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library_name: transformers
base_model: Fsoft-AIC/videberta-xsmall
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videberta-xsmall_v2
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-xsmall_v2
This model is a fine-tuned version of [Fsoft-AIC/videberta-xsmall](https://huggingface.co/Fsoft-AIC/videberta-xsmall) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3825
- Accuracy: 0.9021
- Precision Macro: 0.7867
- Recall Macro: 0.7246
- F1 Macro: 0.7460
- F1 Weighted: 0.8970
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-----------:|
| 0.8576 | 1.0 | 90 | 0.5078 | 0.8263 | 0.5549 | 0.5804 | 0.5639 | 0.8071 |
| 0.462 | 2.0 | 180 | 0.4000 | 0.8667 | 0.5773 | 0.6061 | 0.5912 | 0.8464 |
| 0.3895 | 3.0 | 270 | 0.3928 | 0.8711 | 0.5819 | 0.6073 | 0.5939 | 0.8503 |
| 0.334 | 4.0 | 360 | 0.3652 | 0.8793 | 0.5871 | 0.6133 | 0.5996 | 0.8584 |
| 0.2906 | 5.0 | 450 | 0.3542 | 0.8844 | 0.7567 | 0.6261 | 0.6205 | 0.8658 |
| 0.2912 | 6.0 | 540 | 0.3562 | 0.8895 | 0.8269 | 0.6502 | 0.6619 | 0.8747 |
| 0.2564 | 7.0 | 630 | 0.3404 | 0.8932 | 0.7944 | 0.7308 | 0.7537 | 0.8890 |
| 0.2424 | 8.0 | 720 | 0.3381 | 0.8970 | 0.8492 | 0.6758 | 0.6998 | 0.8854 |
| 0.2167 | 9.0 | 810 | 0.3292 | 0.9015 | 0.8294 | 0.7088 | 0.7377 | 0.8938 |
| 0.2052 | 10.0 | 900 | 0.3621 | 0.9021 | 0.8239 | 0.7163 | 0.7459 | 0.8953 |
| 0.1976 | 11.0 | 990 | 0.3453 | 0.9002 | 0.8251 | 0.7113 | 0.7408 | 0.8930 |
| 0.1904 | 12.0 | 1080 | 0.3754 | 0.9015 | 0.8426 | 0.7040 | 0.7345 | 0.8931 |
| 0.176 | 13.0 | 1170 | 0.3586 | 0.9046 | 0.8177 | 0.7101 | 0.7378 | 0.8971 |
| 0.1783 | 14.0 | 1260 | 0.3635 | 0.8958 | 0.7590 | 0.7239 | 0.7379 | 0.8922 |
| 0.1566 | 15.0 | 1350 | 0.4087 | 0.8926 | 0.7601 | 0.7089 | 0.7270 | 0.8874 |
| 0.1509 | 16.0 | 1440 | 0.3878 | 0.9033 | 0.8019 | 0.7172 | 0.7427 | 0.8970 |
| 0.1463 | 17.0 | 1530 | 0.3730 | 0.8989 | 0.7670 | 0.7348 | 0.7481 | 0.8959 |
| 0.1493 | 18.0 | 1620 | 0.3801 | 0.8996 | 0.7687 | 0.7225 | 0.7397 | 0.8952 |
| 0.1465 | 19.0 | 1710 | 0.3995 | 0.8983 | 0.7738 | 0.7175 | 0.7372 | 0.8932 |
| 0.133 | 20.0 | 1800 | 0.3825 | 0.9021 | 0.7867 | 0.7246 | 0.7460 | 0.8970 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
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