|
|
|
|
|
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 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
