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---
base_model: Fsoft-AIC/videberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ComOM-VIDeBERTa-2
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-2
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: 1.3046
- Accuracy: 0.5357
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 77 | 1.4249 | 0.4708 |
| No log | 2.0 | 154 | 1.4096 | 0.4708 |
| No log | 3.0 | 231 | 1.3871 | 0.4708 |
| No log | 4.0 | 308 | 1.3809 | 0.5032 |
| No log | 5.0 | 385 | 1.3529 | 0.5195 |
| No log | 6.0 | 462 | 1.3257 | 0.5260 |
| 1.4302 | 7.0 | 539 | 1.3101 | 0.5325 |
| 1.4302 | 8.0 | 616 | 1.3046 | 0.5357 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
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