def __init__(self, data, type=1):
print("Build char-word based NER Task...")
super(CW_NER, self).__init__()
self.gpu = data.HP_gpu
label_size = data.label_alphabet_size
self.type = type
self.gaz_embed = Gaz_Embed(data, type)
self.word_embedding = nn.Embedding(data.word_alphabet.size(),
data.word_emb_dim)
self.lstm = Gaz_BiLSTM(data, data.word_emb_dim + data.gaz_emb_dim,
data.HP_hidden_dim)
self.crf = CRF(data.label_alphabet_size, self.gpu)
self.hidden2tag = nn.Linear(data.HP_hidden_dim * 2,
data.label_alphabet_size + 2)
if data.pretrain_word_embedding is not None:
self.word_embedding.weight.data.copy_(
torch.from_numpy(data.pretrain_word_embedding))
else:
self.word_embedding.weight.data.copy_(
random_embedding(data.word_alphabet_size, data.word_emb_dim))
if self.gpu:
self.word_embedding = self.word_embedding.cuda()
self.hidden2tag = self.hidden2tag.cuda()
def __init__(self, data):
super(Char_NER, self).__init__()
print("Build Character-based BiLSTM-CRF....")
self.gpu = data.HP_gpu
self.embedding_dim = data.char_emb_dim
self.hidden_dim = data.HP_hidden_dim
self.char_embedding = nn.Embedding(data.char_alphabet.size(), self.embedding_dim)
self.lstm = Char_BiLSTM(data)
self.hidden2tag = nn.Linear(self.hidden_dim * 2, data.label_alphabet_size + 2)
self.crf = CRF(data.label_alphabet_size, self.gpu)
if data.pretrain_char_embedding is not None:
self.char_embedding.weight.data.copy_(
torch.from_numpy(data.pretrain_char_embedding)
)
else:
self.char_embedding.weight.data.copy_(
random_embedding(data.char_alphabet.size(), self.embedding_dim)
)
if self.gpu:
self.char_embedding = self.char_embedding.cuda()
self.hidden2tag = self.hidden2tag.cuda()
def __init__(self, data, type=1):
"""
Args:
data: all the data information
type: the type of strategy, 1 for avg, 2 for short first, 3 for long first
"""
print('build gaz embedding...')
super(Gaz_Embed, self).__init__()
self.gpu = data.HP_gpu
self.data = data
self.type = type
self.gaz_dim = data.gaz_emb_dim
self.gaz_embedding = nn.Embedding(data.gaz_alphabet.size(),
data.gaz_emb_dim)
self.dropout = nn.Dropout(p=0.5)
if data.pretrain_gaz_embedding is not None:
self.gaz_embedding.weight.data.copy_(
torch.from_numpy(data.pretrain_gaz_embedding))
else:
self.gaz_embedding.weight.data.copy_(
torch.from_numpy(
random_embedding(data.gaz_alphabet.size(),
data.gaz_emb_dim)))
self.filters = [[1, 20], [2, 30]]
if self.type == 4:
# use conv, so we need to define some conv
# here we use 20 1-d conv, and 30 2-d conv
self.build_cnn(self.filters)
## also use highway, 2 layers highway
# self.highway = Highway(self.gaz_dim, num_layers=2)
# if self.gpu:
# self.highway = self.highway.cuda()
if self.type == 5:
# use self-attention
self.build_attention()
if self.gpu:
self.gaz_embedding = self.gaz_embedding.cuda()