---
base_model: Fsoft-AIC/videberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ComOM-VIDeBERTa-3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ComOM-VIDeBERTa-3

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: 1.0971
- Precision: 0.1319
- Recall: 0.1029
- F1: 0.1156
- Accuracy: 0.6647

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 78   | 1.2306          | 0.0768    | 0.0370 | 0.0499 | 0.6486   |
| No log        | 2.0   | 156  | 1.1902          | 0.0755    | 0.0609 | 0.0674 | 0.6407   |
| No log        | 3.0   | 234  | 1.1627          | 0.0923    | 0.0679 | 0.0783 | 0.6499   |
| No log        | 4.0   | 312  | 1.1489          | 0.1159    | 0.0879 | 0.1000 | 0.6530   |
| No log        | 5.0   | 390  | 1.1219          | 0.0997    | 0.0749 | 0.0856 | 0.6529   |
| No log        | 6.0   | 468  | 1.1130          | 0.1245    | 0.0879 | 0.1030 | 0.6589   |
| 1.0673        | 7.0   | 546  | 1.1095          | 0.1247    | 0.0919 | 0.1058 | 0.6600   |
| 1.0673        | 8.0   | 624  | 1.0971          | 0.1319    | 0.1029 | 0.1156 | 0.6647   |


### Framework versions

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