` | unicode | IOB code of named entity tag. "B" means the token begins an entity, "I" means it is inside an entity, "O" means it is outside an entity, and "" means no entity tag is set. |
| `ent_kb_id` <Tag variant="new">2.2</Tag> | int | Knowledge base ID that refers to the named entity this token is a part of, if any. |
| `ent_kb_id_` <Tag variant="new">2.2</Tag> | unicode | Knowledge base ID that refers to the named entity this token is a part of, if any. |
| `ent_id` | int | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
| `ent_id_` | unicode | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
| `lemma` | int | Base form of the token, with no inflectional suffixes. |
| `lemma_` | unicode | Base form of the token, with no inflectional suffixes. |
| `norm` | int | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
| `norm_` | unicode | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
| `lower` | int | Lowercase form of the token. |
| `lower_` | unicode | Lowercase form of the token text. Equivalent to `Token.text.lower()`. |
| `shape` | int | Transform of the tokens's string, to show orthographic features. For example, "Xxxx" or "dd". |
| `shape_` | unicode | Transform of the tokens's string, to show orthographic features. For example, "Xxxx" or "dd". |
| `prefix` | int | Hash value of a length-N substring from the start of the token. Defaults to `N=1`. |
| `prefix_` | unicode | A length-N substring from the start of the token. Defaults to `N=1`. |
| `suffix` | int | Hash value of a length-N substring from the end of the token. Defaults to `N=3`. |
| `suffix_` | unicode | Length-N substring from the end of the token. Defaults to `N=3`. |
| `is_alpha` | bool | Does the token consist of alphabetic characters? Equivalent to `token.text.isalpha()`. |
| `is_ascii` | bool | Does the token consist of ASCII characters? Equivalent to `all(ord(c) < 128 for c in token.text)`. |
| `is_digit` | bool | Does the token consist of digits? Equivalent to `token.text.isdigit()`. |
| `is_lower` | bool | Is the token in lowercase? Equivalent to `token.text.islower()`. |
| `is_upper` | bool | Is the token in uppercase? Equivalent to `token.text.isupper()`. |
| `is_title` | bool | Is the token in titlecase? Equivalent to `token.text.istitle()`. |
| `is_punct` | bool | Is the token punctuation? |
| `is_left_punct` | bool | Is the token a left punctuation mark, e.g. `(`? |
| `is_right_punct` | bool | Is the token a right punctuation mark, e.g. `)`? |
| `is_space` | bool | Does the token consist of whitespace characters? Equivalent to `token.text.isspace()`. |
| `is_bracket` | bool | Is the token a bracket? |
| `is_quote` | bool | Is the token a quotation mark? |
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the token a currency symbol? |
| `like_url` | bool | Does the token resemble a URL? |
| `like_num` | bool | Does the token represent a number? e.g. "10.9", "10", "ten", etc. |
| `like_email` | bool | Does the token resemble an email address? |
| `is_oov` | bool | Is the token out-of-vocabulary? |
| `is_stop` | bool | Is the token part of a "stop list"? |
| `pos` | int | Coarse-grained part-of-speech. |
| `pos_` | unicode | Coarse-grained part-of-speech. |
| `tag` | int | Fine-grained part-of-speech. |
| `tag_` | unicode | Fine-grained part-of-speech. |
| `dep` | int | Syntactic dependency relation. |
| `dep_` | unicode | Syntactic dependency relation. |
| `lang` | int | Language of the parent document's vocabulary. |
| `lang_` | unicode | Language of the parent document's vocabulary. |
| `prob` | float | Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). |
| `idx` | int | The character offset of the token within the parent document. |
| `sentiment` | float | A scalar value indicating the positivity or negativity of the token. |
| `lex_id` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
| `rank` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
| `cluster` | int | Brown cluster ID. |
| `_` | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). |
| Name | Type | Description |
| -------------------------------------------- | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The parent document. |
| `sent` <Tag variant="new">2.0.12</Tag> | `Span` | The sentence span that this token is a part of. |
| `text` | unicode | Verbatim text content. |
| `text_with_ws` | unicode | Text content, with trailing space character if present. |
| `whitespace_` | unicode | Trailing space character if present. |
| `orth` | int | ID of the verbatim text content. |
| `orth_` | unicode | Verbatim text content (identical to `Token.text`). Exists mostly for consistency with the other attributes. |
| `vocab` | `Vocab` | The vocab object of the parent `Doc`. |
| `tensor` <Tag variant="new">2.1.7</Tag> | `ndarray` | The tokens's slice of the parent `Doc`'s tensor. |
| `head` | `Token` | The syntactic parent, or "governor", of this token. |
| `left_edge` | `Token` | The leftmost token of this token's syntactic descendants. |
| `right_edge` | `Token` | The rightmost token of this token's syntactic descendants. |
| `i` | int | The index of the token within the parent document. |
| `ent_type` | int | Named entity type. |
| `ent_type_` | unicode | Named entity type. |
| `ent_iob` | int | IOB code of named entity tag. `3` means the token begins an entity, `2` means it is outside an entity, `1` means it is inside an entity, and `0` means no entity tag is set. |
| `ent_iob_` | unicode | IOB code of named entity tag. "B" means the token begins an entity, "I" means it is inside an entity, "O" means it is outside an entity, and "" means no entity tag is set. |
| `ent_kb_id` <Tag variant="new">2.2</Tag> | int | Knowledge base ID that refers to the named entity this token is a part of, if any. |
| `ent_kb_id_` <Tag variant="new">2.2</Tag> | unicode | Knowledge base ID that refers to the named entity this token is a part of, if any. |
| `ent_id` | int | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
| `ent_id_` | unicode | ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. |
| `lemma` | int | Base form of the token, with no inflectional suffixes. |
| `lemma_` | unicode | Base form of the token, with no inflectional suffixes. |
| `norm` | int | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
| `norm_` | unicode | The token's norm, i.e. a normalized form of the token text. Usually set in the language's [tokenizer exceptions](/usage/adding-languages#tokenizer-exceptions) or [norm exceptions](/usage/adding-languages#norm-exceptions). |
| `lower` | int | Lowercase form of the token. |
| `lower_` | unicode | Lowercase form of the token text. Equivalent to `Token.text.lower()`. |
| `shape` | int | Transform of the tokens's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
| `shape_` | unicode | Transform of the tokens's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
| `prefix` | int | Hash value of a length-N substring from the start of the token. Defaults to `N=1`. |
| `prefix_` | unicode | A length-N substring from the start of the token. Defaults to `N=1`. |
| `suffix` | int | Hash value of a length-N substring from the end of the token. Defaults to `N=3`. |
| `suffix_` | unicode | Length-N substring from the end of the token. Defaults to `N=3`. |
| `is_alpha` | bool | Does the token consist of alphabetic characters? Equivalent to `token.text.isalpha()`. |
| `is_ascii` | bool | Does the token consist of ASCII characters? Equivalent to `all(ord(c) < 128 for c in token.text)`. |
| `is_digit` | bool | Does the token consist of digits? Equivalent to `token.text.isdigit()`. |
| `is_lower` | bool | Is the token in lowercase? Equivalent to `token.text.islower()`. |
| `is_upper` | bool | Is the token in uppercase? Equivalent to `token.text.isupper()`. |
| `is_title` | bool | Is the token in titlecase? Equivalent to `token.text.istitle()`. |
| `is_punct` | bool | Is the token punctuation? |
| `is_left_punct` | bool | Is the token a left punctuation mark, e.g. `'('` ? |
| `is_right_punct` | bool | Is the token a right punctuation mark, e.g. `')'` ? |
| `is_space` | bool | Does the token consist of whitespace characters? Equivalent to `token.text.isspace()`. |
| `is_bracket` | bool | Is the token a bracket? |
| `is_quote` | bool | Is the token a quotation mark? |
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the token a currency symbol? |
| `like_url` | bool | Does the token resemble a URL? |
| `like_num` | bool | Does the token represent a number? e.g. "10.9", "10", "ten", etc. |
| `like_email` | bool | Does the token resemble an email address? |
| `is_oov` | bool | Is the token out-of-vocabulary? |
| `is_stop` | bool | Is the token part of a "stop list"? |
| `pos` | int | Coarse-grained part-of-speech. |
| `pos_` | unicode | Coarse-grained part-of-speech. |
| `tag` | int | Fine-grained part-of-speech. |
| `tag_` | unicode | Fine-grained part-of-speech. |
| `dep` | int | Syntactic dependency relation. |
| `dep_` | unicode | Syntactic dependency relation. |
| `lang` | int | Language of the parent document's vocabulary. |
| `lang_` | unicode | Language of the parent document's vocabulary. |
| `prob` | float | Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). |
| `idx` | int | The character offset of the token within the parent document. |
| `sentiment` | float | A scalar value indicating the positivity or negativity of the token. |
| `lex_id` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
| `rank` | int | Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. |
| `cluster` | int | Brown cluster ID. |
| `_` | `Underscore` | User space for adding custom [attribute extensions](/usage/processing-pipelines#custom-components-attributes). |

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@ -638,7 +638,7 @@ punctuation – depending on the
The `IS_DIGIT` flag is not very helpful here, because it doesn't tell us
anything about the length. However, you can use the `SHAPE` flag, with each `d`
representing a digit:
representing a digit (up to 4 digits / characters):
```python
[{"ORTH": "("}, {"SHAPE": "ddd"}, {"ORTH": ")"}, {"SHAPE": "dddd"},
@ -654,7 +654,7 @@ match the most common formats of
```python
[{"ORTH": "+"}, {"ORTH": "49"}, {"ORTH": "(", "OP": "?"}, {"SHAPE": "dddd"},
{"ORTH": ")", "OP": "?"}, {"SHAPE": "dddddd"}]
{"ORTH": ")", "OP": "?"}, {"SHAPE": "dddd", "LENGTH": 6}]
```
Depending on the formats your application needs to match, creating an extensive

2
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@ -219,7 +219,7 @@ tokens. You can customize these behaviors by modifying the `doc.user_hooks`,
For more details on **adding hooks** and **overwriting** the built-in `Doc`,
`Span` and `Token` methods, see the usage guide on
[user hooks](/usage/processing-pipelines#user-hooks).
[user hooks](/usage/processing-pipelines#custom-components-user-hooks).
</Infobox>