---
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