metadata
base_model: Fsoft-AIC/videberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ComOM-VIDeBERTa-2
results: []
ComOM-VIDeBERTa-2
This model is a fine-tuned version of 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