我尝试从bert模型的最后一个隐藏状态获得输出，并将其通过CNN-model
class MixModel(nn.Module):
def __init__(self,pre_trained='distilbert-base-uncased'):
super().__init__()
self.bert = AutoModel.from_pretrained('distilbert-base-uncased')
self.hidden_size = self.bert.config.hidden_size
self.conv = nn.Conv1d(in_channels=1, out_channels=256, kernel_size=5, padding='valid', stride=1)
self.relu = nn.ReLU()
self.pool = nn.MaxPool1d(kernel_size= 256- 5 + 1)
self.dropout = nn.Dropout(0.3)
self.clf = nn.Linear(self.hidden_size*2,6)
def forward(self,inputs, mask , labels):
cls_hs = self.bert(input_ids=inputs,attention_mask=mask, return_dict= False)
x=cls_hs
# x = torch.cat(cls_hs[0]) # x= [416, 64, 768]
x = self.conv(x)
x = self.relu(x)
x = self.pool(x)
x = self.dropout(x)
x = self.clf(x)
return x
我犯了这个错误TypeError:conv1d((接收到无效的参数组合-get(tuple，Parameter，Parameter，tuple、str、tuple和int(，但应为以下之一：
- (张量输入，张量权重，张量偏差，int步长元组，int填充元组，int扩展元组，int组(不匹配，因为某些参数具有无效的类型：(！tuple！，！Parameter！，参数！，！tuple！，！str！，，！tuble！，int(
- (张量输入、张量权重、张量偏差、int步长元组、str填充、int扩展元组、int组(不匹配，因为某些参数的类型无效：(！tuple！，！Parameter！，，！Parameter！，！tuple！，str，！tuble！，int(
def __init__(self,pre_trained='distilbert-base-uncased'):
super().__init__()
self.bert = AutoModel.from_pretrained('distilbert-base-uncased')
self.hidden_size = self.bert.config.hidden_size
self.conv = nn.Conv1d(in_channels=768, out_channels=256, kernel_size=5, padding='valid', stride=1)
self.relu = nn.ReLU()
self.pool = nn.MaxPool1d(kernel_size= 256- 5 + 1)
self.dropout = nn.Dropout(0.3)
self.clf1 = nn.Linear(256,256)
self.clf2 = nn.Linear(256,6)
也在x = self.conv(x)使用permute(0,2,1)之前in_channel=变压器的输出(768(out_channel=由您决定(过滤器数量(