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Check out the documentation for more information.
Neural Network Language Model for Next Token Prediction
Overview
This project implements a neural network-based language model designed to predict the next token in a sequence of text. By leveraging a variety of neural network architectures, including Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, this model aims to generate coherent and contextually relevant text. Predicts the next token in a sequence. Utilizes RNN and LSTM architectures for better performance. Supports multiple languages (English and others). Includes data preprocessing and tokenization scripts. Installation
Clone this repository: bash Copy code git clone https://github.com/yourusername/neural-network-language-model.git cd neural-network-language-model Install the required packages: bash Copy code pip install -r requirements.txt Usage
Prepare your dataset by placing it in the data/ directory. Preprocess the data: bash Copy code python preprocess.py Train the model: bash Copy code python train.py Predict the next token: bash Copy code python predict.py --input "Your input text here" Data
This model can be trained on various text datasets. For this project, datasets in [languages] (e.g., English, Finnish, Italian, Latin, Punjabi) have been used. The data should be formatted in plain text files.
Model Architecture
The model is based on a neural network architecture that includes:
Embedding Layer: Converts tokens to dense vectors. RNN/LSTM Layer: Processes sequences and captures temporal dependencies. Dense Layer: Outputs the predicted probabilities for the next token. Training
The model is trained using [specify loss function, optimizer, and metrics]. Training configurations can be adjusted in the train.py file.
Evaluation
Model performance can be evaluated using metrics such as accuracy and perplexity. Results can be generated by running:
bash Copy code python evaluate.py Results
The model's performance on various test sets can be found in the results/ directory. Performance metrics, including accuracy and loss, will be documented here.