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* whitespace
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@ -84,7 +84,7 @@ def _merge_sents(sents):
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def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
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seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
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verbose=False,
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eta=0.01, mu=0.9, n_hidden=100,
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eta=0.01, mu=0.9, nv_hidden=100,
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nv_word=10, nv_tag=10, nv_label=10):
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dep_model_dir = path.join(model_dir, 'deps')
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pos_model_dir = path.join(model_dir, 'pos')
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@ -105,7 +105,7 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
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features=feat_set,
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labels=Language.ParserTransitionSystem.get_labels(gold_tuples),
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vector_lengths=(nv_word, nv_tag, nv_label),
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hidden_nodes=n_hidden,
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hidden_nodes=nv_hidden,
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eta=eta,
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mu=mu
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)
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@ -123,7 +123,7 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
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(nv_tag * len(tags)) + \
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(nv_label * len(labels))
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print 'Compiling'
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debug, train_func, predict_func = compile_theano_model(n_classes, n_hidden,
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debug, train_func, predict_func = compile_theano_model(n_classes, nv_hidden,
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n_in, 0.0, 0.0)
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print 'Done'
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return TheanoModel(
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@ -251,7 +251,7 @@ def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbos
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gold_train = list(read_json_file(train_loc))
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nlp = train(English, gold_train, model_dir,
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feat_set='embed',
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nv_word=nv_word, nv_tag=nv_tag, nv_label=nv_label,
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nv_word=nv_word, nv_tag=nv_tag, nv_label=nv_label, nv_hidden=nv_hidden,
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gold_preproc=gold_preproc, n_sents=n_sents,
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corruption_level=corruption_level, n_iter=n_iter,
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verbose=verbose)
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