| |
| |
| import torch |
| import torch.nn as nn |
| import torch |
| from torch.autograd import Variable |
| import copy |
| import torch.nn.functional as F |
| from torch.nn import CrossEntropyLoss, MSELoss |
|
|
| class RobertaClassificationHead(nn.Module): |
| """Head for sentence-level classification tasks.""" |
|
|
| def __init__(self, config): |
| super().__init__() |
| self.dense = nn.Linear(config.hidden_size*2, config.hidden_size) |
| self.dropout = nn.Dropout(config.hidden_dropout_prob) |
| self.out_proj = nn.Linear(config.hidden_size, 2) |
|
|
| def forward(self, features, **kwargs): |
| x = features[:, 0, :] |
| x = x.reshape(-1,x.size(-1)*2) |
| x = self.dropout(x) |
| x = self.dense(x) |
| x = torch.tanh(x) |
| x = self.dropout(x) |
| x = self.out_proj(x) |
| return x |
| |
| class Model(nn.Module): |
| def __init__(self, encoder,config,tokenizer,args): |
| super(Model, self).__init__() |
| self.encoder = encoder |
| self.config=config |
| self.tokenizer=tokenizer |
| self.classifier=RobertaClassificationHead(config) |
| self.args=args |
| |
| |
| def forward(self, input_ids=None,labels=None, return_vec=None): |
| input_ids=input_ids.view(-1,self.args.block_size) |
| outputs = self.encoder(input_ids= input_ids,attention_mask=input_ids.ne(1)) |
|
|
| if return_vec: |
| return outputs.pooler_output |
|
|
| outputs = outputs[0] |
| logits=self.classifier(outputs) |
| prob=F.softmax(logits) |
|
|
| if labels is not None: |
| loss_fct = CrossEntropyLoss() |
| loss = loss_fct(logits, labels) |
| return loss,prob |
| else: |
| return prob |
| |
| |
| |
| |
|
|
|
|
|
|
