Perplexity: The model achieves a perplexity of around 10 on the validation set.
Accuracy: Not applicable for text generation tasks.
Strengths:
Contextual Understanding: The model demonstrates good contextual understanding based on the training data.
Text Generation: It can generate coherent text based on the input prompt.
Weaknesses:
Limited Vocabulary: The model's vocabulary is limited to the training dataset.
Overfitting: There is a risk of overfitting due to the relatively small size of the training dataset.
Future Improvements
Increase Training Data: Using a larger and more diverse dataset can improve the model's performance and vocabulary.
Hyperparameter Tuning: Experimenting with different hyperparameters (e.g., batch size, epochs, learning rate) may enhance the model's accuracy and efficiency.
Model Architecture: Exploring other architectures like Transformers might offer better performance for text generation tasks.
Contributing
Contributions are welcome! If you have suggestions or improvements, please submit a pull request or open an issue.
License
This repository is licensed under the MIT License. See LICENSE for details.
