---
base_model: google/pegasus-large
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: ALLPrincipalPegasusLargeModel
  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. -->

# ALLPrincipalPegasusLargeModel

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: 5.0232
- Rouge1: 47.6658
- Rouge2: 14.3424
- Rougel: 32.0036
- Rougelsum: 44.3129
- Bertscore Precision: 80.0039
- Bertscore Recall: 82.2832
- Bertscore F1: 81.1232
- Bleu: 0.0968
- Gen Len: 215.5775

## 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  |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 6.0475        | 0.1059 | 500  | 5.8195          | 40.9165 | 11.2781 | 27.4543 | 38.2464   | 77.6812             | 80.818           | 79.2126      | 0.0750 | 215.5775 |
| 5.7557        | 0.2118 | 1000 | 5.5171          | 44.1505 | 12.4929 | 29.115  | 41.1231   | 78.4631             | 81.3235          | 79.8626      | 0.0826 | 215.5775 |
| 5.5723        | 0.3178 | 1500 | 5.3613          | 45.368  | 13.0491 | 30.0119 | 42.2834   | 79.0906             | 81.6243          | 80.3329      | 0.0866 | 215.5775 |
| 5.5134        | 0.4237 | 2000 | 5.2571          | 45.8933 | 13.254  | 30.5132 | 42.7279   | 79.3615             | 81.82            | 80.5674      | 0.0886 | 215.5775 |
| 5.3793        | 0.5296 | 2500 | 5.1767          | 46.8804 | 13.7974 | 31.0723 | 43.5411   | 79.5774             | 82.0228          | 80.7769      | 0.0926 | 215.5775 |
| 5.4004        | 0.6355 | 3000 | 5.1101          | 46.7592 | 13.9082 | 31.3732 | 43.5105   | 79.7564             | 82.1032          | 80.9084      | 0.0934 | 215.5775 |
| 5.2125        | 0.7414 | 3500 | 5.0666          | 47.6221 | 14.2392 | 31.788  | 44.2768   | 79.9394             | 82.2162          | 81.0575      | 0.0954 | 215.5775 |
| 5.2604        | 0.8473 | 4000 | 5.0372          | 47.5699 | 14.3045 | 31.92   | 44.208    | 80.008              | 82.2777          | 81.1227      | 0.0967 | 215.5775 |
| 5.2959        | 0.9533 | 4500 | 5.0232          | 47.6658 | 14.3424 | 32.0036 | 44.3129   | 80.0039             | 82.2832          | 81.1232      | 0.0968 | 215.5775 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1