model/sentiment_model.py

import torch.nn as nn
from transformers import BertModel


class SentimentAnalysisModel(nn.Module):
    def __init__(self, pretrained_model_name="bert-base-uncased"):
        super().__init__()

        self.bert = BertModel.from_pretrained(pretrained_model_name)
        self.dropout = nn.Dropout(0.3)
        self.classifier = nn.Linear(self.bert.config.hidden_size, 2)

    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.classifier(pooled_output)

        return logits