mirror of https://github.com/explosion/spaCy.git
Update ud_train script
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@ -247,12 +247,18 @@ Token.set_extension('inside_fused', default=False)
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##################
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def load_nlp(corpus, config):
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def load_nlp(corpus, config, vectors=None):
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lang = corpus.split('_')[0]
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nlp = spacy.blank(lang)
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if config.vectors:
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nlp.vocab.from_disk(Path(config.vectors) / 'vocab')
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if not vectors:
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raise ValueError("config asks for vectors, but no vectors "
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"directory set on command line (use -v)")
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if (Path(vectors) / corpus).exists():
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nlp.vocab.from_disk(Path(vectors) / corpus / 'vocab')
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nlp.meta['treebank'] = corpus
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return nlp
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def initialize_pipeline(nlp, docs, golds, config, device):
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nlp.add_pipe(nlp.create_pipe('parser'))
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@ -274,10 +280,12 @@ def initialize_pipeline(nlp, docs, golds, config, device):
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class Config(object):
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def __init__(self, vectors=None, max_doc_length=10, multitask_tag=True,
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multitask_sent=True, nr_epoch=30, batch_size=1000, dropout=0.2):
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multitask_sent=True, multitask_dep=True, multitask_vectors=False,
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nr_epoch=30, batch_size=1000, dropout=0.2):
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for key, value in locals().items():
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setattr(self, key, value)
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@classmethod
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def load(cls, loc):
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with Path(loc).open('r', encoding='utf8') as file_:
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@ -319,9 +327,11 @@ class TreebankPaths(object):
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parses_dir=("Directory to write the development parses", "positional", None, Path),
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config=("Path to json formatted config file", "positional"),
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limit=("Size limit", "option", "n", int),
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use_gpu=("Use GPU", "option", "g", int)
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use_gpu=("Use GPU", "option", "g", int),
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vectors_dir=("Path to directory with pre-trained vectors, named e.g. en/",
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"option", "v", Path),
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)
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def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1):
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def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=None):
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spacy.util.fix_random_seed()
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lang.zh.Chinese.Defaults.use_jieba = False
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lang.ja.Japanese.Defaults.use_janome = False
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@ -331,7 +341,7 @@ def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1):
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if not (parses_dir / corpus).exists():
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(parses_dir / corpus).mkdir()
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print("Train and evaluate", corpus, "using lang", paths.lang)
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nlp = load_nlp(paths.lang, config)
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nlp = load_nlp(paths.lang, config, vectors=vectors_dir)
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docs, golds = read_data(nlp, paths.train.conllu.open(), paths.train.text.open(),
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max_doc_length=config.max_doc_length, limit=limit)
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