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Improve defaults
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@ -138,7 +138,7 @@ class TokenVectorEncoder(BaseThincComponent):
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name = 'tensorizer'
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@classmethod
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def Model(cls, width=128, embed_size=7500, **cfg):
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def Model(cls, width=128, embed_size=4000, **cfg):
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"""Create a new statistical model for the class.
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width (int): Output size of the model.
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@ -238,12 +238,12 @@ cdef class Parser:
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Base class of the DependencyParser and EntityRecognizer.
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"""
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@classmethod
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def Model(cls, nr_class, token_vector_width=128, hidden_width=128, depth=1, **cfg):
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def Model(cls, nr_class, token_vector_width=128, hidden_width=300, depth=1, **cfg):
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depth = util.env_opt('parser_hidden_depth', depth)
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token_vector_width = util.env_opt('token_vector_width', token_vector_width)
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hidden_width = util.env_opt('hidden_width', hidden_width)
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parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2)
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embed_size = util.env_opt('embed_size', 7500)
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embed_size = util.env_opt('embed_size', 4000)
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tensors = fine_tune(Tok2Vec(token_vector_width, embed_size,
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preprocess=doc2feats()))
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if parser_maxout_pieces == 1:
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