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Add -prune-vectors argument to spacy.cly.train
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@ -32,6 +32,7 @@ numpy.random.seed(0)
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n_sents=("number of sentences", "option", "ns", int),
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use_gpu=("Use GPU", "option", "g", int),
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vectors=("Model to load vectors from", "option", "v"),
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vectors_limit=("Truncate to N vectors (requires -v)", "option", None, int),
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no_tagger=("Don't train tagger", "flag", "T", bool),
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no_parser=("Don't train parser", "flag", "P", bool),
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no_entities=("Don't train NER", "flag", "N", bool),
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@ -40,9 +41,9 @@ numpy.random.seed(0)
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meta_path=("Optional path to meta.json. All relevant properties will be "
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"overwritten.", "option", "m", Path))
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def train(cmd, lang, output_dir, train_data, dev_data, n_iter=30, n_sents=0,
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use_gpu=-1, vectors=None, no_tagger=False, no_parser=False,
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no_entities=False, gold_preproc=False, version="0.0.0",
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meta_path=None):
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use_gpu=-1, vectors=None, vectors_limit=None, no_tagger=False,
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no_parser=False, no_entities=False, gold_preproc=False,
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version="0.0.0", meta_path=None):
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"""
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Train a model. Expects data in spaCy's JSON format.
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"""
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@ -94,6 +95,8 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=30, n_sents=0,
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nlp.meta.update(meta)
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if vectors:
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util.load_model(vectors, vocab=nlp.vocab)
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if vectors_limit is not None:
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nlp.vocab.prune_vectors(vectors_limit)
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for name in pipeline:
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nlp.add_pipe(nlp.create_pipe(name), name=name)
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optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
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