---
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