Milan63/automated-movie-ratings-lstm

LSTM Text Classification Model

Model Details

• Model Type: LSTM
• Task: Text Classification
• Input Dimension: 100
• Hidden Size: 256
• Number of Layers: 2
• Number of Labels: 2

Usage

This model is designed for text classification with two possible labels. It takes tokenized input sequences and processes them using an LSTM architecture.

Requirements

• Ensure that your input sequences are properly preprocessed and padded to match input_dim = 100.
• Use a compatible tokenizer before passing input to the model.

Example Usage (PyTorch)

import torch
import torch.nn as nn

class LSTMClassifier(nn.Module):
    def __init__(self, input_dim, hidden_size, num_layers, num_labels):
        super(LSTMClassifier, self).__init__()
        self.lstm = nn.LSTM(input_dim, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_labels)
    
    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        output = self.fc(lstm_out[:, -1, :])
        return output

# Load model with config.json parameters
config = {
    "input_dim": 100,
    "hidden_size": 256,
    "num_layers": 2,
    "num_labels": 2
}

model = LSTMClassifier(**config)
dummy_input = torch.randn(1, 10, config["input_dim"])  # Example input
output = model(dummy_input)
print(output)