Fulltrain Xsum Bart

🚀 fulltrain-xsum-bart

This is a fine-tuned model on the XSum dataset for abstractive summarization tasks, which can generate concise summaries from long documents.

✨ Features

• Architecture: BART (Bidirectional and Auto-Regressive Transformers)
• Task: Abstractive Summarization
• Dataset: XSum (Extreme Summarization)
• Training Hardware: 2x NVIDIA T4 GPUs (using Kaggle)
• Training Time: ~9 hours

📦 Installation

Since this model is based on the transformers library, you can install it using the following command:

pip install transformers

💻 Usage Examples

Basic Usage

from transformers import pipeline

# Load the few-shot model
summarizer = pipeline("summarization", model="bhargavis/fulltrain-xsum-bart")

# Provide input text
input_text = """
Authorities have issued a warning after multiple sightings of a large brown bear in the woods. The bear is known to become aggressive if disturbed, and residents are urged to exercise caution. Last week, a group of hikers reported a close encounter with the animal. While no injuries were sustained, the bear displayed defensive behavior when approached. Wildlife officials advise keeping a safe distance and avoiding the area if possible. Those encountering the bear should remain calm, back away slowly, and refrain from making sudden movements. Officials continue to monitor the situation.
"""

# Generate summary
summary = summarizer(input_text, max_length=64, min_length=30, do_sample=False)
print(summary[0]["summary_text"])

📚 Documentation

Dataset Details

The XSum dataset consists of BBC articles and their corresponding single-sentence summaries. The model was trained to generate summaries that are concise and capture the essence of the input document.

Training Details

Evaluation Metrics

These metrics were computed using the rouge_scorer library for ROUGE scores.

Training Arguments

🔧 Technical Details

The model is fine-tuned on the XSum dataset for abstractive summarization tasks. It uses the BART architecture, which is a pre-trained sequence-to-sequence model. The training process involves adjusting the model's weights to minimize the loss on the training data while maximizing the ROUGE scores on the validation data.

📄 License

This project is licensed under the MIT License.