---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: HealthScienceBARTPrincipal
  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. -->

# HealthScienceBARTPrincipal

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: 3.8639
- Rouge1: 57.9681
- Rouge2: 23.5702
- Rougel: 42.298
- Rougelsum: 54.4306
- Bertscore Precision: 83.6132
- Bertscore Recall: 84.9752
- Bertscore F1: 84.2861
- Bleu: 0.1834
- Gen Len: 234.8649

## 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  |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 5.8822        | 0.0826 | 100  | 5.6709          | 48.7438 | 17.0806 | 33.6533 | 45.9891   | 80.2777             | 82.0236          | 81.1384      | 0.1276 | 234.8898 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1