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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: HealthScienceBARTPrincipal |
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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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# HealthScienceBARTPrincipal |
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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: 3.8639 |
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- Rouge1: 57.9681 |
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- Rouge2: 23.5702 |
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- Rougel: 42.298 |
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- Rougelsum: 54.4306 |
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- Bertscore Precision: 83.6132 |
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- Bertscore Recall: 84.9752 |
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- Bertscore F1: 84.2861 |
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- Bleu: 0.1834 |
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- Gen Len: 234.8649 |
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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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| 5.8822 | 0.0826 | 100 | 5.6709 | 48.7438 | 17.0806 | 33.6533 | 45.9891 | 80.2777 | 82.0236 | 81.1384 | 0.1276 | 234.8898 | |
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| 5.2537 | 0.1653 | 200 | 5.1160 | 48.5934 | 17.8578 | 34.7839 | 45.8306 | 80.2943 | 82.3539 | 81.3073 | 0.1358 | 234.8898 | |
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| 4.8915 | 0.2479 | 300 | 4.7665 | 53.8863 | 19.5528 | 37.1095 | 50.6262 | 81.6835 | 83.1962 | 82.4302 | 0.1499 | 234.8898 | |
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| 4.6879 | 0.3305 | 400 | 4.5500 | 53.0399 | 20.3314 | 38.0481 | 49.3041 | 81.2912 | 83.6329 | 82.4409 | 0.1582 | 234.8898 | |
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| 4.4472 | 0.4131 | 500 | 4.3787 | 55.7809 | 21.4354 | 39.5787 | 52.1713 | 82.3882 | 84.0466 | 83.2062 | 0.1663 | 234.8898 | |
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| 4.4391 | 0.4958 | 600 | 4.2267 | 55.0551 | 21.5312 | 39.9051 | 51.3866 | 82.1951 | 84.1433 | 83.1541 | 0.1686 | 234.8898 | |
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| 4.386 | 0.5784 | 700 | 4.1013 | 56.2812 | 22.3834 | 40.9161 | 52.93 | 82.9407 | 84.4308 | 83.6764 | 0.1738 | 234.8898 | |
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| 4.198 | 0.6610 | 800 | 4.0168 | 56.3251 | 22.6045 | 41.1441 | 52.8715 | 83.2275 | 84.6518 | 83.931 | 0.1762 | 234.8898 | |
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| 3.9607 | 0.7436 | 900 | 3.9377 | 57.4072 | 22.9187 | 41.6959 | 53.899 | 83.4352 | 84.8095 | 84.1141 | 0.1787 | 234.8898 | |
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| 3.9771 | 0.8263 | 1000 | 3.8963 | 58.1506 | 23.5231 | 42.1596 | 54.4019 | 83.6132 | 85.0153 | 84.3057 | 0.1842 | 234.8898 | |
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| 3.8807 | 0.9089 | 1100 | 3.8447 | 57.9746 | 23.8219 | 42.4743 | 54.4461 | 83.6303 | 85.03 | 84.3217 | 0.1867 | 234.8898 | |
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| 4.0011 | 0.9915 | 1200 | 3.8214 | 58.2153 | 23.8513 | 42.5964 | 54.7631 | 83.7005 | 85.0498 | 84.3672 | 0.1867 | 234.8898 | |
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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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