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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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metrics: |
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- rouge |
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- bleu |
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model-index: |
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- name: LifeScienceBARTPrincipal |
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results: [] |
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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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# LifeScienceBARTPrincipal |
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.6738 |
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- Rouge1: 49.5951 |
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- Rouge2: 14.7925 |
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- Rougel: 33.5728 |
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- Rougelsum: 46.0607 |
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- Bertscore Precision: 81.3188 |
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- Bertscore Recall: 83.0508 |
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- Bertscore F1: 82.1725 |
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- Bleu: 0.1002 |
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- Gen Len: 227.8658 |
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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: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 16 |
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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: 500 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 | Bleu | Gen Len | |
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|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:| |
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| 6.522 | 0.0881 | 100 | 6.3500 | 39.9657 | 10.1487 | 26.5496 | 37.2024 | 77.4604 | 80.5886 | 78.9876 | 0.0657 | 227.8658 | |
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| 6.0286 | 0.1762 | 200 | 5.9070 | 38.2642 | 10.0835 | 26.309 | 35.3774 | 76.6233 | 80.7328 | 78.6174 | 0.0677 | 227.8658 | |
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| 5.633 | 0.2643 | 300 | 5.5510 | 46.5639 | 12.278 | 29.4476 | 43.4837 | 79.3971 | 81.6021 | 80.4809 | 0.0788 | 227.8658 | |
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| 5.4363 | 0.3524 | 400 | 5.3379 | 45.7917 | 12.3778 | 29.8157 | 42.5627 | 79.5999 | 81.8994 | 80.7293 | 0.0818 | 227.8658 | |
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| 5.3556 | 0.4405 | 500 | 5.1825 | 45.7896 | 12.7724 | 30.8163 | 42.7893 | 80.125 | 82.0725 | 81.0835 | 0.0839 | 227.8658 | |
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| 5.176 | 0.5286 | 600 | 5.0198 | 46.4704 | 13.3572 | 31.4246 | 43.3941 | 80.2167 | 82.2861 | 81.2343 | 0.0877 | 227.8658 | |
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| 5.0712 | 0.6167 | 700 | 4.9271 | 49.2365 | 14.1585 | 32.4079 | 45.6186 | 81.1084 | 82.7557 | 81.9207 | 0.0939 | 227.8658 | |
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| 4.9175 | 0.7048 | 800 | 4.8257 | 48.3333 | 14.04 | 32.7436 | 44.7264 | 80.8876 | 82.7874 | 81.8231 | 0.0947 | 227.8658 | |
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| 4.9291 | 0.7929 | 900 | 4.7636 | 49.1012 | 14.4773 | 33.113 | 45.5053 | 80.9593 | 82.9139 | 81.9214 | 0.0977 | 227.8658 | |
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| 4.6748 | 0.8810 | 1000 | 4.7083 | 49.9025 | 14.8866 | 33.4691 | 46.352 | 81.3587 | 83.0297 | 82.1826 | 0.1002 | 227.8658 | |
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| 4.8064 | 0.9691 | 1100 | 4.6738 | 49.5951 | 14.7925 | 33.5728 | 46.0607 | 81.3188 | 83.0508 | 82.1725 | 0.1002 | 227.8658 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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