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Add function to make morphologizer model
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28
spacy/_ml.py
28
spacy/_ml.py
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@ -483,7 +483,33 @@ class MultiSoftmax(Affine):
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return output__BO, finish_update
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return output__BO, finish_update
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def build_tagger_model(class_nums, **cfg):
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def build_tagger_model(nr_class, **cfg):
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embed_size = util.env_opt('embed_size', 7000)
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if 'token_vector_width' in cfg:
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token_vector_width = cfg['token_vector_width']
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else:
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token_vector_width = util.env_opt('token_vector_width', 128)
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pretrained_vectors = cfg.get('pretrained_vectors')
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subword_features = cfg.get('subword_features', True)
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with Model.define_operators({'>>': chain, '+': add}):
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if 'tok2vec' in cfg:
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tok2vec = cfg['tok2vec']
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else:
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tok2vec = Tok2Vec(token_vector_width, embed_size,
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subword_features=subword_features,
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pretrained_vectors=pretrained_vectors)
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softmax = with_flatten(
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Softmax(nr_class, token_vector_width))
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model = (
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tok2vec
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>> softmax
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)
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model.nI = None
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model.tok2vec = tok2vec
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model.softmax = softmax
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return model
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def build_morphologizer_model(class_nums, **cfg):
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embed_size = util.env_opt('embed_size', 7000)
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embed_size = util.env_opt('embed_size', 7000)
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if 'token_vector_width' in cfg:
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if 'token_vector_width' in cfg:
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token_vector_width = cfg['token_vector_width']
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token_vector_width = cfg['token_vector_width']
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