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https://github.com/explosion/spaCy.git
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43b960c01b
* 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>
118 lines
3.3 KiB
Python
118 lines
3.3 KiB
Python
from typing import Set, Optional, Any, Dict
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from thinc.api import Config
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from .stop_words import STOP_WORDS
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from .tag_map import TAG_MAP
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from ...language import Language
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from ...tokens import Doc
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from ...compat import copy_reg
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from ...util import DummyTokenizer, registry
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DEFAULT_CONFIG = """
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[nlp]
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lang = "ko"
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stop_words = {"@language_data": "spacy.ko.stop_words"}
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[nlp.tokenizer]
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@tokenizers = "spacy.KoreanTokenizer.v1"
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[nlp.writing_system]
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direction = "ltr"
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has_case = false
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has_letters = false
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"""
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@registry.language_data("spacy.ko.stop_words")
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def stop_words() -> Set[str]:
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return STOP_WORDS
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@registry.tokenizers("spacy.KoreanTokenizer.v1")
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def create_korean_tokenizer():
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def korean_tokenizer_factory(nlp):
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return KoreanTokenizer(nlp)
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return korean_tokenizer_factory
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class KoreanTokenizer(DummyTokenizer):
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def __init__(self, nlp: Optional[Language] = None):
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self.vocab = nlp.vocab
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MeCab = try_mecab_import()
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self.mecab_tokenizer = MeCab("-F%f[0],%f[7]")
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def __del__(self):
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self.mecab_tokenizer.__del__()
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def __call__(self, text: str) -> Doc:
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dtokens = list(self.detailed_tokens(text))
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surfaces = [dt["surface"] for dt in dtokens]
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doc = Doc(self.vocab, words=surfaces, spaces=list(check_spaces(text, surfaces)))
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for token, dtoken in zip(doc, dtokens):
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first_tag, sep, eomi_tags = dtoken["tag"].partition("+")
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token.tag_ = first_tag # stem(어간) or pre-final(선어말 어미)
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token.lemma_ = dtoken["lemma"]
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doc.user_data["full_tags"] = [dt["tag"] for dt in dtokens]
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return doc
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def detailed_tokens(self, text: str) -> Dict[str, Any]:
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# 품사 태그(POS)[0], 의미 부류(semantic class)[1], 종성 유무(jongseong)[2], 읽기(reading)[3],
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# 타입(type)[4], 첫번째 품사(start pos)[5], 마지막 품사(end pos)[6], 표현(expression)[7], *
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for node in self.mecab_tokenizer.parse(text, as_nodes=True):
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if node.is_eos():
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break
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surface = node.surface
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feature = node.feature
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tag, _, expr = feature.partition(",")
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lemma, _, remainder = expr.partition("/")
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if lemma == "*":
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lemma = surface
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yield {"surface": surface, "lemma": lemma, "tag": tag}
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class KoreanDefaults(Language.Defaults):
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tag_map = TAG_MAP
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class Korean(Language):
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lang = "ko"
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Defaults = KoreanDefaults
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default_config = Config().from_str(DEFAULT_CONFIG)
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def try_mecab_import() -> None:
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try:
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from natto import MeCab
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return MeCab
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except ImportError:
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raise ImportError(
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"Korean support requires [mecab-ko](https://bitbucket.org/eunjeon/mecab-ko/src/master/README.md), "
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"[mecab-ko-dic](https://bitbucket.org/eunjeon/mecab-ko-dic), "
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"and [natto-py](https://github.com/buruzaemon/natto-py)"
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)
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def check_spaces(text, tokens):
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prev_end = -1
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start = 0
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for token in tokens:
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idx = text.find(token, start)
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if prev_end > 0:
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yield prev_end != idx
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prev_end = idx + len(token)
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start = prev_end
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if start > 0:
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yield False
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def pickle_korean(instance):
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return Korean, tuple()
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copy_reg.pickle(Korean, pickle_korean)
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__all__ = ["Korean"]
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