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

# PhysicalScienceBARTMainSections

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: 4.2611
- Rouge1: 53.3257
- Rouge2: 19.9372
- Rougel: 38.7516
- Rougelsum: 49.5491
- Bertscore Precision: 82.9683
- Bertscore Recall: 84.3765
- Bertscore F1: 83.6629
- Bleu: 0.1444
- Gen Len: 195.4093

## 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  |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 6.0567        | 0.0622 | 100  | 5.9024          | 44.7542 | 14.8626 | 32.3438 | 41.6627   | 79.6577             | 81.9972          | 80.8049      | 0.1058 | 195.4093 |
| 5.628         | 0.1244 | 200  | 5.5009          | 44.7374 | 15.4406 | 32.7203 | 41.5684   | 79.7952             | 82.4154          | 81.0775      | 0.1106 | 195.4093 |
| 5.3608        | 0.1866 | 300  | 5.2016          | 47.9813 | 16.6932 | 34.1908 | 44.4923   | 80.6116             | 82.8487          | 81.709       | 0.1189 | 195.4093 |
| 5.1172        | 0.2489 | 400  | 5.0247          | 49.6117 | 17.0694 | 35.1947 | 46.0939   | 81.4181             | 83.2142          | 82.3018      | 0.1228 | 195.4093 |
| 5.1058        | 0.3111 | 500  | 4.8769          | 49.7791 | 17.282  | 35.3202 | 45.4459   | 80.9748             | 83.2981          | 82.1135      | 0.1250 | 195.4093 |
| 4.9831        | 0.3733 | 600  | 4.7486          | 49.7885 | 17.5964 | 36.1885 | 46.1291   | 81.8182             | 83.5683          | 82.6792      | 0.1263 | 195.4093 |
| 4.7239        | 0.4355 | 700  | 4.6365          | 49.9977 | 18.0061 | 36.4943 | 46.3477   | 81.7979             | 83.6503          | 82.7089      | 0.1299 | 195.4093 |
| 4.6893        | 0.4977 | 800  | 4.5773          | 51.7141 | 18.7056 | 37.2897 | 48.1051   | 82.4355             | 83.9204          | 83.1676      | 0.1347 | 195.4093 |
| 4.641         | 0.5599 | 900  | 4.5179          | 51.337  | 18.6106 | 37.3188 | 47.6183   | 82.1666             | 83.9203          | 83.0297      | 0.1355 | 195.4093 |
| 4.4518        | 0.6222 | 1000 | 4.4457          | 52.5898 | 18.9865 | 37.7363 | 48.9758   | 82.5619             | 84.028           | 83.2849      | 0.1363 | 195.4093 |
| 4.4246        | 0.6844 | 1100 | 4.4001          | 52.5771 | 19.1928 | 37.92   | 48.9098   | 82.5426             | 84.0673          | 83.2942      | 0.1392 | 195.4093 |
| 4.549         | 0.7466 | 1200 | 4.3539          | 52.3117 | 19.2452 | 38.0721 | 48.7304   | 82.7547             | 84.1096          | 83.4232      | 0.1383 | 195.4093 |
| 4.3528        | 0.8088 | 1300 | 4.3296          | 52.5899 | 19.6953 | 38.3709 | 48.8248   | 82.7757             | 84.2642          | 83.5094      | 0.1424 | 195.4093 |
| 4.3692        | 0.8710 | 1400 | 4.2972          | 53.2821 | 19.763  | 38.4702 | 49.3072   | 82.9332             | 84.4115          | 83.6622      | 0.1434 | 195.4093 |
| 4.2056        | 0.9332 | 1500 | 4.2795          | 53.4962 | 19.9871 | 38.7098 | 49.5905   | 83.0029             | 84.4307          | 83.7072      | 0.1449 | 195.4093 |
| 4.3956        | 0.9955 | 1600 | 4.2611          | 53.3257 | 19.9372 | 38.7516 | 49.5491   | 82.9683             | 84.3765          | 83.6629      | 0.1444 | 195.4093 |


### Framework versions

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