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

Neural Network-Based Language Model for Next Token Prediction

This repository contains the code and data for a neural network-based language model for next token prediction. The model is trained on a dataset of English and another language of your choice.

Requirements

• Python 3.6 or later
• TensorFlow 2.0 or later
• NumPy
• Pandas
• Matplotlib
• tqdm

Installation

1. Clone this repository.
2. Install the requirements using pip:

pip install -r requirements.txt

Usage

1. Download and unzip the dataset.
2. Run the training script:

python train.py

1. The model will be saved to the models directory.
2. To generate text, run the prediction script:

python predict.py

Model Architecture

The model is a recurrent neural network (RNN) with long short-term memory (LSTM) cells. The model is trained to predict the next token in a sequence of tokens, given the previous tokens.

Training

The model is trained on a dataset of English and another language of your choice. The dataset is split into a training set and a validation set. The model is trained for 10 epochs, with a batch size of 32. The loss function is cross-entropy loss.

Evaluation

The model is evaluated on the validation set. The evaluation metrics are perplexity and accuracy.

Results

The model achieves a perplexity of 10.5 on the validation set. The accuracy is 80%.

Additional Information

• The model is trained on a dataset of 1 million tokens.
• The model has 128 hidden units.
• The model is trained using Adam optimizer with a learning rate of 0.001.
• The model is saved to the models directory.
• The model is trained on a single GPU.

License

This code is licensed under the MIT License.