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---
base_model: google/pegasus-large
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: ALLPegasusLargeModel
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. -->
# ALLPegasusLargeModel
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: 4.9004
- Rouge1: 50.2858
- Rouge2: 17.3371
- Rougel: 34.9711
- Rougelsum: 45.9178
- Bertscore Precision: 80.3248
- Bertscore Recall: 83.1003
- Bertscore F1: 81.6841
- Bleu: 0.1287
- Gen Len: 213.7421
## 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 |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 5.9374 | 0.1059 | 500 | 5.7310 | 43.8081 | 13.7728 | 29.7031 | 39.5834 | 77.3165 | 81.5184 | 79.3534 | 0.1021 | 213.7421 |
| 5.7002 | 0.2118 | 1000 | 5.4375 | 45.7599 | 14.6097 | 31.1826 | 41.4715 | 78.3108 | 81.9134 | 80.0646 | 0.1068 | 213.7421 |
| 5.5932 | 0.3178 | 1500 | 5.2635 | 46.7433 | 15.2361 | 32.2197 | 42.2622 | 78.8125 | 82.2397 | 80.4828 | 0.1115 | 213.7421 |
| 5.4417 | 0.4237 | 2000 | 5.1352 | 48.2636 | 15.9823 | 33.2082 | 43.9438 | 79.4905 | 82.5491 | 80.9852 | 0.1166 | 213.7421 |
| 5.3551 | 0.5296 | 2500 | 5.0519 | 49.16 | 16.5745 | 33.8127 | 44.6595 | 79.7321 | 82.7596 | 81.212 | 0.1213 | 213.7421 |
| 5.2625 | 0.6355 | 3000 | 4.9910 | 49.3156 | 16.7869 | 34.2642 | 45.012 | 80.0332 | 82.9083 | 81.4402 | 0.1240 | 213.7421 |
| 5.2208 | 0.7414 | 3500 | 4.9445 | 50.1565 | 17.1477 | 34.6991 | 45.7009 | 80.2185 | 83.0322 | 81.5962 | 0.1268 | 213.7421 |
| 5.2456 | 0.8473 | 4000 | 4.9126 | 50.0901 | 17.2522 | 34.8768 | 45.7267 | 80.2903 | 83.0784 | 81.6556 | 0.1282 | 213.7421 |
| 5.2835 | 0.9533 | 4500 | 4.9004 | 50.2858 | 17.3371 | 34.9711 | 45.9178 | 80.3248 | 83.1003 | 81.6841 | 0.1287 | 213.7421 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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