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--- |
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license: mit |
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base_model: facebook/bart-large-cnn |
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tags: |
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- generated_from_trainer |
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datasets: |
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- samsum |
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model-index: |
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- name: bart-samsum-finetuned |
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results: [] |
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metrics: |
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- bertscore |
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- bleu |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bart-samsum-finetuned |
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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 samsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1326 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.1196 | 1.0 | 74 | 0.1362 | |
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| 0.0948 | 2.0 | 148 | 0.1334 | |
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| 0.0738 | 3.0 | 222 | 0.1326 | |
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### Evaluation results |
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Rouge Scores: |
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| Metric | Precision | Recall | F-Measure | |
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|:----------:|:-----------------:|:-----------------:|:--------------------:| |
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| rouge1 | Low - 0.2923 | Low - 0.5755 | Low - 0.3645 | |
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| | Mid - 0.3012 | Mid - 0.5881 | Mid - 0.3722 | |
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| | High - 0.3108 | High - 0.6011 | High - 0.3811 | |
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| rouge2 | Low - 0.1185 | Low - 0.2418 | Low - 0.1481 | |
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| | Mid - 0.1252 | Mid - 0.2545 | Mid - 0.1555 | |
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| | High - 0.1321 | High - 0.2682 | High - 0.1632 | |
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| rougeL | Low - 0.2182 | Low - 0.4434 | Low - 0.2744 | |
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| | Mid - 0.2251 | Mid - 0.4547 | Mid - 0.2810 | |
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| | High - 0.2328 | High - 0.4679 | High - 0.2886 | |
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| rougeLsum | Low - 0.2178 | Low - 0.4425 | Low - 0.2739 | |
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| | Mid - 0.2249 | Mid - 0.4546 | Mid - 0.2807 | |
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| | High - 0.2321 | High - 0.4679 | High - 0.2883 | |
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BERTScore: |
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| Precision | Recall | F1 | |
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|:---------:|:---------:|:---------:| |
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| 0.6054495 | 0.6918860 | 0.6425597 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
