---
base_model: google/pegasus-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: sumarize_model_pegasus_v1
  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. -->

# sumarize_model_pegasus_v1

This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3379
- Rouge1: 0.6034
- Rouge2: 0.4459
- Rougel: 0.5685
- Rougelsum: 0.5681
- Gen Len: 32.8647

## 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: 3.419313942464226e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 239  | 1.4418          | 0.6747 | 0.5033 | 0.6338 | 0.6335    | 43.9549 |
| No log        | 2.0   | 478  | 1.3434          | 0.6869 | 0.5148 | 0.646  | 0.6459    | 44.938  |
| 1.8531        | 3.0   | 717  | 1.2791          | 0.6843 | 0.5141 | 0.6451 | 0.645     | 44.7556 |
| 1.8531        | 4.0   | 956  | 1.2358          | 0.6868 | 0.5168 | 0.6473 | 0.647     | 44.4305 |
| 1.4419        | 5.0   | 1195 | 1.2654          | 0.6858 | 0.5172 | 0.6467 | 0.6464    | 43.7857 |
| 1.4419        | 6.0   | 1434 | 1.2838          | 0.6686 | 0.4999 | 0.6291 | 0.6288    | 39.9549 |
| 1.4368        | 7.0   | 1673 | 1.3379          | 0.6034 | 0.4459 | 0.5685 | 0.5681    | 32.8647 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2