def __init__(self, config, classes_num):
super(ERENet, self).__init__(config, classes_num)
print('spo_transformers_freeze')
self.classes_num = classes_num
# BERT model
self.bert = BertModel(config)
for p in self.bert.parameters():
p.requires_grad = False
self.lstm_encoder = SentenceEncoder(config.hidden_size,
config.hidden_size // 2)
# self.token_entity_emb = nn.Embedding(num_embeddings=2, embedding_dim=config.hidden_size,
# padding_idx=0)
self.LayerNorm = ConditionalLayerNorm(config.hidden_size,
eps=config.layer_norm_eps)
# pointer net work
self.po_dense = nn.Linear(config.hidden_size, self.classes_num * 2)
self.subject_dense = nn.Linear(config.hidden_size, 2)
self.loss_fct = nn.BCEWithLogitsLoss(reduction='none')
# self.init_weights()
self.apply(self.init_bert_weights)
def __init__(self, args, word_emb, ent_conf, spo_conf):
print('mhs using only char2v+w2v mixed and word_emb is freeze ')
super(ERENet, self).__init__()
self.max_len = args.max_len
self.word_emb = nn.Embedding.from_pretrained(torch.tensor(word_emb, dtype=torch.float32), freeze=True,
padding_idx=0)
self.char_emb = nn.Embedding(num_embeddings=args.char_vocab_size, embedding_dim=args.char_emb_size,
padding_idx=0)
self.word_convert_char = nn.Linear(args.word_emb_size, args.char_emb_size, bias=False)
self.classes_num = len(spo_conf)
self.first_sentence_encoder = SentenceEncoder(args, args.char_emb_size)
# self.second_sentence_encoder = SentenceEncoder(args, args.hidden_size)
# self.token_entity_emb = nn.Embedding(num_embeddings=2, embedding_dim=config.hidden_size,
# padding_idx=0)
self.encoder_layer = TransformerEncoderLayer(args.hidden_size * 2, nhead=3)
self.transformer_encoder = TransformerEncoder(self.encoder_layer, num_layers=1)
self.LayerNorm = ConditionalLayerNorm(args.hidden_size * 2, eps=1e-12)
# self.subject_dense = nn.Linear(args.hidden_size * 2, 2)
self.ent_emission = nn.Linear(args.hidden_size * 2, len(ent_conf))
self.ent_crf = CRF(len(ent_conf), batch_first=True)
self.emission = nn.Linear(args.hidden_size * 2, len(spo_conf))
self.crf = CRF(len(spo_conf), batch_first=True)
self.loss_fct = nn.BCEWithLogitsLoss(reduction='none')
def __init__(self, args, word_emb, spo_conf):
print('drug extract using only char2v')
super(ERENet, self).__init__()
# self.word_emb = nn.Embedding.from_pretrained(torch.tensor(word_emb, dtype=torch.float32), freeze=True,
# padding_idx=0)
self.char_emb = nn.Embedding(num_embeddings=args.char_vocab_size,
embedding_dim=args.char_emb_size,
padding_idx=0)
# self.word_convert_char = nn.Linear(args.word_emb_size, args.char_emb_size, bias=False)
self.classes_num = len(spo_conf)
self.first_sentence_encoder = SentenceEncoder(args, args.char_emb_size)
self.second_sentence_encoder = SentenceEncoder(args,
args.hidden_size * 2)
# self.second_sentence_encoder = SentenceEncoder(args, args.hidden_size)
self.token_entity_emb = nn.Embedding(num_embeddings=2,
embedding_dim=args.hidden_size *
2,
padding_idx=0)
self.encoder_layer = TransformerEncoderLayer(args.hidden_size * 2,
nhead=3)
self.transformer_encoder = TransformerEncoder(self.encoder_layer,
num_layers=1)
self.LayerNorm = ConditionalLayerNorm(args.hidden_size * 2, eps=1e-12)
# pointer net work
self.po_dense = nn.Linear(args.hidden_size * 2, self.classes_num * 2)
self.subject_dense = nn.Linear(args.hidden_size * 2, 2)
self.loss_fct = nn.BCEWithLogitsLoss(reduction='none')
def __init__(self, config, classes_num):
super(ERENet, self).__init__(config, classes_num)
self.classes_num = classes_num
self.bert = BertModel(config)
self.token_entity_emb = nn.Embedding(num_embeddings=2, embedding_dim=config.hidden_size,
padding_idx=0)
# self.encoder_layer = TransformerEncoderLayer(config.hidden_size, nhead=4)
# self.transformer_encoder = TransformerEncoder(self.encoder_layer, num_layers=1)
self.LayerNorm = ConditionalLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
# pointer net work
self.po_dense = nn.Linear(config.hidden_size, self.classes_num * 2)
self.subject_dense = nn.Linear(config.hidden_size, 2)
self.loss_fct = nn.BCEWithLogitsLoss(reduction='none')
self.apply(self.init_bert_weights)
def __init__(self, config, classes_num):
super(ERENet, self).__init__(config, classes_num)
self.classes_num = classes_num
# BERT model
self.bert = BertModel(config)
self.token_entity_emb = nn.Embedding(num_embeddings=2, embedding_dim=config.hidden_size,
padding_idx=0)
self.LayerNorm = ConditionalLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
# pointer net work
self.po_dense = nn.Linear(config.hidden_size, self.classes_num * 2)
self.subject_dense = nn.Linear(config.hidden_size, 2)
self.loss_fct = nn.BCEWithLogitsLoss(reduction='none')
self.init_weights()
def __init__(self, config, classes_num):
super(EntityRelationExtractionNet, self).__init__(config, classes_num)
self.classes_num = classes_num
self.bert = BertModel(config)
self.token_entity_emb = nn.Embedding(num_embeddings=2,
embedding_dim=config.hidden_size,
padding_idx=0)
self.LayerNorm = ConditionalLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
# Pointer Net Work
self.po_dense = nn.Linear(config.hidden_size, self.classes_num * 2)
self.subject_dense = nn.Linear(config.hidden_size, 2)
# 二进制交叉熵损失函数：sigmoid操作和与BCELoss集合
self.loss_fct = nn.BCEWithLogitsLoss(reduction='none')
self.init_weights()