---
base_model: tringuyen-uit/VP_ViSoBERT_syl_ViWikiFC
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 [tringuyen-uit/VP_ViSoBERT_syl_ViWikiFC](https://huggingface.co/tringuyen-uit/VP_ViSoBERT_syl_ViWikiFC) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1555
- Accuracy: 0.6445

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6994        | 0.05  | 100  | 0.9688          | 0.6158   |
| 0.6904        | 0.1   | 200  | 0.9753          | 0.6014   |
| 0.7969        | 0.14  | 300  | 0.9446          | 0.5871   |
| 0.6801        | 0.19  | 400  | 0.9912          | 0.6057   |
| 0.7089        | 0.24  | 500  | 0.9617          | 0.5861   |
| 0.6627        | 0.29  | 600  | 1.0585          | 0.5689   |
| 0.6792        | 0.33  | 700  | 1.0064          | 0.6230   |
| 0.6702        | 0.38  | 800  | 1.0593          | 0.5818   |
| 0.6252        | 0.43  | 900  | 0.9621          | 0.5967   |
| 0.6262        | 0.48  | 1000 | 1.0152          | 0.5957   |
| 0.6515        | 0.53  | 1100 | 0.9539          | 0.6225   |
| 0.6596        | 0.57  | 1200 | 0.9188          | 0.6067   |
| 0.6458        | 0.62  | 1300 | 0.9318          | 0.6201   |
| 0.6087        | 0.67  | 1400 | 0.9532          | 0.6172   |
| 0.6282        | 0.72  | 1500 | 1.0107          | 0.6244   |
| 0.6266        | 0.76  | 1600 | 1.0199          | 0.6096   |
| 0.6165        | 0.81  | 1700 | 1.0973          | 0.6096   |
| 0.5869        | 0.86  | 1800 | 0.9177          | 0.6325   |
| 0.596         | 0.91  | 1900 | 0.8821          | 0.6364   |
| 0.6073        | 0.96  | 2000 | 0.9350          | 0.6306   |
| 0.5921        | 1.0   | 2100 | 0.9606          | 0.6282   |
| 0.4551        | 1.05  | 2200 | 1.0386          | 0.6373   |
| 0.3922        | 1.1   | 2300 | 1.1936          | 0.6368   |
| 0.39          | 1.15  | 2400 | 1.1922          | 0.6316   |
| 0.442         | 1.19  | 2500 | 1.1599          | 0.6220   |
| 0.4092        | 1.24  | 2600 | 1.3106          | 0.6196   |
| 0.4582        | 1.29  | 2700 | 1.1817          | 0.6316   |
| 0.4356        | 1.34  | 2800 | 1.1257          | 0.6316   |
| 0.4145        | 1.39  | 2900 | 1.1899          | 0.6354   |
| 0.4379        | 1.43  | 3000 | 1.1385          | 0.6388   |
| 0.4222        | 1.48  | 3100 | 1.1844          | 0.6249   |
| 0.3758        | 1.53  | 3200 | 1.2444          | 0.6311   |
| 0.4114        | 1.58  | 3300 | 1.1908          | 0.6349   |
| 0.4449        | 1.62  | 3400 | 1.1483          | 0.6273   |
| 0.4046        | 1.67  | 3500 | 1.1977          | 0.6306   |
| 0.4274        | 1.72  | 3600 | 1.1520          | 0.6450   |
| 0.3785        | 1.77  | 3700 | 1.1665          | 0.6330   |
| 0.3854        | 1.82  | 3800 | 1.1680          | 0.6474   |
| 0.3562        | 1.86  | 3900 | 1.1616          | 0.6459   |
| 0.3938        | 1.91  | 4000 | 1.1823          | 0.6397   |
| 0.5083        | 1.96  | 4100 | 1.1555          | 0.6445   |


### Framework versions

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