Rename lang codes

This commit is contained in:
thomashacker 2022-11-09 13:47:16 +01:00
parent d0fc871a1c
commit 432db3d299
22 changed files with 68 additions and 72 deletions

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@ -7,7 +7,7 @@ class IcelandicDefaults(BaseDefaults):
class Icelandic(Language):
lang = "is"
lang = "isl"
Defaults = IcelandicDefaults

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@ -3,10 +3,10 @@ from ...language import Language
class MultiLanguage(Language):
"""Language class to be used for models that support multiple languages.
This module allows models to specify their language ID as 'xx'.
This module allows models to specify their language ID as 'mul'.
"""
lang = "xx"
lang = "mul"
__all__ = ["MultiLanguage"]

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@ -104,7 +104,7 @@ class Scorer:
def __init__(
self,
nlp: Optional["Language"] = None,
default_lang: str = "xx",
default_lang: str = "mul",
default_pipeline: Iterable[str] = DEFAULT_PIPELINE,
**cfg,
) -> None:

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@ -52,7 +52,7 @@ def pytest_runtest_setup(item):
@pytest.fixture(scope="module")
def tokenizer():
return get_lang_class("xx")().tokenizer
return get_lang_class("mul")().tokenizer
@pytest.fixture(scope="session")
@ -212,8 +212,8 @@ def id_tokenizer():
@pytest.fixture(scope="session")
def is_tokenizer():
return get_lang_class("is")().tokenizer
def isl_tokenizer():
return get_lang_class("isl")().tokenizer
@pytest.fixture(scope="session")
@ -465,8 +465,8 @@ def vi_tokenizer():
@pytest.fixture(scope="session")
def xx_tokenizer():
return get_lang_class("xx")().tokenizer
def mul_tokenizer():
return get_lang_class("mul")().tokenizer
@pytest.fixture(scope="session")

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@ -9,7 +9,7 @@ from thinc.api import NumpyOps, get_current_ops
from spacy.attrs import DEP, ENT_IOB, ENT_TYPE, HEAD, IS_ALPHA, MORPH, POS
from spacy.attrs import SENT_START, TAG
from spacy.lang.en import English
from spacy.lang.xx import MultiLanguage
from spacy.lang.mul import MultiLanguage
from spacy.language import Language
from spacy.lexeme import Lexeme
from spacy.tokens import Doc, Span, SpanGroup, Token

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@ -1,7 +1,7 @@
import pytest
def test_long_text(is_tokenizer):
def test_long_text(isl_tokenizer):
# Excerpt: European Convention on Human Rights
text = """
hafa í huga, yfirlýsing þessi hefur það markmið tryggja
@ -15,12 +15,12 @@ réttlætis og friðar í heiminum og best er tryggt, annars vegar með
virku, lýðræðislegu stjórnarfari og, hins vegar, almennum skilningi
og varðveislu þeirra mannréttinda, sem eru grundvöllur frelsisins;
"""
tokens = is_tokenizer(text)
tokens = isl_tokenizer(text)
assert len(tokens) == 120
@pytest.mark.xfail
def test_ordinal_number(is_tokenizer):
def test_ordinal_number(isl_tokenizer):
text = "10. desember 1948"
tokens = is_tokenizer(text)
tokens = isl_tokenizer(text)
assert len(tokens) == 3

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@ -1,6 +1,6 @@
import pytest
IS_BASIC_TOKENIZATION_TESTS = [
ISL_BASIC_TOKENIZATION_TESTS = [
(
"Enginn maður skal sæta pyndingum eða ómannlegri eða "
"vanvirðandi meðferð eða refsingu. ",
@ -23,8 +23,8 @@ IS_BASIC_TOKENIZATION_TESTS = [
]
@pytest.mark.parametrize("text,expected_tokens", IS_BASIC_TOKENIZATION_TESTS)
def test_is_tokenizer_basic(is_tokenizer, text, expected_tokens):
tokens = is_tokenizer(text)
@pytest.mark.parametrize("text,expected_tokens", ISL_BASIC_TOKENIZATION_TESTS)
def test_isl_tokenizer_basic(isl_tokenizer, text, expected_tokens):
tokens = isl_tokenizer(text)
token_list = [token.text for token in tokens if not token.is_space]
assert expected_tokens == token_list

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@ -1,7 +1,7 @@
import pytest
def test_long_text(xx_tokenizer):
def test_long_text(mul_tokenizer):
# Excerpt: Text in Skolt Sami taken from https://www.samediggi.fi
text = """
ʹmmla lie Euroopp unioon oʹdinakai alggmeer. ʹmmlai alggmeerstatus lij raʹvvjum Lääʹddjânnam vuâđđlääʹjjest.
@ -20,5 +20,5 @@ vuâđđlääʹjj meâldlaž jiõččvaaldâšm. Säʹmmlai jiõččvaldšma kuu
Sääʹmteʹǧǧ.
"""
tokens = xx_tokenizer(text)
tokens = mul_tokenizer(text)
assert len(tokens) == 179

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@ -1,6 +1,6 @@
import pytest
XX_BASIC_TOKENIZATION_TESTS = [
MUL_BASIC_TOKENIZATION_TESTS = [
(
"Lääʹddjânnmest lie nuʹtt 10 000 säʹmmliʹžžed. Seeʹst pâʹjjel",
[
@ -18,8 +18,8 @@ XX_BASIC_TOKENIZATION_TESTS = [
]
@pytest.mark.parametrize("text,expected_tokens", XX_BASIC_TOKENIZATION_TESTS)
def test_xx_tokenizer_basic(xx_tokenizer, text, expected_tokens):
tokens = xx_tokenizer(text)
@pytest.mark.parametrize("text,expected_tokens", MUL_BASIC_TOKENIZATION_TESTS)
def test_mul_tokenizer_basic(mul_tokenizer, text, expected_tokens):
tokens = mul_tokenizer(text)
token_list = [token.text for token in tokens if not token.is_space]
assert expected_tokens == token_list

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@ -7,10 +7,10 @@ from spacy.util import get_lang_class
# excluded: ja, ko, th, vi, zh
LANGUAGES = ["af", "am", "ar", "az", "bg", "bn", "ca", "cs", "da", "de", "el",
"en", "es", "et", "eu", "fa", "fi", "fr", "ga", "gu", "he", "hi",
"hr", "hu", "hy", "id", "is", "it", "kn", "ky", "lb", "lt", "lv",
"hr", "hu", "hy", "id", "isl", "it", "kn", "ky", "lb", "lt", "lv",
"mk", "ml", "mr", "nb", "ne", "nl", "pl", "pt", "ro", "ru", "sa",
"si", "sk", "sl", "sq", "sr", "sv", "ta", "te", "ti", "tl", "tn",
"tr", "tt", "uk", "ur", "xx", "yo"]
"tr", "tt", "uk", "ur", "mul", "yo"]
# fmt: on

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@ -47,7 +47,7 @@ def person_org_date_patterns(person_org_patterns):
def test_span_ruler_add_empty(patterns):
"""Test that patterns don't get added excessively."""
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler", config={"validate": True})
ruler.add_patterns(patterns)
pattern_count = sum(len(mm) for mm in ruler.matcher._patterns.values())
@ -58,7 +58,7 @@ def test_span_ruler_add_empty(patterns):
def test_span_ruler_init(patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(patterns)
assert len(ruler) == len(patterns)
@ -74,7 +74,7 @@ def test_span_ruler_init(patterns):
def test_span_ruler_no_patterns_warns():
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
assert len(ruler) == 0
assert len(ruler.labels) == 0
@ -86,7 +86,7 @@ def test_span_ruler_no_patterns_warns():
def test_span_ruler_init_patterns(patterns):
# initialize with patterns
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
assert len(ruler.labels) == 0
ruler.initialize(lambda: [], patterns=patterns)
@ -110,7 +110,7 @@ def test_span_ruler_init_patterns(patterns):
def test_span_ruler_init_clear(patterns):
"""Test that initialization clears patterns."""
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(patterns)
assert len(ruler.labels) == 4
@ -119,7 +119,7 @@ def test_span_ruler_init_clear(patterns):
def test_span_ruler_clear(patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(patterns)
assert len(ruler.labels) == 4
@ -133,7 +133,7 @@ def test_span_ruler_clear(patterns):
def test_span_ruler_existing(patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler", config={"overwrite": False})
ruler.add_patterns(patterns)
doc = nlp.make_doc("OH HELLO WORLD bye bye")
@ -148,7 +148,7 @@ def test_span_ruler_existing(patterns):
def test_span_ruler_existing_overwrite(patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler", config={"overwrite": True})
ruler.add_patterns(patterns)
doc = nlp.make_doc("OH HELLO WORLD bye bye")
@ -161,13 +161,13 @@ def test_span_ruler_existing_overwrite(patterns):
def test_span_ruler_serialize_bytes(patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(patterns)
assert len(ruler) == len(patterns)
assert len(ruler.labels) == 4
ruler_bytes = ruler.to_bytes()
new_nlp = spacy.blank("xx")
new_nlp = spacy.blank("mul")
new_ruler = new_nlp.add_pipe("span_ruler")
assert len(new_ruler) == 0
assert len(new_ruler.labels) == 0
@ -181,7 +181,7 @@ def test_span_ruler_serialize_bytes(patterns):
def test_span_ruler_validate():
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
validated_ruler = nlp.add_pipe(
"span_ruler", name="validated_span_ruler", config={"validate": True}
@ -203,14 +203,14 @@ def test_span_ruler_validate():
def test_span_ruler_properties(patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler", config={"overwrite": True})
ruler.add_patterns(patterns)
assert sorted(ruler.labels) == sorted(set([p["label"] for p in patterns]))
def test_span_ruler_overlapping_spans(overlapping_patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(overlapping_patterns)
doc = ruler(nlp.make_doc("foo bar baz"))
@ -220,7 +220,7 @@ def test_span_ruler_overlapping_spans(overlapping_patterns):
def test_span_ruler_scorer(overlapping_patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(overlapping_patterns)
text = "foo bar baz"
@ -243,7 +243,7 @@ def test_span_ruler_multiprocessing(n_process):
patterns = [{"label": "FASTFOOD", "pattern": "Pizza Hut"}]
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(patterns)
@ -253,7 +253,7 @@ def test_span_ruler_multiprocessing(n_process):
def test_span_ruler_serialize_dir(patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(patterns)
with make_tempdir() as d:
@ -264,7 +264,7 @@ def test_span_ruler_serialize_dir(patterns):
def test_span_ruler_remove_basic(person_org_patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(person_org_patterns)
doc = ruler(nlp.make_doc("Dina went to school"))
@ -279,7 +279,7 @@ def test_span_ruler_remove_basic(person_org_patterns):
def test_span_ruler_remove_nonexisting_pattern(person_org_patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(person_org_patterns)
assert len(ruler.patterns) == 3
@ -290,7 +290,7 @@ def test_span_ruler_remove_nonexisting_pattern(person_org_patterns):
def test_span_ruler_remove_several_patterns(person_org_patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(person_org_patterns)
doc = ruler(nlp.make_doc("Dina founded the company ACME."))
@ -314,7 +314,7 @@ def test_span_ruler_remove_several_patterns(person_org_patterns):
def test_span_ruler_remove_patterns_in_a_row(person_org_date_patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(person_org_date_patterns)
doc = ruler(nlp.make_doc("Dina founded the company ACME on June 14th"))
@ -332,7 +332,7 @@ def test_span_ruler_remove_patterns_in_a_row(person_org_date_patterns):
def test_span_ruler_remove_all_patterns(person_org_date_patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
ruler.add_patterns(person_org_date_patterns)
assert len(ruler.patterns) == 4
@ -348,7 +348,7 @@ def test_span_ruler_remove_all_patterns(person_org_date_patterns):
def test_span_ruler_remove_and_add():
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler")
patterns1 = [{"label": "DATE1", "pattern": "last time"}]
ruler.add_patterns(patterns1)
@ -404,7 +404,7 @@ def test_span_ruler_remove_and_add():
def test_span_ruler_spans_filter(overlapping_patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe(
"span_ruler",
config={"spans_filter": {"@misc": "spacy.first_longest_spans_filter.v1"}},
@ -416,7 +416,7 @@ def test_span_ruler_spans_filter(overlapping_patterns):
def test_span_ruler_ents_default_filter(overlapping_patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe("span_ruler", config={"annotate_ents": True})
ruler.add_patterns(overlapping_patterns)
doc = ruler(nlp.make_doc("foo bar baz"))
@ -425,7 +425,7 @@ def test_span_ruler_ents_default_filter(overlapping_patterns):
def test_span_ruler_ents_overwrite_filter(overlapping_patterns):
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe(
"span_ruler",
config={
@ -452,7 +452,7 @@ def test_span_ruler_ents_bad_filter(overlapping_patterns):
return pass_through_filter
nlp = spacy.blank("xx")
nlp = spacy.blank("mul")
ruler = nlp.add_pipe(
"span_ruler",
config={

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@ -552,10 +552,10 @@ def test_spacy_blank():
("fre", "fr"),
("iw", "he"),
("mo", "ro"),
("mul", "xx"),
("no", "nb"),
("is", "isl"),
("pt-BR", "pt"),
("xx", "xx"),
("xx", "mul"),
("zh-Hans", "zh"),
("zh-Hant", None),
("zxx", None),
@ -577,10 +577,10 @@ def test_language_matching(lang, target):
("fre", "fr"),
("iw", "he"),
("mo", "ro"),
("mul", "xx"),
("is", "isl"),
("xx", "mul"),
("no", "nb"),
("pt-BR", "pt"),
("xx", "xx"),
("zh-Hans", "zh"),
],
)

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@ -10,7 +10,6 @@ from spacy.tokenizer import Tokenizer
from spacy.util import get_lang_class
# Only include languages with no external dependencies
# "is" seems to confuse importlib, so we're also excluding it for now
# excluded: ja, ru, th, uk, vi, zh, is
LANGUAGES = [
pytest.param("fr", marks=pytest.mark.slow()),
@ -36,6 +35,7 @@ LANGUAGES = [
"hu",
pytest.param("id", marks=pytest.mark.slow()),
pytest.param("it", marks=pytest.mark.slow()),
pytest.param("isl", marks=pytest.mark.slow()),
pytest.param("kn", marks=pytest.mark.slow()),
pytest.param("lb", marks=pytest.mark.slow()),
pytest.param("lt", marks=pytest.mark.slow()),

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@ -86,7 +86,7 @@ def conll_ner_to_docs(
if model:
nlp = load_model(model)
else:
nlp = get_lang_class("xx")()
nlp = get_lang_class("mul")()
for conll_doc in input_data.strip().split(doc_delimiter):
conll_doc = conll_doc.strip()
if not conll_doc:
@ -133,7 +133,7 @@ def segment_sents_and_docs(doc, n_sents, doc_delimiter, model=None, msg=None):
"Segmenting sentences with sentencizer. (Use `-b model` for "
"improved parser-based sentence segmentation.)"
)
nlp = get_lang_class("xx")()
nlp = get_lang_class("mul")()
sentencizer = nlp.create_pipe("sentencizer")
lines = doc.strip().split("\n")
words = [line.strip().split()[0] for line in lines]

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@ -3,7 +3,7 @@ from ..gold_io import json_iterate, json_to_annotations
from ..example import annotations_to_doc
from ..example import _fix_legacy_dict_data, _parse_example_dict_data
from ...util import load_model
from ...lang.xx import MultiLanguage
from ...lang.mul import MultiLanguage
def json_to_docs(input_data, model=None, **kwargs):

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@ -282,7 +282,7 @@ def find_matching_language(lang: str) -> Optional[str]:
import spacy.lang # noqa: F401
if lang == "xx":
return "xx"
return "mul"
# Find out which language modules we have
possible_languages = []
@ -300,10 +300,6 @@ def find_matching_language(lang: str) -> Optional[str]:
# is labeled that way is probably trying to be distinct from 'zh' and
# shouldn't automatically match.
match = langcodes.closest_supported_match(lang, possible_languages, max_distance=9)
if match == "mul":
# Convert 'mul' back to spaCy's 'xx'
return "xx"
else:
return match

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@ -74,11 +74,11 @@ import Languages from 'widgets/languages.js'
> ```python
> # Standard import
> from spacy.lang.xx import MultiLanguage
> from spacy.lang.mul import MultiLanguage
> nlp = MultiLanguage()
>
> # With lazy-loading
> nlp = spacy.blank("xx")
> nlp = spacy.blank("mul")
> ```
spaCy also supports pipelines trained on more than one language. This is
@ -88,9 +88,9 @@ generic subclass containing only the base language data, can be found in
[`lang/xx`](%%GITHUB_SPACY/spacy/lang/xx).
To train a pipeline using the neutral multi-language class, you can set
`lang = "xx"` in your [training config](/usage/training#config). You can also
`lang = "mul"` in your [training config](/usage/training#config). You can also
import the `MultiLanguage` class directly, or call
[`spacy.blank("xx")`](/api/top-level#spacy.blank) for lazy-loading.
[`spacy.blank("mul")`](/api/top-level#spacy.blank) for lazy-loading.
### Chinese language support {#chinese new="2.3"}

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@ -207,7 +207,7 @@
"has_examples": true
},
{
"code": "is",
"code": "isl",
"name": "Icelandic"
},
{
@ -530,7 +530,7 @@
]
},
{
"code": "xx",
"code": "mul",
"name": "Multi-language",
"models": [
"xx_ent_wiki_sm",