README.md

metadata

license: cc-by-4.0
language:
  - en
tags:
  - summarization
  - text-generation
  - NLP
  - transformers
datasets:
  - your-dataset-name

BART Fine-Tuned Summarization Model

This repository hosts a BART-based model fine-tuned for text summarization on a custom dataset of articles and highlights. The model is suitable for generating concise summaries from long-form text.

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Model Overview

• Base Model: facebook/bart-large-cnn
• Task: Text Summarization
• Fine-Tuning Dataset: Custom CSV dataset containing document and summary columns
• Dataset Size: Varies depending on your CSV file
• Framework: Hugging Face Transformers
• Language: English

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Dataset Preparation

1. Load your CSV dataset containing columns: article (renamed to document) and highlights (renamed to summary).
2. Clean the dataset by removing missing or non-string entries.
3. Split the dataset into train and validation sets (80/20 split).

from datasets import Dataset
dataset = Dataset.from_pandas(df)
dataset = dataset.train_test_split(test_size=0.2, seed=42)