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# ComOM-VIDeBERTa-1

This model is a fine-tuned version of [Fsoft-AIC/videberta-base](https://huggingface.co/Fsoft-AIC/videberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3821
- Accuracy: 0.8270

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 126  | 0.5622          | 0.6899   |
| No log        | 2.0   | 252  | 0.4775          | 0.7972   |
| No log        | 3.0   | 378  | 0.4007          | 0.8131   |
| 0.512         | 4.0   | 504  | 0.3964          | 0.8171   |
| 0.512         | 5.0   | 630  | 0.3821          | 0.8270   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "Fsoft-AIC/videberta-base", "model-index": [{"name": "ComOM-VIDeBERTa-1", "results": []}]}
transformers tensorboard safetensors deberta-v2 text-classification generated_from_trainer base_model:Fsoft-AIC/videberta-base autotrain_compatible endpoints_compatible region:us