model.py

import torch
import torch.nn as nn


class LSTMModel(nn.Module):
    def __init__(self, embedding_matrix, hidden_size=256, num_layers=2, dropout=0.2):
        super(LSTMModel, self).__init__()
        num_embeddings, embedding_dim = embedding_matrix.shape
        self.embedding = nn.Embedding(num_embeddings, embedding_dim)
        self.embedding.weight = nn.Parameter(
            torch.tensor(embedding_matrix, dtype=torch.float32)
        )
        self.embedding.weight.requires_grad = False  # Do not train the embedding layer

        self.lstm = nn.LSTM(
            input_size=embedding_matrix.shape[1],
            hidden_size=hidden_size,
            num_layers=num_layers,
            batch_first=True,
            dropout=dropout,
        )
        self.fc = nn.Linear(hidden_size, 1)

    def forward(self, title, text):
        title_emb = self.embedding(title)
        text_emb = self.embedding(text)
        combined = torch.cat((title_emb, text_emb), dim=1)
        output, (hidden, _) = self.lstm(combined)
        out = self.fc(hidden[-1])
        return torch.sigmoid(out)