spaCy/spacy/tests/lang/ja/test_tokenizer.py

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import pytest
from ...tokenizer.test_naughty_strings import NAUGHTY_STRINGS
from spacy.lang.ja import Japanese, DetailedToken
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# fmt: off
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TOKENIZER_TESTS = [
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
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("日本語だよ", ['日本', '', '', '']),
("東京タワーの近くに住んでいます。", ['東京', 'タワー', '', '近く', '', '住ん', '', '', 'ます', '']),
("吾輩は猫である。", ['吾輩', '', '', '', 'ある', '']),
("月に代わって、お仕置きよ!", ['', '', '代わっ', '', '', '', '仕置き', '', '!']),
("すもももももももものうち", ['すもも', '', 'もも', '', 'もも', '', 'うち'])
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]
TAG_TESTS = [
Add Japanese Model (#5544) * Add more rules to deal with Japanese UD mappings Japanese UD rules sometimes give different UD tags to tokens with the same underlying POS tag. The UD spec indicates these cases should be disambiguated using the output of a tool called "comainu", but rules are enough to get the right result. These rules are taken from Ginza at time of writing, see #3756. * Add new tags from GSD This is a few rare tags that aren't in Unidic but are in the GSD data. * Add basic Japanese sentencization This code is taken from Ginza again. * Add sentenceizer quote handling Could probably add more paired characters but this will do for now. Also includes some tests. * Replace fugashi with SudachiPy * Modify tag format to match GSD annotations Some of the tests still need to be updated, but I want to get this up for testing training. * Deal with case with closing punct without opening * refactor resolve_pos() * change tag field separator from "," to "-" * add TAG_ORTH_MAP * add TAG_BIGRAM_MAP * revise rules for 連体詞 * revise rules for 連体詞 * improve POS about 2% * add syntax_iterator.py (not mature yet) * improve syntax_iterators.py * improve syntax_iterators.py * add phrases including nouns and drop NPs consist of STOP_WORDS * First take at noun chunks This works in many situations but still has issues in others. If the start of a subtree has no noun, then nested phrases can be generated. また行きたい、そんな気持ちにさせてくれるお店です。 [そんな気持ち, また行きたい、そんな気持ちにさせてくれるお店] For some reason て gets included sometimes. Not sure why. ゲンに連れ添って円盤生物を調査するパートナーとなる。 [て円盤生物, ...] Some phrases that look like they should be split are grouped together; not entirely sure that's wrong. This whole thing becomes one chunk: 道の駅遠山郷北側からかぐら大橋南詰現道交点までの1.060kmのみ開通済み * Use new generic get_words_and_spaces The new get_words_and_spaces function is simpler than what was used in Japanese, so it's good to be able to switch to it. However, there was an issue. The new function works just on text, so POS info could get out of sync. Fixing this required a small change to the way dtokens (tokens with POS and lemma info) were generated. Specifically, multiple extraneous spaces now become a single token, so when generating dtokens multiple space tokens should be created in a row. * Fix noun_chunks, should be working now * Fix some tests, add naughty strings tests Some of the existing tests changed because the tokenization mode of Sudachi changed to the more fine-grained A mode. Sudachi also has issues with some strings, so this adds a test against the naughty strings. * Remove empty Sudachi tokens Not doing this creates zero-length tokens and causes errors in the internal spaCy processing. * Add yield_bunsetu back in as a separate piece of code Co-authored-by: Hiroshi Matsuda <40782025+hiroshi-matsuda-rit@users.noreply.github.com> Co-authored-by: hiroshi <hiroshi_matsuda@megagon.ai>
2020-06-04 20:15:43 +03:00
("日本語だよ", ['名詞-固有名詞-地名-国', '名詞-普通名詞-一般', '助動詞', '助詞-終助詞']),
("東京タワーの近くに住んでいます。", ['名詞-固有名詞-地名-一般', '名詞-普通名詞-一般', '助詞-格助詞', '名詞-普通名詞-副詞可能', '助詞-格助詞', '動詞-一般', '助詞-接続助詞', '動詞-非自立可能', '助動詞', '補助記号-句点']),
("吾輩は猫である。", ['代名詞', '助詞-係助詞', '名詞-普通名詞-一般', '助動詞', '動詞-非自立可能', '補助記号-句点']),
("月に代わって、お仕置きよ!", ['名詞-普通名詞-助数詞可能', '助詞-格助詞', '動詞-一般', '助詞-接続助詞', '補助記号-読点', '接頭辞', '名詞-普通名詞-一般', '助詞-終助詞', '補助記号-句点']),
("すもももももももものうち", ['名詞-普通名詞-一般', '助詞-係助詞', '名詞-普通名詞-一般', '助詞-係助詞', '名詞-普通名詞-一般', '助詞-格助詞', '名詞-普通名詞-副詞可能'])
]
POS_TESTS = [
('日本語だよ', ['PROPN', 'NOUN', 'AUX', 'PART']),
('東京タワーの近くに住んでいます。', ['PROPN', 'NOUN', 'ADP', 'NOUN', 'ADP', 'VERB', 'SCONJ', 'AUX', 'AUX', 'PUNCT']),
('吾輩は猫である。', ['PRON', 'ADP', 'NOUN', 'AUX', 'AUX', 'PUNCT']),
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
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('月に代わって、お仕置きよ!', ['NOUN', 'ADP', 'VERB', 'SCONJ', 'PUNCT', 'NOUN', 'NOUN', 'PART', 'PUNCT']),
('すもももももももものうち', ['NOUN', 'ADP', 'NOUN', 'ADP', 'NOUN', 'ADP', 'NOUN'])
]
Add Japanese Model (#5544) * Add more rules to deal with Japanese UD mappings Japanese UD rules sometimes give different UD tags to tokens with the same underlying POS tag. The UD spec indicates these cases should be disambiguated using the output of a tool called "comainu", but rules are enough to get the right result. These rules are taken from Ginza at time of writing, see #3756. * Add new tags from GSD This is a few rare tags that aren't in Unidic but are in the GSD data. * Add basic Japanese sentencization This code is taken from Ginza again. * Add sentenceizer quote handling Could probably add more paired characters but this will do for now. Also includes some tests. * Replace fugashi with SudachiPy * Modify tag format to match GSD annotations Some of the tests still need to be updated, but I want to get this up for testing training. * Deal with case with closing punct without opening * refactor resolve_pos() * change tag field separator from "," to "-" * add TAG_ORTH_MAP * add TAG_BIGRAM_MAP * revise rules for 連体詞 * revise rules for 連体詞 * improve POS about 2% * add syntax_iterator.py (not mature yet) * improve syntax_iterators.py * improve syntax_iterators.py * add phrases including nouns and drop NPs consist of STOP_WORDS * First take at noun chunks This works in many situations but still has issues in others. If the start of a subtree has no noun, then nested phrases can be generated. また行きたい、そんな気持ちにさせてくれるお店です。 [そんな気持ち, また行きたい、そんな気持ちにさせてくれるお店] For some reason て gets included sometimes. Not sure why. ゲンに連れ添って円盤生物を調査するパートナーとなる。 [て円盤生物, ...] Some phrases that look like they should be split are grouped together; not entirely sure that's wrong. This whole thing becomes one chunk: 道の駅遠山郷北側からかぐら大橋南詰現道交点までの1.060kmのみ開通済み * Use new generic get_words_and_spaces The new get_words_and_spaces function is simpler than what was used in Japanese, so it's good to be able to switch to it. However, there was an issue. The new function works just on text, so POS info could get out of sync. Fixing this required a small change to the way dtokens (tokens with POS and lemma info) were generated. Specifically, multiple extraneous spaces now become a single token, so when generating dtokens multiple space tokens should be created in a row. * Fix noun_chunks, should be working now * Fix some tests, add naughty strings tests Some of the existing tests changed because the tokenization mode of Sudachi changed to the more fine-grained A mode. Sudachi also has issues with some strings, so this adds a test against the naughty strings. * Remove empty Sudachi tokens Not doing this creates zero-length tokens and causes errors in the internal spaCy processing. * Add yield_bunsetu back in as a separate piece of code Co-authored-by: Hiroshi Matsuda <40782025+hiroshi-matsuda-rit@users.noreply.github.com> Co-authored-by: hiroshi <hiroshi_matsuda@megagon.ai>
2020-06-04 20:15:43 +03:00
SENTENCE_TESTS = [
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("あれ。これ。", ["あれ。", "これ。"]),
("「伝染るんです。」という漫画があります。", ["「伝染るんです。」という漫画があります。"]),
]
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tokens1 = [
DetailedToken(surface="委員", tag="名詞-普通名詞-一般", inf="", lemma="委員", norm="委員", reading="イイン", sub_tokens=None),
DetailedToken(surface="", tag="名詞-普通名詞-一般", inf="", lemma="", norm="", reading="カイ", sub_tokens=None),
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]
tokens2 = [
DetailedToken(surface="選挙", tag="名詞-普通名詞-サ変可能", inf="", lemma="選挙", norm="選挙", reading="センキョ", sub_tokens=None),
DetailedToken(surface="管理", tag="名詞-普通名詞-サ変可能", inf="", lemma="管理", norm="管理", reading="カンリ", sub_tokens=None),
DetailedToken(surface="委員", tag="名詞-普通名詞-一般", inf="", lemma="委員", norm="委員", reading="イイン", sub_tokens=None),
DetailedToken(surface="", tag="名詞-普通名詞-一般", inf="", lemma="", norm="", reading="カイ", sub_tokens=None),
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]
tokens3 = [
DetailedToken(surface="選挙", tag="名詞-普通名詞-サ変可能", inf="", lemma="選挙", norm="選挙", reading="センキョ", sub_tokens=None),
DetailedToken(surface="管理", tag="名詞-普通名詞-サ変可能", inf="", lemma="管理", norm="管理", reading="カンリ", sub_tokens=None),
DetailedToken(surface="委員会", tag="名詞-普通名詞-一般", inf="", lemma="委員会", norm="委員会", reading="イインカイ", sub_tokens=None),
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]
SUB_TOKEN_TESTS = [
("選挙管理委員会", [None, None, [tokens1]], [[tokens2, tokens3]])
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]
# fmt: on
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@pytest.mark.parametrize("text,expected_tokens", TOKENIZER_TESTS)
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
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def test_ja_tokenizer(ja_tokenizer, text, expected_tokens):
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tokens = [token.text for token in ja_tokenizer(text)]
assert tokens == expected_tokens
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
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@pytest.mark.parametrize("text,expected_tags", TAG_TESTS)
def test_ja_tokenizer_tags(ja_tokenizer, text, expected_tags):
tags = [token.tag_ for token in ja_tokenizer(text)]
assert tags == expected_tags
💫 Refactor test suite (#2568) ## Description Related issues: #2379 (should be fixed by separating model tests) * **total execution time down from > 300 seconds to under 60 seconds** 🎉 * removed all model-specific tests that could only really be run manually anyway – those will now live in a separate test suite in the [`spacy-models`](https://github.com/explosion/spacy-models) repository and are already integrated into our new model training infrastructure * changed all relative imports to absolute imports to prepare for moving the test suite from `/spacy/tests` to `/tests` (it'll now always test against the installed version) * merged old regression tests into collections, e.g. `test_issue1001-1500.py` (about 90% of the regression tests are very short anyways) * tidied up and rewrote existing tests wherever possible ### Todo - [ ] move tests to `/tests` and adjust CI commands accordingly - [x] move model test suite from internal repo to `spacy-models` - [x] ~~investigate why `pipeline/test_textcat.py` is flakey~~ - [x] review old regression tests (leftover files) and see if they can be merged, simplified or deleted - [ ] update documentation on how to run tests ### Types of change enhancement, tests ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
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@pytest.mark.parametrize("text,expected_pos", POS_TESTS)
def test_ja_tokenizer_pos(ja_tokenizer, text, expected_pos):
pos = [token.pos_ for token in ja_tokenizer(text)]
assert pos == expected_pos
@pytest.mark.skip(reason="sentence segmentation in tokenizer is buggy")
Add Japanese Model (#5544) * Add more rules to deal with Japanese UD mappings Japanese UD rules sometimes give different UD tags to tokens with the same underlying POS tag. The UD spec indicates these cases should be disambiguated using the output of a tool called "comainu", but rules are enough to get the right result. These rules are taken from Ginza at time of writing, see #3756. * Add new tags from GSD This is a few rare tags that aren't in Unidic but are in the GSD data. * Add basic Japanese sentencization This code is taken from Ginza again. * Add sentenceizer quote handling Could probably add more paired characters but this will do for now. Also includes some tests. * Replace fugashi with SudachiPy * Modify tag format to match GSD annotations Some of the tests still need to be updated, but I want to get this up for testing training. * Deal with case with closing punct without opening * refactor resolve_pos() * change tag field separator from "," to "-" * add TAG_ORTH_MAP * add TAG_BIGRAM_MAP * revise rules for 連体詞 * revise rules for 連体詞 * improve POS about 2% * add syntax_iterator.py (not mature yet) * improve syntax_iterators.py * improve syntax_iterators.py * add phrases including nouns and drop NPs consist of STOP_WORDS * First take at noun chunks This works in many situations but still has issues in others. If the start of a subtree has no noun, then nested phrases can be generated. また行きたい、そんな気持ちにさせてくれるお店です。 [そんな気持ち, また行きたい、そんな気持ちにさせてくれるお店] For some reason て gets included sometimes. Not sure why. ゲンに連れ添って円盤生物を調査するパートナーとなる。 [て円盤生物, ...] Some phrases that look like they should be split are grouped together; not entirely sure that's wrong. This whole thing becomes one chunk: 道の駅遠山郷北側からかぐら大橋南詰現道交点までの1.060kmのみ開通済み * Use new generic get_words_and_spaces The new get_words_and_spaces function is simpler than what was used in Japanese, so it's good to be able to switch to it. However, there was an issue. The new function works just on text, so POS info could get out of sync. Fixing this required a small change to the way dtokens (tokens with POS and lemma info) were generated. Specifically, multiple extraneous spaces now become a single token, so when generating dtokens multiple space tokens should be created in a row. * Fix noun_chunks, should be working now * Fix some tests, add naughty strings tests Some of the existing tests changed because the tokenization mode of Sudachi changed to the more fine-grained A mode. Sudachi also has issues with some strings, so this adds a test against the naughty strings. * Remove empty Sudachi tokens Not doing this creates zero-length tokens and causes errors in the internal spaCy processing. * Add yield_bunsetu back in as a separate piece of code Co-authored-by: Hiroshi Matsuda <40782025+hiroshi-matsuda-rit@users.noreply.github.com> Co-authored-by: hiroshi <hiroshi_matsuda@megagon.ai>
2020-06-04 20:15:43 +03:00
@pytest.mark.parametrize("text,expected_sents", SENTENCE_TESTS)
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def test_ja_tokenizer_sents(ja_tokenizer, text, expected_sents):
Add Japanese Model (#5544) * Add more rules to deal with Japanese UD mappings Japanese UD rules sometimes give different UD tags to tokens with the same underlying POS tag. The UD spec indicates these cases should be disambiguated using the output of a tool called "comainu", but rules are enough to get the right result. These rules are taken from Ginza at time of writing, see #3756. * Add new tags from GSD This is a few rare tags that aren't in Unidic but are in the GSD data. * Add basic Japanese sentencization This code is taken from Ginza again. * Add sentenceizer quote handling Could probably add more paired characters but this will do for now. Also includes some tests. * Replace fugashi with SudachiPy * Modify tag format to match GSD annotations Some of the tests still need to be updated, but I want to get this up for testing training. * Deal with case with closing punct without opening * refactor resolve_pos() * change tag field separator from "," to "-" * add TAG_ORTH_MAP * add TAG_BIGRAM_MAP * revise rules for 連体詞 * revise rules for 連体詞 * improve POS about 2% * add syntax_iterator.py (not mature yet) * improve syntax_iterators.py * improve syntax_iterators.py * add phrases including nouns and drop NPs consist of STOP_WORDS * First take at noun chunks This works in many situations but still has issues in others. If the start of a subtree has no noun, then nested phrases can be generated. また行きたい、そんな気持ちにさせてくれるお店です。 [そんな気持ち, また行きたい、そんな気持ちにさせてくれるお店] For some reason て gets included sometimes. Not sure why. ゲンに連れ添って円盤生物を調査するパートナーとなる。 [て円盤生物, ...] Some phrases that look like they should be split are grouped together; not entirely sure that's wrong. This whole thing becomes one chunk: 道の駅遠山郷北側からかぐら大橋南詰現道交点までの1.060kmのみ開通済み * Use new generic get_words_and_spaces The new get_words_and_spaces function is simpler than what was used in Japanese, so it's good to be able to switch to it. However, there was an issue. The new function works just on text, so POS info could get out of sync. Fixing this required a small change to the way dtokens (tokens with POS and lemma info) were generated. Specifically, multiple extraneous spaces now become a single token, so when generating dtokens multiple space tokens should be created in a row. * Fix noun_chunks, should be working now * Fix some tests, add naughty strings tests Some of the existing tests changed because the tokenization mode of Sudachi changed to the more fine-grained A mode. Sudachi also has issues with some strings, so this adds a test against the naughty strings. * Remove empty Sudachi tokens Not doing this creates zero-length tokens and causes errors in the internal spaCy processing. * Add yield_bunsetu back in as a separate piece of code Co-authored-by: Hiroshi Matsuda <40782025+hiroshi-matsuda-rit@users.noreply.github.com> Co-authored-by: hiroshi <hiroshi_matsuda@megagon.ai>
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sents = [str(sent) for sent in ja_tokenizer(text).sents]
assert sents == expected_sents
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def test_ja_tokenizer_extra_spaces(ja_tokenizer):
# note: three spaces after "I"
tokens = ja_tokenizer("I like cheese.")
Add Japanese Model (#5544) * Add more rules to deal with Japanese UD mappings Japanese UD rules sometimes give different UD tags to tokens with the same underlying POS tag. The UD spec indicates these cases should be disambiguated using the output of a tool called "comainu", but rules are enough to get the right result. These rules are taken from Ginza at time of writing, see #3756. * Add new tags from GSD This is a few rare tags that aren't in Unidic but are in the GSD data. * Add basic Japanese sentencization This code is taken from Ginza again. * Add sentenceizer quote handling Could probably add more paired characters but this will do for now. Also includes some tests. * Replace fugashi with SudachiPy * Modify tag format to match GSD annotations Some of the tests still need to be updated, but I want to get this up for testing training. * Deal with case with closing punct without opening * refactor resolve_pos() * change tag field separator from "," to "-" * add TAG_ORTH_MAP * add TAG_BIGRAM_MAP * revise rules for 連体詞 * revise rules for 連体詞 * improve POS about 2% * add syntax_iterator.py (not mature yet) * improve syntax_iterators.py * improve syntax_iterators.py * add phrases including nouns and drop NPs consist of STOP_WORDS * First take at noun chunks This works in many situations but still has issues in others. If the start of a subtree has no noun, then nested phrases can be generated. また行きたい、そんな気持ちにさせてくれるお店です。 [そんな気持ち, また行きたい、そんな気持ちにさせてくれるお店] For some reason て gets included sometimes. Not sure why. ゲンに連れ添って円盤生物を調査するパートナーとなる。 [て円盤生物, ...] Some phrases that look like they should be split are grouped together; not entirely sure that's wrong. This whole thing becomes one chunk: 道の駅遠山郷北側からかぐら大橋南詰現道交点までの1.060kmのみ開通済み * Use new generic get_words_and_spaces The new get_words_and_spaces function is simpler than what was used in Japanese, so it's good to be able to switch to it. However, there was an issue. The new function works just on text, so POS info could get out of sync. Fixing this required a small change to the way dtokens (tokens with POS and lemma info) were generated. Specifically, multiple extraneous spaces now become a single token, so when generating dtokens multiple space tokens should be created in a row. * Fix noun_chunks, should be working now * Fix some tests, add naughty strings tests Some of the existing tests changed because the tokenization mode of Sudachi changed to the more fine-grained A mode. Sudachi also has issues with some strings, so this adds a test against the naughty strings. * Remove empty Sudachi tokens Not doing this creates zero-length tokens and causes errors in the internal spaCy processing. * Add yield_bunsetu back in as a separate piece of code Co-authored-by: Hiroshi Matsuda <40782025+hiroshi-matsuda-rit@users.noreply.github.com> Co-authored-by: hiroshi <hiroshi_matsuda@megagon.ai>
2020-06-04 20:15:43 +03:00
assert tokens[1].orth_ == " "
@pytest.mark.parametrize("text", NAUGHTY_STRINGS)
def test_ja_tokenizer_naughty_strings(ja_tokenizer, text):
Add Japanese Model (#5544) * Add more rules to deal with Japanese UD mappings Japanese UD rules sometimes give different UD tags to tokens with the same underlying POS tag. The UD spec indicates these cases should be disambiguated using the output of a tool called "comainu", but rules are enough to get the right result. These rules are taken from Ginza at time of writing, see #3756. * Add new tags from GSD This is a few rare tags that aren't in Unidic but are in the GSD data. * Add basic Japanese sentencization This code is taken from Ginza again. * Add sentenceizer quote handling Could probably add more paired characters but this will do for now. Also includes some tests. * Replace fugashi with SudachiPy * Modify tag format to match GSD annotations Some of the tests still need to be updated, but I want to get this up for testing training. * Deal with case with closing punct without opening * refactor resolve_pos() * change tag field separator from "," to "-" * add TAG_ORTH_MAP * add TAG_BIGRAM_MAP * revise rules for 連体詞 * revise rules for 連体詞 * improve POS about 2% * add syntax_iterator.py (not mature yet) * improve syntax_iterators.py * improve syntax_iterators.py * add phrases including nouns and drop NPs consist of STOP_WORDS * First take at noun chunks This works in many situations but still has issues in others. If the start of a subtree has no noun, then nested phrases can be generated. また行きたい、そんな気持ちにさせてくれるお店です。 [そんな気持ち, また行きたい、そんな気持ちにさせてくれるお店] For some reason て gets included sometimes. Not sure why. ゲンに連れ添って円盤生物を調査するパートナーとなる。 [て円盤生物, ...] Some phrases that look like they should be split are grouped together; not entirely sure that's wrong. This whole thing becomes one chunk: 道の駅遠山郷北側からかぐら大橋南詰現道交点までの1.060kmのみ開通済み * Use new generic get_words_and_spaces The new get_words_and_spaces function is simpler than what was used in Japanese, so it's good to be able to switch to it. However, there was an issue. The new function works just on text, so POS info could get out of sync. Fixing this required a small change to the way dtokens (tokens with POS and lemma info) were generated. Specifically, multiple extraneous spaces now become a single token, so when generating dtokens multiple space tokens should be created in a row. * Fix noun_chunks, should be working now * Fix some tests, add naughty strings tests Some of the existing tests changed because the tokenization mode of Sudachi changed to the more fine-grained A mode. Sudachi also has issues with some strings, so this adds a test against the naughty strings. * Remove empty Sudachi tokens Not doing this creates zero-length tokens and causes errors in the internal spaCy processing. * Add yield_bunsetu back in as a separate piece of code Co-authored-by: Hiroshi Matsuda <40782025+hiroshi-matsuda-rit@users.noreply.github.com> Co-authored-by: hiroshi <hiroshi_matsuda@megagon.ai>
2020-06-04 20:15:43 +03:00
tokens = ja_tokenizer(text)
assert tokens.text_with_ws == text
2020-06-21 23:38:04 +03:00
@pytest.mark.parametrize(
"text,len_a,len_b,len_c",
[
("選挙管理委員会", 4, 3, 1),
("客室乗務員", 3, 2, 1),
("労働者協同組合", 4, 3, 1),
("機能性食品", 3, 2, 1),
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],
)
def test_ja_tokenizer_split_modes(ja_tokenizer, text, len_a, len_b, len_c):
Refactor pipeline components, config and language data (#5759) * Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-22 14:42:59 +03:00
nlp_a = Japanese.from_config({"nlp": {"tokenizer": {"split_mode": "A"}}})
nlp_b = Japanese.from_config({"nlp": {"tokenizer": {"split_mode": "B"}}})
nlp_c = Japanese.from_config({"nlp": {"tokenizer": {"split_mode": "C"}}})
assert len(ja_tokenizer(text)) == len_a
assert len(nlp_a(text)) == len_a
assert len(nlp_b(text)) == len_b
assert len(nlp_c(text)) == len_c
@pytest.mark.parametrize("text,sub_tokens_list_b,sub_tokens_list_c", SUB_TOKEN_TESTS)
2020-07-25 16:01:15 +03:00
def test_ja_tokenizer_sub_tokens(
ja_tokenizer, text, sub_tokens_list_b, sub_tokens_list_c
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):
nlp_a = Japanese.from_config({"nlp": {"tokenizer": {"split_mode": "A"}}})
nlp_b = Japanese.from_config({"nlp": {"tokenizer": {"split_mode": "B"}}})
nlp_c = Japanese.from_config({"nlp": {"tokenizer": {"split_mode": "C"}}})
assert ja_tokenizer(text).user_data.get("sub_tokens") is None
assert nlp_a(text).user_data.get("sub_tokens") is None
assert nlp_b(text).user_data["sub_tokens"] == sub_tokens_list_b
assert nlp_c(text).user_data["sub_tokens"] == sub_tokens_list_c
2020-07-25 16:01:15 +03:00
@pytest.mark.parametrize(
"text,inflections,reading_forms",
[
(
"取ってつけた",
(["五段-ラ行;連用形-促音便"], [], ["下一段-カ行;連用形-一般"], ["助動詞-タ;終止形-一般"]),
(["トッ"], [""], ["ツケ"], [""]),
),
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("2=3", ([], [], []), ([""], ["_"], ["サン"])),
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],
)
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def test_ja_tokenizer_inflections_reading_forms(
ja_tokenizer, text, inflections, reading_forms
):
tokens = ja_tokenizer(text)
test_inflections = [tt.morph.get("Inflection") for tt in tokens]
assert test_inflections == list(inflections)
test_readings = [tt.morph.get("Reading") for tt in tokens]
assert test_readings == list(reading_forms)
def test_ja_tokenizer_emptyish_texts(ja_tokenizer):
doc = ja_tokenizer("")
assert len(doc) == 0
doc = ja_tokenizer(" ")
assert len(doc) == 1
doc = ja_tokenizer("\n\n\n \t\t \n\n\n")
assert len(doc) == 1