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---
base_model: google/pegasus-large
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: PhysicalSciencePegasusLargeModel
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. -->
# PhysicalSciencePegasusLargeModel
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.4857
- Rouge1: 45.4096
- Rouge2: 14.1811
- Rougel: 31.2204
- Rougelsum: 40.9948
- Bertscore Precision: 78.8464
- Bertscore Recall: 82.0345
- Bertscore F1: 80.4009
- Bleu: 0.0934
- Gen Len: 192.7149
## 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 |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 6.8224 | 0.0620 | 100 | 6.4676 | 35.0969 | 8.6135 | 23.5193 | 31.2379 | 75.6321 | 79.8224 | 77.6611 | 0.0532 | 192.7149 |
| 6.3975 | 0.1239 | 200 | 6.1277 | 38.4317 | 11.0631 | 27.2821 | 34.6261 | 76.5444 | 80.6289 | 78.5221 | 0.0704 | 192.7149 |
| 6.1059 | 0.1859 | 300 | 6.0085 | 39.425 | 11.6567 | 27.8193 | 35.5195 | 76.8342 | 80.9269 | 78.8164 | 0.0749 | 192.7149 |
| 6.1215 | 0.2478 | 400 | 5.9209 | 40.7923 | 12.1231 | 28.4792 | 36.7207 | 77.1433 | 81.1253 | 79.0738 | 0.0786 | 192.7149 |
| 6.0244 | 0.3098 | 500 | 5.8054 | 41.3668 | 12.6049 | 28.8963 | 37.3873 | 77.1495 | 81.267 | 79.1445 | 0.0830 | 192.7149 |
| 5.9814 | 0.3717 | 600 | 5.7414 | 43.1237 | 13.2946 | 29.7215 | 38.645 | 77.6801 | 81.5336 | 79.5507 | 0.0875 | 192.7149 |
| 5.9421 | 0.4337 | 700 | 5.6719 | 43.8248 | 13.4876 | 30.0523 | 39.3496 | 77.8832 | 81.6572 | 79.7164 | 0.0887 | 192.7149 |
| 5.7744 | 0.4957 | 800 | 5.6277 | 44.5131 | 13.6831 | 30.4494 | 39.9332 | 78.1354 | 81.7356 | 79.8859 | 0.0896 | 192.7149 |
| 5.7227 | 0.5576 | 900 | 5.5916 | 44.8391 | 13.8057 | 30.6822 | 40.4155 | 78.4064 | 81.8258 | 80.0711 | 0.0902 | 192.7149 |
| 5.7532 | 0.6196 | 1000 | 5.5600 | 44.5759 | 13.8594 | 30.7325 | 40.1139 | 78.3708 | 81.8401 | 80.0593 | 0.0909 | 192.7149 |
| 5.7156 | 0.6815 | 1100 | 5.5421 | 45.2882 | 13.9686 | 31.0389 | 40.7447 | 78.6902 | 81.9492 | 80.2784 | 0.0916 | 192.7149 |
| 5.6819 | 0.7435 | 1200 | 5.5229 | 45.3689 | 14.1424 | 31.0474 | 40.781 | 78.6599 | 81.9628 | 80.269 | 0.0925 | 192.7149 |
| 5.7394 | 0.8055 | 1300 | 5.5060 | 45.4608 | 14.1555 | 31.1182 | 40.9486 | 78.7697 | 82.0031 | 80.3458 | 0.0930 | 192.7149 |
| 5.7374 | 0.8674 | 1400 | 5.4912 | 44.9845 | 14.0026 | 31.0462 | 40.6411 | 78.6797 | 81.9501 | 80.2733 | 0.0919 | 192.7149 |
| 5.6422 | 0.9294 | 1500 | 5.4874 | 45.5215 | 14.1925 | 31.2402 | 41.0589 | 78.8731 | 82.0393 | 80.4171 | 0.0934 | 192.7149 |
| 5.6744 | 0.9913 | 1600 | 5.4857 | 45.4096 | 14.1811 | 31.2204 | 40.9948 | 78.8464 | 82.0345 | 80.4009 | 0.0934 | 192.7149 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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