| --- |
| 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 |
|
|
| --- |
|
|
| ## 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). |
|
|
| ```python |
| from datasets import Dataset |
| dataset = Dataset.from_pandas(df) |
| dataset = dataset.train_test_split(test_size=0.2, seed=42) |
| |
