| import torch | |
| from torch import nn | |
| from transformers import BertModel | |
| class BertClassifier(nn.Module): | |
| def __init__(self, dropout=0.3): | |
| super(BertClassifier, self).__init__() | |
| self.bert = BertModel.from_pretrained("bert-base-uncased") | |
| self.dropout = nn.Dropout(dropout) | |
| self.classifier = nn.Linear(self.bert.config.hidden_size, 4) | |
| def forward(self, input_ids, attention_mask): | |
| outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) | |
| pooled_output = outputs.pooler_output | |
| dropped = self.dropout(pooled_output) | |
| return self.classifier(dropped) |