regression_models.py

import torch
import torch.nn as nn
from transformers import BertModel, BertTokenizer

class BERTRegression(nn.Module):
    def __init__(self):
        super(BERTRegression, self).__init__()
        self.bert = BertModel.from_pretrained("bert-base-uncased")
        self.dropout = nn.Dropout(0.1)
        self.linear = nn.Linear(self.bert.config.hidden_size, 1)

    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = outputs.pooler_output
        pooled_output = self.dropout(pooled_output)
        logits = self.linear(pooled_output)
        return logits.squeeze(-1)