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Next Token Prediction Language Model
This repository contains a neural network-based model designed for next token prediction. The model is developed as part of a midterm project to work with two languages: English and Norwegian. The architecture used is [insert architecture used: RNN, LSTM, or NN] based, and it does not utilize transformer models.
Model Overview
The model was trained using a dataset of large corpora in both English and Norwegian languages. The task of the model is to predict the next token in a sequence, making it a useful tool for language modeling and text generation tasks.
Training Details
Training Steps: [number of steps trained]
Loss Metrics:
- Initial Train Loss: 5.7090
- Final Train Loss: 2.2550
- Final Validation Loss: 2.3366
Languages Used: English and Norwegian
Architecture: [Specify whether it's RNN, LSTM, or NN]
Checkpoints: Model checkpoints are saved after every 500 steps and are available in this repository.
Usage
To use this model, load the model and use it for token prediction or text generation by feeding it sequences from the two supported languages.
Example usage:
from keras.models import load_model
model = load_model('path_to_model')
# Use the model for token prediction
How to Run
To run the model on your local machine, follow the steps below:
Download the model and checkpoints.
Load the model in your preferred environment (e.g., TensorFlow, PyTorch).
Run inference by providing a sequence from the dataset used.