def __init__(self, params):
super(BiencoderRanker, self).__init__()
self.params = params
# self.ctxt_bert=BertModel_new.from_pretrained(params['context_bert_model'],config=BertConfig_new.from_pretrained(params['context_bert_model']))
self.ctxt_bert = load_model('./model/bert-large-uncased',
params['context_bert_model'])
self.cand_encoder = wiki_encoder.WikiEncoderModule(params)
# self.load_cand_encoder_state()
self.context_encoder = BertEncoder(
self.ctxt_bert,
params["out_dim"],
layer_pulled=params["pull_from_layer"],
add_linear=params["add_linear"],
)
self.mention_score = MentionScoresHead(bert_output_dim=768,
score_method='qa_linear',
max_mention_length=10)
self.mention_loss = MentionLoss()
# self.change_mention_embedding_dim = nn.Linear(768,1024)
# self.change_mention_embedding_dim=nn.Sequential(
# nn.Linear(768, 768),
# nn.ReLU(),
# nn.Dropout(0.1),
# nn.Linear(768, 1024),
# )
#冻结参数
for param in self.cand_encoder.parameters():
param.requires_grad = True
def __init__(self, params):
super(BiencoderRanker, self).__init__()
self.params = params
self.ctxt_bert = BertModel_new.from_pretrained(
params['context_bert_model'],
config=BertConfig_new.from_pretrained(
params['context_bert_model']))
self.cand_encoder = wiki_encoder.WikiEncoderModule(params)
self.load_cand_encoder_state()
self.context_encoder = BertEncoder(
self.ctxt_bert,
params["out_dim"],
layer_pulled=params["pull_from_layer"],
add_linear=params["add_linear"],
)
self.mention_score = mention_detection.MentionScoresHead(
bert_output_dim=768, score_method='qa_mlp', max_mention_length=10)
self.mention_loss = mention_detection.MentionLoss()
self.change_mention_embedding_dim = nn.Linear(768, 1024)
#冻结参数
for param in self.cand_encoder.parameters():
param.requires_grad = False