This repository contains the script finetuned_bart.py, designed for fine-tuning, evaluating, and using the BART (Bidirectional and Auto-Regressive Transformers) model for text summarization. BART, developed by Facebook AI, excels in natural language understanding and generation tasks.
- Fine-Tuning: Adapt the BART model to specific characteristics of a custom dataset for improved summarization.
- Summarization: Demonstrate the use of the fine-tuned model to generate summaries for new texts.
- Python 3.9
- PyTorch
- Transformers Library (Hugging Face)
- NLTK
https://uscode.house.gov/download/download.shtml
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Fine-Tuning the Model:
- Fine-tune the BART model on your dataset by running the script. Ensure your dataset is compatible with the script's data processing functions.
- Adjust hyperparameters like batch size, learning rate, and epochs as needed.
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Generating Summaries:
- Generate summaries for new text inputs using the fine-tuned model.
- The
generate_summaryfunction in the script takes a text input and outputs its summary.
Modify the script to meet specific requirements, such as changing the dataset, adjusting preprocessing steps, tweaking model parameters, or using different evaluation metrics.