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---
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
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