def __init__(self, out=3, out_src=0, bert_type='bert'):
super(Bert_finetuning, self).__init__()
_, self.bert_model = bert.get_bert(bert_type=bert_type)
'''
#self.fc=nn.Linear(768,out)
#print(self.bert_model)
self.fc=nn.Sequential(
#nn.BatchNorm1d(768),
#nn.ReLU(inplace=True),
#nn.LayerNorm(768),
#nn.Dropout(p=0.1,inplace=False),
nn.Linear(768,out),
#nn.GELU()
#nn.LayerNorm(out),
#nn.Dropout(p=0.1,inplace=False)
#nn.LogSigmoid()
)
'''
#self.dropout = torch.nn.Dropout(0.1)#0.1
#torch.nn.init.normal_(self.fc[0].weight.data,mean=0.0,std=0.02)
#torch.nn.init.normal_(self.fc[0].weight.data,mean=0.0,std=0.02)
#self.fc.weight.data.uniform_(0.0,0.02)
#self.fc.weight.data.fill_(0.1)
#self.fc[0].bias.data.fill_(0)
#print("0.5 -1.1,-1 -1")
self.trensfer = out_src != 0
self.out_layers = nn.Linear(768, out)
self.out_src_layers = nn.Linear(768,
out_src) if self.trensfer else None
def __init__(self, out=3, out_src=0, bert_type='bert'):
super(Bert_finetuning_CNN_Entity, self).__init__()
_, self.bert_model = bert.get_bert(bert_type=bert_type)
self.cnn1 = CNN(inc=768,
Dr=[0.25, 0.25],
nb_filtres=[3],
FC_L_size=[100],
ker_size=[3, 2],
padding=False,
maxpooling_dir=True,
out=100,
out_src=out_src)
self.cnn2 = CNN(inc=768,
Dr=[0.25, 0.25],
nb_filtres=[3],
FC_L_size=[100],
ker_size=[3, 2],
padding=False,
maxpooling_dir=True,
out=100,
out_src=out_src)
self.cnn3 = CNN(inc=768,
Dr=[0.25, 0.25],
nb_filtres=[3],
FC_L_size=[100],
ker_size=[3, 2],
padding=False,
maxpooling_dir=True,
out=100,
out_src=out_src)
self.cnn4 = CNN(inc=768,
Dr=[0.25, 0.25],
nb_filtres=[3],
FC_L_size=[100],
ker_size=[3, 2],
padding=False,
maxpooling_dir=True,
out=100,
out_src=out_src)
self.cnn5 = CNN(inc=768,
Dr=[0.25, 0.25],
nb_filtres=[3],
FC_L_size=[100],
ker_size=[3, 2],
padding=False,
maxpooling_dir=True,
out=100,
out_src=out_src)
self.fc = nn.Sequential(nn.LayerNorm(500),
nn.Dropout(p=0.1, inplace=False),
nn.Linear(500, 100), nn.Hardtanh(),
nn.LayerNorm(100), nn.Linear(100, out),
nn.Hardtanh())
def __init__(self, out=3, out_src=0, bert_type='bert'):
super(Bert_finetuning_CNN_resedual, self).__init__()
_, self.bert_model = bert.get_bert(bert_type=bert_type)
self.cnn = CNN(inc=768,
nb_filtres=[3],
FC_L_size=[768],
ker_size=[3, 2],
padding=False,
maxpooling_dir=True,
out=out,
out_src=0,
w_size=768)
def __init__(self, out=3, out_src=0, bert_type='bert'):
super(Bert_finetuning_Mixed, self).__init__()
_, self.bert_model = bert.get_bert(bert_type=bert_type)
self.cnn = CNN(inc=768,
Dr=[0.5, 0.5],
nb_filtres=[8],
FC_L_size=[768],
ker_size=[3, 2],
padding=False,
maxpooling_dir=True,
out=out,
out_src=out_src)
self.out_layers = nn.Linear(768, out)