models/qa_model.py

import torch
import torch.nn as nn

class QAModel(nn.Module):
    def __init__(self, vocab_size, embed_dim=200, hidden_dim=256):
        super().__init__()

        self.embedding = nn.Embedding(vocab_size, embed_dim)

        self.lstm = nn.LSTM(
            embed_dim,
            hidden_dim,
            batch_first=True,
            bidirectional=True
        )

        self.fc_start = nn.Linear(hidden_dim*2, 1)
        self.fc_end = nn.Linear(hidden_dim*2, 1)

    def forward(self, x):
        x = self.embedding(x)
        out, _ = self.lstm(x)

        start = self.fc_start(out).squeeze(-1)
        end = self.fc_end(out).squeeze(-1)

        return start, end