---
license: mit
base_model: VietAI/vit5-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: sentiment_25_12
  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. -->

# sentiment_25_12

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: 0.1668
- F1: 0.5760
- Accuracy: 0.8423

## 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: 80
- eval_batch_size: 80
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 2.0596        | 0.24  | 100  | 0.2039          | 0.5316 | 0.8088   |
| 0.1995        | 0.48  | 200  | 0.1781          | 0.5587 | 0.8332   |
| 0.1887        | 0.72  | 300  | 0.1749          | 0.5600 | 0.8364   |
| 0.1849        | 0.95  | 400  | 0.1703          | 0.5615 | 0.8376   |
| 0.1791        | 1.19  | 500  | 0.1764          | 0.5615 | 0.8339   |
| 0.1775        | 1.43  | 600  | 0.1687          | 0.5682 | 0.8420   |
| 0.1794        | 1.67  | 700  | 0.1659          | 0.5665 | 0.8410   |
| 0.1688        | 1.91  | 800  | 0.1710          | 0.5663 | 0.8414   |
| 0.1766        | 2.15  | 900  | 0.1650          | 0.5665 | 0.8424   |
| 0.1662        | 2.39  | 1000 | 0.1696          | 0.5750 | 0.8420   |
| 0.1711        | 2.63  | 1100 | 0.1668          | 0.5718 | 0.8439   |
| 0.1616        | 2.86  | 1200 | 0.1689          | 0.5680 | 0.8424   |
| 0.166         | 3.1   | 1300 | 0.1667          | 0.5720 | 0.8437   |
| 0.1634        | 3.34  | 1400 | 0.1654          | 0.5717 | 0.8401   |
| 0.158         | 3.58  | 1500 | 0.1692          | 0.5664 | 0.8431   |
| 0.1567        | 3.82  | 1600 | 0.1668          | 0.5727 | 0.8445   |
| 0.165         | 4.06  | 1700 | 0.1661          | 0.5679 | 0.8439   |
| 0.1577        | 4.3   | 1800 | 0.1672          | 0.5696 | 0.8433   |
| 0.1563        | 4.53  | 1900 | 0.1666          | 0.5763 | 0.8435   |
| 0.1554        | 4.77  | 2000 | 0.1667          | 0.5738 | 0.8424   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0