def __init__(self, config_path):
super().__init__(config_path)
# self.bert_ranker = VanillaBertRanker()
self.topk = 20
self.attention = modeling_util.Attention(self.ATTEN_SIZE) # combine+transform
# self.transform = torch.nn.Linear(self.BERT_SIZE,self.ATTEN_SIZE)#combine+transform
# self.transform_ent = torch.nn.Linear(self.ENTITY_SIZE,self.ATTEN_SIZE)#combine+transform
self.linear = torch.nn.Linear(self.topk, 1)
self.out = torch.nn.Linear(1, 1)
def __init__(self,QLEN):
super().__init__(QLEN)
# self.bert_ranker = VanillaBertRanker()
self.topk = 20
self.BERT_SIZE = 768
self.attention = modeling_util.Attention(self.BERT_SIZE)
self.linear = torch.nn.Linear(self.topk,1)
self.out = torch.nn.Linear(13,1)
def __init__(self, config):
super().__init__(config)
#self.bert_ranker = VanillaBertRanker()
self.topk = 20
# self.BERT_SIZE = 768
# self.ATTEN_SIZE = 500
self.attention = modeling_util.Attention(
self.ATTEN_SIZE) #combine+transform
self.linear = torch.nn.Linear(self.topk, 1)
self.out = torch.nn.Linear(self.BERT_SIZE + 13, 1)
def __init__(self, config):
super().__init__(config)
#self.bert_ranker = VanillaBertRanker()
self.topk = 20
self.BERT_SIZE = 768
self.ENTITY_SIZE = 100
self.attention = modeling_util.Attention(self.BERT_SIZE +
self.ENTITY_SIZE)
self.transform = torch.nn.Linear(self.BERT_SIZE + self.ENTITY_SIZE,
self.BERT_SIZE + self.ENTITY_SIZE)
self.linear = torch.nn.Linear(self.topk, 1)
self.dropout = torch.nn.Dropout(0.1)
self.out = torch.nn.Linear(self.BERT_SIZE + 13, 1) # combine+transform
