Analysis
- Performance Metrics:
- 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.
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