---
base_model: uitnlp/visobert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: VP_ViSoBERT_syl_ViWikiFC
  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. -->

# VP_ViSoBERT_syl_ViWikiFC

This model is a fine-tuned version of [uitnlp/visobert](https://huggingface.co/uitnlp/visobert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9243
- Accuracy: 0.6364

## 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: 2e-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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1299        | 0.1   | 100  | 1.1182          | 0.3411   |
| 1.0816        | 0.19  | 200  | 1.0678          | 0.3976   |
| 1.0181        | 0.29  | 300  | 1.0163          | 0.4823   |
| 1.0121        | 0.38  | 400  | 0.9956          | 0.5072   |
| 0.9617        | 0.48  | 500  | 0.9718          | 0.5048   |
| 0.9297        | 0.57  | 600  | 0.9665          | 0.5239   |
| 0.9332        | 0.67  | 700  | 0.9252          | 0.5646   |
| 0.9057        | 0.76  | 800  | 0.9667          | 0.5421   |
| 0.8756        | 0.86  | 900  | 0.8884          | 0.5871   |
| 0.879         | 0.96  | 1000 | 0.8907          | 0.5718   |
| 0.8249        | 1.05  | 1100 | 0.8793          | 0.5981   |
| 0.7177        | 1.15  | 1200 | 0.8951          | 0.5957   |
| 0.7145        | 1.24  | 1300 | 0.9523          | 0.6062   |
| 0.7469        | 1.34  | 1400 | 0.9001          | 0.5986   |
| 0.7358        | 1.43  | 1500 | 0.8865          | 0.6081   |
| 0.7112        | 1.53  | 1600 | 0.9099          | 0.6057   |
| 0.7299        | 1.62  | 1700 | 0.8496          | 0.6144   |
| 0.6949        | 1.72  | 1800 | 0.8580          | 0.6124   |
| 0.6988        | 1.81  | 1900 | 0.8840          | 0.6215   |
| 0.6524        | 1.91  | 2000 | 0.8753          | 0.6134   |
| 0.6914        | 2.01  | 2100 | 0.8729          | 0.6330   |
| 0.5427        | 2.1   | 2200 | 0.9494          | 0.6431   |
| 0.5628        | 2.2   | 2300 | 0.9531          | 0.6120   |
| 0.5607        | 2.29  | 2400 | 0.9050          | 0.6340   |
| 0.5396        | 2.39  | 2500 | 0.9149          | 0.6335   |
| 0.5178        | 2.48  | 2600 | 0.9848          | 0.6124   |
| 0.5322        | 2.58  | 2700 | 0.9198          | 0.6330   |
| 0.5406        | 2.67  | 2800 | 0.9206          | 0.6364   |
| 0.5183        | 2.77  | 2900 | 0.9150          | 0.6392   |
| 0.5369        | 2.87  | 3000 | 0.9200          | 0.6340   |
| 0.5105        | 2.96  | 3100 | 0.9243          | 0.6364   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2