---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: finetune-newwiki-summarization-ver1
  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-ver1

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: 0.4720
- Rouge1: 48.6293
- Rouge2: 25.6053
- Rougel: 35.2967
- Rougelsum: 37.4842

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.7106        | 1.0   | 1980  | 0.5006          | 46.5921 | 22.8276 | 33.1994 | 35.6330   |
| 0.621         | 2.0   | 3960  | 0.4774          | 47.4426 | 24.1508 | 34.1315 | 36.5692   |
| 0.5607        | 3.0   | 5940  | 0.4690          | 48.1503 | 24.7217 | 34.5071 | 36.7568   |
| 0.5241        | 4.0   | 7920  | 0.4673          | 48.2480 | 25.0604 | 34.4937 | 36.9301   |
| 0.499         | 5.0   | 9900  | 0.4678          | 48.1659 | 25.1857 | 34.9460 | 37.1931   |
| 0.4592        | 6.0   | 11880 | 0.4694          | 48.5839 | 25.5925 | 35.2301 | 37.5352   |
| 0.4535        | 7.0   | 13860 | 0.4720          | 48.6293 | 25.6053 | 35.2967 | 37.4842   |


### Framework versions

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