| --- |
| 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 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.35.0 |
| - Pytorch 2.0.0 |
| - Datasets 2.1.0 |
| - Tokenizers 0.14.1 |
|
|
