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

# LifeScienceBARTPrincipal

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: 4.6738
- Rouge1: 49.5951
- Rouge2: 14.7925
- Rougel: 33.5728
- Rougelsum: 46.0607
- Bertscore Precision: 81.3188
- Bertscore Recall: 83.0508
- Bertscore F1: 82.1725
- Bleu: 0.1002
- Gen Len: 227.8658

## 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  |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |


### Framework versions

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