sdump2/NeuralNetwork_Based_Language_Model_for_Next_Token_Prediction

Neural-Network-Language-Model-for-Next-Token-Prediction

Overview

This project implements a next-token prediction model using an LSTM-based neural network. The model can generate text in English and another assigned language, trained using a corpus of sequential data. It leverages the GPT-2 tokenizer for encoding and decoding text.

Setup

1) Install Dependencies: <pre> pip install torch transformers matplotlib pandas </pre> 2) Run the Model: Make sure your text file is correctly placed (e.g., input1.txt in /content/sample_data). Update the path as needed in the code.

Usage

1. 1.Train the Model

The training loop processes a given text corpus, logs the training and validation loss, and saves performance metrics. Adjust hyperparameters such as:

• —Embedding dimension: 128
• —LSTM hidden units: 256
• —Learning rate: 0.001
• —Epochs: 5

1. 1.Generate Text

Run the generatesequence() function with a prompt. Example: <pre> starttextenglish = "The journey begins" starttextotherlanguage = "Por favor, deme tres consejos" </pre>

Expected output: <pre> Generated English Text: The journey begins with the first step...

Generated Other Language Text: Por favor, deme tres consejos para mejorar...

</pre>

1. 1.Model Explanation:

• —Training Loss: Measures the error between the model’s predictions and ground truth on the training dataset.

• —Validation Loss: Evaluates the model's performance on unseen data to detect overfitting. If validation loss increases while training loss decreases, it could indicate overfitting.

1. 1.Results and Observations:

• —Training and validation loss graphs: Visualize how well the model generalizes.
• —Generated Text: Demonstrates the model’s ability to predict meaningful sequences in both languages.

1. 1.Future Improvements:

• —More Epochs: Improve performance with longer training.
• —Hyperparameter Tuning: Explore different values for hidden units, learning rates, and layers.