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---
library_name: transformers
base_model: Fsoft-AIC/videberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videberta-base_v1
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. -->
# videberta-base_v1
This model is a fine-tuned version of [Fsoft-AIC/videberta-base](https://huggingface.co/Fsoft-AIC/videberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4110
- Accuracy: 0.8882
- Precision Macro: 0.7636
- Recall Macro: 0.7197
- F1 Macro: 0.7363
- F1 Weighted: 0.8843
## 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: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Recall Macro | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:|
| 0.8764 | 1.0 | 90 | 0.7142 | 0.6974 | 0.4684 | 0.4901 | 0.4759 | 0.6809 |
| 0.682 | 2.0 | 180 | 0.5610 | 0.7701 | 0.5261 | 0.5431 | 0.5253 | 0.7514 |
| 0.5221 | 3.0 | 270 | 0.4966 | 0.8294 | 0.5546 | 0.5817 | 0.5660 | 0.8102 |
| 0.429 | 4.0 | 360 | 0.4697 | 0.8395 | 0.6756 | 0.5881 | 0.5807 | 0.8204 |
| 0.3652 | 5.0 | 450 | 0.4085 | 0.8642 | 0.7889 | 0.6334 | 0.6442 | 0.8503 |
| 0.3638 | 6.0 | 540 | 0.4011 | 0.8743 | 0.8328 | 0.6359 | 0.6447 | 0.8591 |
| 0.3148 | 7.0 | 630 | 0.3770 | 0.8806 | 0.8160 | 0.6770 | 0.7037 | 0.8712 |
| 0.2928 | 8.0 | 720 | 0.3874 | 0.8825 | 0.8480 | 0.6751 | 0.7020 | 0.8724 |
| 0.2705 | 9.0 | 810 | 0.3800 | 0.8793 | 0.7808 | 0.7026 | 0.7254 | 0.8737 |
| 0.2397 | 10.0 | 900 | 0.3699 | 0.8882 | 0.8000 | 0.6991 | 0.7257 | 0.8810 |
| 0.2325 | 11.0 | 990 | 0.3837 | 0.8863 | 0.8213 | 0.6647 | 0.6855 | 0.8745 |
| 0.2158 | 12.0 | 1080 | 0.3721 | 0.8857 | 0.7843 | 0.7061 | 0.7296 | 0.8798 |
| 0.1985 | 13.0 | 1170 | 0.3878 | 0.8907 | 0.8037 | 0.7090 | 0.7362 | 0.8844 |
| 0.2035 | 14.0 | 1260 | 0.3784 | 0.8857 | 0.7685 | 0.7173 | 0.7363 | 0.8815 |
| 0.1805 | 15.0 | 1350 | 0.4019 | 0.8850 | 0.7565 | 0.7005 | 0.7193 | 0.8795 |
| 0.1808 | 16.0 | 1440 | 0.4085 | 0.8882 | 0.7732 | 0.7114 | 0.7322 | 0.8831 |
| 0.1646 | 17.0 | 1530 | 0.3906 | 0.8831 | 0.7496 | 0.7368 | 0.7427 | 0.8819 |
| 0.1687 | 18.0 | 1620 | 0.3998 | 0.8857 | 0.7606 | 0.7306 | 0.7431 | 0.8831 |
| 0.1636 | 19.0 | 1710 | 0.4107 | 0.8863 | 0.7594 | 0.7184 | 0.7341 | 0.8826 |
| 0.1634 | 20.0 | 1800 | 0.4110 | 0.8882 | 0.7636 | 0.7197 | 0.7363 | 0.8843 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
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