|
--- |
|
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 |
|
|
