| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/deberta-v3-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: deberta-base |
| 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. --> |
|
|
| # deberta-base |
|
|
| This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1665 |
| - Accuracy: 0.9601 |
| - Precision: 0.9599 |
| - Recall: 0.9601 |
| - F1: 0.9594 |
| - Auroc: 0.9928 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 64 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 1 |
| - label_smoothing_factor: 0.03 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auroc | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:| |
| | 0.4866 | 0.0988 | 256 | 0.2931 | 0.8845 | 0.8939 | 0.8845 | 0.8876 | 0.9465 | |
| | 0.2757 | 0.1977 | 512 | 0.3478 | 0.8898 | 0.8984 | 0.8898 | 0.8765 | 0.9544 | |
| | 0.2433 | 0.2965 | 768 | 0.2097 | 0.9404 | 0.9413 | 0.9404 | 0.9408 | 0.9799 | |
| | 0.2332 | 0.3953 | 1024 | 0.3548 | 0.8815 | 0.8907 | 0.8815 | 0.8657 | 0.9690 | |
| | 0.2152 | 0.4942 | 1280 | 0.1942 | 0.9440 | 0.9434 | 0.9440 | 0.9426 | 0.9868 | |
| | 0.1907 | 0.5930 | 1536 | 0.1615 | 0.9649 | 0.9647 | 0.9649 | 0.9647 | 0.9899 | |
| | 0.1865 | 0.6918 | 1792 | 0.1556 | 0.9655 | 0.9654 | 0.9655 | 0.9654 | 0.9922 | |
| | 0.1865 | 0.7907 | 2048 | 0.2322 | 0.9369 | 0.9370 | 0.9369 | 0.9344 | 0.9773 | |
| | 0.168 | 0.8895 | 2304 | 0.1653 | 0.9672 | 0.9670 | 0.9672 | 0.9668 | 0.9937 | |
| | 0.1732 | 0.9883 | 2560 | 0.1467 | 0.9702 | 0.9716 | 0.9702 | 0.9706 | 0.9935 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.45.1 |
| - Pytorch 2.4.0 |
| - Datasets 3.0.1 |
| - Tokenizers 0.20.0 |
| |
