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

# PhysicalScienceBART

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: 4.2991
- Rouge1: 53.186
- Rouge2: 19.5939
- Rougel: 38.452
- Rougelsum: 49.3854
- Bertscore Precision: 82.8832
- Bertscore Recall: 84.3034
- Bertscore F1: 83.5838
- Bleu: 0.1422
- Gen Len: 196.4045

## 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  |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 6.1797        | 0.0620 | 100  | 5.9428          | 45.7148 | 14.7784 | 32.4391 | 42.4853   | 79.9576             | 82.0698          | 80.9951      | 0.1054 | 196.4045 |
| 5.7661        | 0.1239 | 200  | 5.5214          | 44.5312 | 15.1622 | 32.5105 | 41.2065   | 79.981              | 82.4             | 81.1665      | 0.1088 | 196.4045 |
| 5.2648        | 0.1859 | 300  | 5.2101          | 45.5969 | 15.6417 | 33.588  | 42.4324   | 80.1261             | 82.5551          | 81.3158      | 0.1119 | 196.4045 |
| 5.2069        | 0.2478 | 400  | 5.0522          | 49.0072 | 16.6961 | 34.477  | 45.2146   | 80.7244             | 83.1085          | 81.8929      | 0.1201 | 196.4045 |
| 4.9897        | 0.3098 | 500  | 4.9185          | 48.8492 | 16.7109 | 35.2037 | 45.3551   | 81.2776             | 83.2272          | 82.236       | 0.1207 | 196.4045 |
| 4.8413        | 0.3717 | 600  | 4.8053          | 48.7091 | 16.9882 | 35.3917 | 45.1652   | 81.4957             | 83.4015          | 82.4325      | 0.1226 | 196.4045 |
| 4.829         | 0.4337 | 700  | 4.6973          | 50.548  | 17.8895 | 36.2198 | 47.0729   | 81.8959             | 83.5895          | 82.73        | 0.1278 | 196.4045 |
| 4.6419        | 0.4957 | 800  | 4.6161          | 50.6164 | 18.2248 | 36.6206 | 46.8571   | 81.8709             | 83.7859          | 82.8123      | 0.1313 | 196.4045 |
| 4.5451        | 0.5576 | 900  | 4.5494          | 51.9353 | 18.3864 | 37.1004 | 48.168    | 82.3316             | 83.9031          | 83.1059      | 0.1326 | 196.4045 |
| 4.4911        | 0.6196 | 1000 | 4.4939          | 51.8997 | 18.8308 | 37.4581 | 47.9484   | 82.3519             | 83.9865          | 83.1569      | 0.1361 | 196.4045 |
| 4.5189        | 0.6815 | 1100 | 4.4391          | 52.0976 | 19.0604 | 37.6501 | 48.4561   | 82.5212             | 84.0028          | 83.2516      | 0.1365 | 196.4045 |
| 4.4382        | 0.7435 | 1200 | 4.4061          | 53.1857 | 19.3566 | 37.9447 | 49.2686   | 82.7376             | 84.2523          | 83.4844      | 0.1401 | 196.4045 |
| 4.4027        | 0.8055 | 1300 | 4.3583          | 52.2536 | 19.1902 | 37.9482 | 48.549    | 82.6541             | 84.1335          | 83.3833      | 0.1388 | 196.4045 |
| 4.3911        | 0.8674 | 1400 | 4.3376          | 52.243  | 19.139  | 38.0374 | 48.6274   | 82.6627             | 84.1627          | 83.4023      | 0.1385 | 196.4045 |
| 4.29          | 0.9294 | 1500 | 4.3162          | 53.3823 | 19.4988 | 38.381  | 49.4473   | 82.9505             | 84.3617          | 83.6468      | 0.1419 | 196.4045 |
| 4.3218        | 0.9913 | 1600 | 4.2991          | 53.186  | 19.5939 | 38.452  | 49.3854   | 82.8832             | 84.3034          | 83.5838      | 0.1422 | 196.4045 |


### Framework versions

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