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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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