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---
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
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