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from torch import nn
from transformers import BertModel,BertConfig
from transformers.modeling_outputs import TokenClassifierOutput
class BertClassifier(nn.Module):
def __init__(self, num_labels=2, dropout=0.1,bert_model=None):
super().__init__()
if bert_model:
self.bert = BertModel.from_pretrained(bert_model)
else:
config = BertConfig(vocab_size=34688, max_position_embeddings=512)
self.bert = BertModel(config=config)
self.num_labels = num_labels
self.classifier = nn.Sequential(
nn.Linear(self.bert.config.hidden_size, self.bert.config.hidden_size),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(self.bert.config.hidden_size, num_labels))
def forward(self, input_ids=None, attention_mask=None,labels=None):
output = self.bert(input_ids, attention_mask=attention_mask)
logits = self.classifier(output.pooler_output)
loss = None
if labels:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=output.hidden_states,attentions=output.attentions)
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