def __init__(self, char_init_embed, word_init_embed, pos_init_embed, spo_embed_dim, sentence_length,
hidden_size, num_classes, dropout=0.3, id2words=None, encoding_type='bieso', weight=None):
super().__init__()
# self.Embedding = nn.Embedding(init_embed)
# print(char_init_embed)
self.char_embed = nn.Embedding(char_init_embed[0], char_init_embed[1])
self.word_embed = nn.Embedding(word_init_embed[0], word_init_embed[1])
# word2vec
self.word_embed.weight.data.copy_(torch.from_numpy(weight))
self.pos_embed = nn.Embedding(pos_init_embed[0], pos_init_embed[1])
# spo embed size: 50
self.embed_dim = self.char_embed.embedding_dim + self.word_embed.embedding_dim + self.pos_embed.embedding_dim + spo_embed_dim
# sentence length
#self.sen_len = sentence_length
#self.zeros = torch.zeros(self.sen_len, dtype=torch.long)
self.norm1 = torch.nn.LayerNorm(self.embed_dim)
self.Rnn = nn.LSTM(input_size=self.embed_dim, hidden_size=hidden_size, num_layers=2,
dropout=dropout, bidirectional=True, batch_first=True)
self.Linear1 = nn.Linear(hidden_size * 2, hidden_size * 2 // 3)
self.norm2 = torch.nn.LayerNorm(hidden_size * 2 // 3)
self.relu = torch.nn.LeakyReLU()
self.drop = torch.nn.Dropout(dropout)
self.Linear2 = nn.Linear(hidden_size * 2 // 3, num_classes)
if id2words is None:
self.Crf = CRF(num_classes, include_start_end_trans=False)
else:
self.Crf = CRF(num_classes, include_start_end_trans=False,
allowed_transitions=allowed_transitions(id2words, encoding_type=encoding_type))
def __init__(self,
char_init_embed,
word_init_embed,
pos_init_embed,
spo_embed_dim,
num_classes,
num_layers,
inner_size,
key_size,
value_size,
num_head,
dropout=0.1,
id2words=None,
encoding_type='bieso'):
super().__init__()
# self.Embedding = nn.Embedding(init_embed)
#print(char_init_embed)
self.char_embed = nn.Embedding(char_init_embed[0], char_init_embed[1])
self.word_embed = nn.Embedding(word_init_embed[0], word_init_embed[1])
self.pos_embed = nn.Embedding(pos_init_embed[0], pos_init_embed[1])
# spo embed size: 50
self.embed_dim = self.char_embed.embedding_dim + self.word_embed.embedding_dim + self.pos_embed.embedding_dim + spo_embed_dim
self.norm1 = torch.nn.LayerNorm(self.embed_dim)
# self.Rnn = encoder.LSTM(input_size=self.embed_dim, hidden_size=hidden_size, num_layers=2,
# dropout=dropout, bidirectional=True, batch_first=True)
self.transformer = encoder.TransformerEncoder(
num_layers=num_layers,
model_size=self.embed_dim,
inner_size=inner_size,
key_size=key_size,
value_size=value_size,
num_head=num_head,
dropout=dropout)
self.Linear1 = nn.Linear(self.embed_dim, self.embed_dim // 3)
self.norm2 = torch.nn.LayerNorm(self.embed_dim // 3)
self.relu = torch.nn.LeakyReLU()
self.drop = torch.nn.Dropout(dropout)
self.Linear2 = nn.Linear(self.embed_dim // 3, num_classes)
self.Linear = nn.Linear(self.embed_dim, num_classes)
if id2words is None:
self.Crf = CRF(num_classes, include_start_end_trans=False)
else:
self.Crf = CRF(num_classes,
include_start_end_trans=False,
allowed_transitions=allowed_transitions(
id2words, encoding_type=encoding_type))
def __init__(self,
char_init_embed,
word_init_embed,
pos_init_embed,
spo_embed_dim,
num_classes,
num_layers,
inner_size,
key_size,
value_size,
num_head,
dropout=0.1,
id2words=None,
encoding_type='bieso',
weight=None):
super().__init__()
# self.Embedding = nn.Embedding(init_embed)
#print(char_init_embed)
self.char_embed = nn.Embedding(char_init_embed[0], char_init_embed[1])
self.word_embed = nn.Embedding(word_init_embed[0], word_init_embed[1])
self.word_embed.weight.data.copy_(torch.from_numpy(weight))
self.pos_embed = nn.Embedding(pos_init_embed[0], pos_init_embed[1])
# spo embed size: 50
self.embed_dim = self.char_embed.embedding_dim + self.word_embed.embedding_dim + self.pos_embed.embedding_dim + spo_embed_dim
self.norm1 = torch.nn.LayerNorm(self.embed_dim)
self.transformer = encoder.TransformerEncoder(
num_layers=num_layers,
model_size=self.embed_dim,
inner_size=inner_size,
key_size=key_size,
value_size=value_size,
num_head=num_head,
dropout=dropout)
self.Linear1 = nn.Linear(self.embed_dim, self.embed_dim // 3)
self.norm2 = torch.nn.LayerNorm(self.embed_dim // 3)
self.relu = torch.nn.LeakyReLU()
self.drop = torch.nn.Dropout(dropout)
self.Linear2 = nn.Linear(self.embed_dim // 3, num_classes)
self.Linear = nn.Linear(self.embed_dim, num_classes)
if id2words is None:
self.Crf = CRF(num_classes, include_start_end_trans=False)
else:
self.Crf = CRF(num_classes,
include_start_end_trans=False,
allowed_transitions=allowed_transitions(
id2words, encoding_type=encoding_type))