| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: bart_large_summarise |
| results: [] |
| --- |
| |
|  |
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| This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the SGH news articles and summaries dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.7389 |
| - Rouge1: 0.5297 |
| - Rouge2: 0.3602 |
| - Rougel: 0.3961 |
| - Rougelsum: 0.4821 |
| - Gen Len: 137.9091 |
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| ## Model description |
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| This model was created to generate summaries of news articles. |
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| ## Intended uses & limitations |
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| The model takes up to maximum article length of 1024 tokens and generates a summary of maximum length of 512 tokens. |
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| ## Training data |
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| This model was trained on 100+ articles and summaries from SGH. |
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| ### Training hyperparameters |
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| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - num_epochs: 10 |
| - label_smoothing_factor: 0.1 |
| |
| ### Framework versions |
| |
| - Transformers 4.23.1 |
| - Pytorch 1.12.1+cu113 |
| - Datasets 2.6.1 |
| - Tokenizers 0.13.1 |
| |
