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---
base_model: google/pegasus-large
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: Physical_Principal_PegasusLargeModel
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. -->
# Physical_Principal_PegasusLargeModel
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: 5.5512
- Rouge1: 42.7556
- Rouge2: 11.8875
- Rougel: 28.895
- Rougelsum: 39.3949
- Bertscore Precision: 78.8849
- Bertscore Recall: 81.4458
- Bertscore F1: 80.1383
- Bleu: 0.0723
- Gen Len: 195.6218
## 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 |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 6.8945 | 0.0620 | 100 | 6.5742 | 31.5625 | 6.7644 | 21.3466 | 29.564 | 75.9351 | 79.1872 | 77.5195 | 0.0377 | 195.6218 |
| 6.3686 | 0.1239 | 200 | 6.2216 | 36.3235 | 9.5662 | 25.3803 | 33.4451 | 76.783 | 80.08 | 78.3873 | 0.0551 | 195.6218 |
| 6.1776 | 0.1859 | 300 | 6.0881 | 38.1424 | 10.5393 | 26.1916 | 35.0327 | 77.3676 | 80.4951 | 78.892 | 0.0625 | 195.6218 |
| 6.1663 | 0.2478 | 400 | 5.9817 | 39.7408 | 10.7982 | 26.9356 | 36.7781 | 77.9473 | 80.6841 | 79.2852 | 0.0636 | 195.6218 |
| 6.0978 | 0.3098 | 500 | 5.8917 | 39.3921 | 10.6543 | 26.9539 | 36.4703 | 77.9172 | 80.7242 | 79.289 | 0.0631 | 195.6218 |
| 5.9824 | 0.3717 | 600 | 5.8200 | 42.4464 | 11.3324 | 27.8739 | 39.2401 | 78.5855 | 81.0563 | 79.7957 | 0.0669 | 195.6218 |
| 5.9387 | 0.4337 | 700 | 5.7582 | 41.98 | 11.3435 | 28.0672 | 38.7014 | 78.4184 | 81.1429 | 79.7509 | 0.0688 | 195.6218 |
| 5.8692 | 0.4957 | 800 | 5.7002 | 41.8091 | 11.4629 | 28.0863 | 38.4041 | 78.2676 | 81.1789 | 79.6896 | 0.0691 | 195.6218 |
| 5.8287 | 0.5576 | 900 | 5.6638 | 42.0986 | 11.4605 | 28.3743 | 38.792 | 78.3915 | 81.233 | 79.7799 | 0.0691 | 195.6218 |
| 5.8113 | 0.6196 | 1000 | 5.6285 | 41.7907 | 11.4896 | 28.4144 | 38.688 | 78.6019 | 81.2325 | 79.889 | 0.0691 | 195.6218 |
| 5.788 | 0.6815 | 1100 | 5.6124 | 42.7557 | 11.8057 | 28.7632 | 39.4786 | 78.7952 | 81.3582 | 80.0499 | 0.0709 | 195.6218 |
| 5.7594 | 0.7435 | 1200 | 5.5892 | 42.8952 | 11.8442 | 28.8255 | 39.5519 | 78.7779 | 81.3894 | 80.0556 | 0.0716 | 195.6218 |
| 5.7829 | 0.8055 | 1300 | 5.5713 | 42.9309 | 11.8742 | 28.8596 | 39.5816 | 78.8398 | 81.4053 | 80.0955 | 0.0719 | 195.6218 |
| 5.7359 | 0.8674 | 1400 | 5.5603 | 42.5415 | 11.7179 | 28.8073 | 39.2871 | 78.8019 | 81.37 | 80.0586 | 0.0710 | 195.6218 |
| 5.7216 | 0.9294 | 1500 | 5.5546 | 43.1987 | 12.0116 | 29.0028 | 39.8203 | 78.9462 | 81.474 | 80.1838 | 0.0728 | 195.6218 |
| 5.6968 | 0.9913 | 1600 | 5.5512 | 42.7556 | 11.8875 | 28.895 | 39.3949 | 78.8849 | 81.4458 | 80.1383 | 0.0723 | 195.6218 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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