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,
embed_drop_p=0.3,
num_layers=1,
num_heads=8,
tag_size=4):
super().__init__()
self.embedding = nn.Embedding(vocab_num, embed_dim)
input_size = embed_dim
if bigram_vocab_num:
self.bigram_embedding = nn.Embedding(bigram_vocab_num,
bigram_embed_dim)
input_size += num_bigram_per_char * bigram_embed_dim
self.drop = nn.Dropout(embed_drop_p, inplace=True)
self.fc1 = nn.Linear(input_size, hidden_size)
value_size = hidden_size // num_heads
self.transformer = TransformerEncoder(num_layers,
model_size=hidden_size,
inner_size=hidden_size,
key_size=value_size,
value_size=value_size,
num_head=num_heads)
self.fc2 = nn.Linear(hidden_size, tag_size)
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, args, emb=None, id2words=None):
super(AdvSeqLabel, self).__init__(args)
vocab_size = args["vocab_size"]
word_emb_dim = args["word_emb_dim"]
hidden_dim = args["rnn_hidden_units"]
num_classes = args["num_classes"]
dropout = args['dropout']
self.Embedding = encoder.embedding.Embedding(vocab_size,
word_emb_dim,
init_emb=emb)
self.norm1 = torch.nn.LayerNorm(word_emb_dim)
# self.Rnn = encoder.lstm.LSTM(word_emb_dim, hidden_dim, num_layers=2, dropout=dropout, bidirectional=True)
self.Rnn = torch.nn.LSTM(input_size=word_emb_dim,
hidden_size=hidden_dim,
num_layers=2,
dropout=dropout,
bidirectional=True,
batch_first=True)
self.Linear1 = encoder.Linear(hidden_dim * 2, hidden_dim * 2 // 3)
self.norm2 = torch.nn.LayerNorm(hidden_dim * 2 // 3)
# self.batch_norm = torch.nn.BatchNorm1d(hidden_dim * 2 // 3)
self.relu = torch.nn.LeakyReLU()
self.drop = torch.nn.Dropout(dropout)
self.Linear2 = encoder.Linear(hidden_dim * 2 // 3, num_classes)
if id2words is None:
self.Crf = decoder.CRF.ConditionalRandomField(
num_classes, include_start_end_trans=False)
else:
self.Crf = decoder.CRF.ConditionalRandomField(
num_classes,
include_start_end_trans=False,
allowed_transitions=allowed_transitions(id2words,
encoding_type="bmes"))
def test_case1(self):
# 检查allowed_transitions()能否正确使用
from fastNLP.modules.decoder.CRF import allowed_transitions
id2label = {0: 'B', 1: 'I', 2: 'O'}
expected_res = {(0, 0), (0, 1), (0, 2), (0, 4), (1, 0), (1, 1), (1, 2),
(1, 4), (2, 0), (2, 2), (2, 4), (3, 0), (3, 2)}
self.assertSetEqual(expected_res, set(allowed_transitions(id2label)))
id2label = {0: 'B', 1: 'M', 2: 'E', 3: 'S'}
expected_res = {(0, 1), (0, 2), (1, 1), (1, 2), (2, 0), (2, 3), (2, 5),
(3, 0), (3, 3), (3, 5), (4, 0), (4, 3)}
self.assertSetEqual(
expected_res,
set(allowed_transitions(id2label, encoding_type='BMES')))
id2label = {0: 'B', 1: 'I', 2: 'O', 3: '<pad>', 4: "<unk>"}
allowed_transitions(id2label)
labels = ['O']
for label in ['X', 'Y']:
for tag in 'BI':
labels.append('{}-{}'.format(tag, label))
id2label = {idx: label for idx, label in enumerate(labels)}
expected_res = {(0, 0), (0, 1), (0, 3), (0, 6), (1, 0), (1, 1), (1, 2),
(1, 3), (1, 6), (2, 0), (2, 1), (2, 2), (2, 3), (2, 6),
(3, 0), (3, 1), (3, 3), (3, 4), (3, 6), (4, 0), (4, 1),
(4, 3), (4, 4), (4, 6), (5, 0), (5, 1), (5, 3)}
self.assertSetEqual(expected_res, set(allowed_transitions(id2label)))
labels = []
for label in ['X', 'Y']:
for tag in 'BMES':
labels.append('{}-{}'.format(tag, label))
id2label = {idx: label for idx, label in enumerate(labels)}
expected_res = {(0, 1), (0, 2), (1, 1), (1, 2), (2, 0), (2, 3), (2, 4),
(2, 7), (2, 9), (3, 0), (3, 3), (3, 4), (3, 7), (3, 9),
(4, 5), (4, 6), (5, 5), (5, 6), (6, 0), (6, 3), (6, 4),
(6, 7), (6, 9), (7, 0), (7, 3), (7, 4), (7, 7), (7, 9),
(8, 0), (8, 3), (8, 4), (8, 7)}
self.assertSetEqual(
expected_res,
set(allowed_transitions(id2label, encoding_type='BMES')))