def __init__(self, vocab_num, embed_dim=100, bigram_vocab_num=None, bigram_embed_dim=100, num_bigram_per_char=None,
hidden_size=200, bidirectional=True, embed_drop_p=0.2, num_layers=1, tag_size=4):
"""
默认使用BMES的标注方式
:param vocab_num:
:param embed_dim:
:param bigram_vocab_num:
:param bigram_embed_dim:
:param num_bigram_per_char:
:param hidden_size:
:param bidirectional:
:param embed_drop_p:
:param num_layers:
:param tag_size:
"""
super(CWSBiLSTMCRF, self).__init__()
self.tag_size = tag_size
self.encoder_model = CWSBiLSTMEncoder(vocab_num, embed_dim, bigram_vocab_num, bigram_embed_dim, num_bigram_per_char,
hidden_size, bidirectional, embed_drop_p, num_layers)
size_layer = [hidden_size, 200, tag_size]
self.decoder_model = MLP(size_layer)
allowed_trans = allowed_transitions({0:'b', 1:'m', 2:'e', 3:'s'}, encoding_type='bmes')
self.crf = ConditionalRandomField(num_tags=tag_size, include_start_end_trans=False,
allowed_transitions=allowed_trans)
def __init__(self, vocab_num, embed_dim=100, bigram_vocab_num=None, bigram_embed_dim=100, num_bigram_per_char=None,
hidden_size=200, bidirectional=True, embed_drop_p=None, num_layers=1, tag_size=2):
super(CWSBiLSTMSegApp, self).__init__()
self.tag_size = tag_size
self.encoder_model = CWSBiLSTMEncoder(vocab_num, embed_dim, bigram_vocab_num, bigram_embed_dim, num_bigram_per_char,
hidden_size, bidirectional, embed_drop_p, num_layers)
size_layer = [hidden_size, 200, tag_size]
self.decoder_model = MLP(size_layer)
def __init__(self, vocab_num, embed_dim=100, bigram_vocab_num=None, bigram_embed_dim=100, num_bigram_per_char=None,
hidden_size=200, bidirectional=True, embed_drop_p=None, num_layers=1, tag_size=4):
super(CWSBiLSTMCRF, self).__init__()
self.tag_size = tag_size
self.encoder_model = CWSBiLSTMEncoder(vocab_num, embed_dim, bigram_vocab_num, bigram_embed_dim, num_bigram_per_char,
hidden_size, bidirectional, embed_drop_p, num_layers)
size_layer = [hidden_size, 200, tag_size]
self.decoder_model = MLP(size_layer)
self.crf = ConditionalRandomField(tag_size=tag_size, include_start_end_trans=False)
def __init__(self, args=None):
super(SELF_ATTENTION_YELP_CLASSIFICATION, self).__init__()
self.embedding = Embedding(len(word2index),
embeding_size,
init_emb=None)
self.lstm = LSTM(input_size=embeding_size,
hidden_size=lstm_hidden_size,
bidirectional=True)
self.attention = SelfAttention(lstm_hidden_size * 2,
dim=attention_unit,
num_vec=attention_hops)
self.mlp = MLP(
size_layer=[lstm_hidden_size * 2 * attention_hops, nfc, class_num])
def __init__(self, in_feature_dim, out_feature_dim):
super(NaiveClassifier, self).__init__()
self.mlp = MLP([in_feature_dim, in_feature_dim, out_feature_dim])