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---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: HealthScienceBART
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HealthScienceBART
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7248
- Rouge1: 59.8432
- Rouge2: 25.926
- Rougel: 44.3683
- Rougelsum: 56.3382
- Bertscore Precision: 84.199
- Bertscore Recall: 85.5429
- Bertscore F1: 84.8633
- Bleu: 0.2087
- Gen Len: 234.8216
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 | Bleu | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 5.662 | 0.0826 | 100 | 5.4864 | 49.8946 | 18.6145 | 35.6824 | 47.1811 | 80.6966 | 82.5402 | 81.6048 | 0.1476 | 234.8216 |
| 5.2036 | 0.1653 | 200 | 4.9823 | 52.1848 | 20.4176 | 37.3029 | 48.9924 | 81.1422 | 83.2665 | 82.1871 | 0.1634 | 234.8216 |
| 4.7061 | 0.2479 | 300 | 4.6422 | 54.5492 | 21.4905 | 38.8501 | 51.1097 | 82.0428 | 83.8584 | 82.9376 | 0.1730 | 234.8216 |
| 4.657 | 0.3305 | 400 | 4.4252 | 54.072 | 22.1609 | 39.6324 | 50.5966 | 81.9494 | 84.1622 | 83.0371 | 0.1793 | 234.8216 |
| 4.3613 | 0.4131 | 500 | 4.2631 | 56.8149 | 23.0471 | 40.9892 | 53.0419 | 83.0301 | 84.669 | 83.8388 | 0.1871 | 234.8216 |
| 4.2804 | 0.4958 | 600 | 4.1142 | 56.8254 | 23.7321 | 41.7326 | 52.8585 | 82.8372 | 84.8241 | 83.8154 | 0.1915 | 234.8216 |
| 4.2477 | 0.5784 | 700 | 3.9926 | 57.2046 | 23.9303 | 42.3439 | 53.6018 | 83.216 | 84.9845 | 84.0878 | 0.1929 | 234.8216 |
| 4.1188 | 0.6610 | 800 | 3.9193 | 57.9987 | 24.8441 | 43.1811 | 54.4399 | 83.6075 | 85.2031 | 84.395 | 0.1999 | 234.8216 |
| 3.8678 | 0.7436 | 900 | 3.8320 | 59.1683 | 25.1465 | 43.4643 | 55.6762 | 83.9212 | 85.315 | 84.6099 | 0.2019 | 234.8216 |
| 3.8831 | 0.8263 | 1000 | 3.7889 | 59.3948 | 25.4051 | 43.821 | 55.8124 | 84.0802 | 85.4569 | 84.7606 | 0.2044 | 234.8216 |
| 3.7856 | 0.9089 | 1100 | 3.7498 | 59.535 | 25.6124 | 44.1831 | 56.071 | 84.0653 | 85.4796 | 84.7641 | 0.2063 | 234.8216 |
| 3.8875 | 0.9915 | 1200 | 3.7248 | 59.8432 | 25.926 | 44.3683 | 56.3382 | 84.199 | 85.5429 | 84.8633 | 0.2087 | 234.8216 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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