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

# PhysicalScienceBARTPrincipal

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: 4.5862
- Rouge1: 49.7214
- Rouge2: 15.9205
- Rougel: 34.8099
- Rougelsum: 45.9442
- Bertscore Precision: 81.8626
- Bertscore Recall: 83.3072
- Bertscore F1: 82.5744
- Bleu: 0.1065
- Gen Len: 196.3779

## 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  |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 6.4881        | 0.0620 | 100  | 6.2790          | 38.9402 | 10.9737 | 28.0473 | 36.4124   | 78.6712             | 80.7927          | 79.7123      | 0.0702 | 196.3779 |
| 5.9838        | 0.1239 | 200  | 5.8574          | 39.6094 | 11.61   | 28.6653 | 36.6426   | 78.5563             | 81.2374          | 79.8672      | 0.0773 | 196.3779 |
| 5.5757        | 0.1859 | 300  | 5.5425          | 43.235  | 12.5595 | 30.3069 | 40.1431   | 79.7016             | 81.7103          | 80.6878      | 0.0826 | 196.3779 |
| 5.4752        | 0.2478 | 400  | 5.3518          | 45.0647 | 13.1878 | 31.0925 | 41.4826   | 79.7122             | 82.0455          | 80.8554      | 0.0880 | 196.3779 |
| 5.3711        | 0.3098 | 500  | 5.2193          | 47.1793 | 13.5223 | 31.7989 | 43.5774   | 80.6424             | 82.3476          | 81.4813      | 0.0892 | 196.3779 |
| 5.1653        | 0.3717 | 600  | 5.0858          | 45.2081 | 13.4909 | 31.8919 | 41.7813   | 80.7104             | 82.4561          | 81.5689      | 0.0897 | 196.3779 |
| 5.0684        | 0.4337 | 700  | 4.9837          | 46.4035 | 14.2034 | 32.654  | 42.8883   | 80.4628             | 82.4529          | 81.4399      | 0.0941 | 196.3779 |
| 4.9625        | 0.4957 | 800  | 4.9084          | 48.2088 | 14.8904 | 33.2025 | 44.5397   | 81.1668             | 82.8469          | 81.9935      | 0.0986 | 196.3779 |
| 4.8858        | 0.5576 | 900  | 4.8370          | 48.5919 | 14.7721 | 33.5041 | 44.7923   | 81.2656             | 82.8635          | 82.0522      | 0.0974 | 196.3779 |
| 4.8251        | 0.6196 | 1000 | 4.7813          | 49.2512 | 15.4584 | 34.0164 | 45.5215   | 81.4958             | 83.0067          | 82.2398      | 0.1030 | 196.3779 |
| 4.8581        | 0.6815 | 1100 | 4.7307          | 48.7203 | 15.379  | 34.0451 | 45.0395   | 81.7154             | 83.106           | 82.4008      | 0.1027 | 196.3779 |
| 4.7934        | 0.7435 | 1200 | 4.6861          | 49.5987 | 15.6207 | 34.3261 | 45.8512   | 81.7656             | 83.1546          | 82.4502      | 0.1042 | 196.3779 |
| 4.7163        | 0.8055 | 1300 | 4.6518          | 48.9818 | 15.5333 | 34.3788 | 45.3444   | 81.6763             | 83.1451          | 82.3998      | 0.1039 | 196.3779 |
| 4.6855        | 0.8674 | 1400 | 4.6199          | 49.1462 | 15.5914 | 34.5149 | 45.5788   | 81.7027             | 83.1199          | 82.401       | 0.1037 | 196.3779 |
| 4.615         | 0.9294 | 1500 | 4.5987          | 49.6903 | 15.8973 | 34.7628 | 45.9111   | 81.8545             | 83.302           | 82.5678      | 0.1064 | 196.3779 |
| 4.5964        | 0.9913 | 1600 | 4.5862          | 49.7214 | 15.9205 | 34.8099 | 45.9442   | 81.8626             | 83.3072          | 82.5744      | 0.1065 | 196.3779 |


### Framework versions

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