---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: finetune-newwiki-summarization-ver-augmented
  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. -->

# finetune-newwiki-summarization-ver-augmented

This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4282
- Rouge1: 48.7749
- Rouge2: 26.3665
- Rougel: 35.7765
- Rougelsum: 38.0111

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.6784        | 1.0   | 2312  | 0.5136          | 46.7374 | 23.3000 | 33.5379 | 35.8923   |
| 0.6015        | 2.0   | 4624  | 0.4759          | 47.7112 | 24.5817 | 34.4939 | 36.9831   |
| 0.5587        | 3.0   | 6936  | 0.4543          | 48.4891 | 25.6592 | 35.2310 | 37.5477   |
| 0.5128        | 4.0   | 9248  | 0.4405          | 48.7777 | 26.0690 | 35.5187 | 37.7896   |
| 0.4899        | 5.0   | 11560 | 0.4338          | 48.6758 | 26.0670 | 35.5783 | 37.8850   |
| 0.4796        | 6.0   | 13872 | 0.4295          | 48.8914 | 26.5018 | 35.8671 | 38.1289   |
| 0.4671        | 7.0   | 16184 | 0.4282          | 48.7749 | 26.3665 | 35.7765 | 38.0111   |


### Framework versions

- Transformers 4.17.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2