| --- |
| library_name: transformers |
| license: mit |
| base_model: Yuvrajg2107/deberta-v3-hybrid-detector_12 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: deberta-v3-hybrid-detector_v2_universal |
| 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-v3-hybrid-detector_v2_universal |
|
|
| This model is a fine-tuned version of [Yuvrajg2107/deberta-v3-hybrid-detector_12](https://huggingface.co/Yuvrajg2107/deberta-v3-hybrid-detector_12) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0389 |
| - Accuracy: 0.9631 |
|
|
| ## 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-06 |
| - train_batch_size: 8 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 32 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 1 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | 0.0127 | 0.1455 | 1000 | 0.0275 | 0.9659 | |
| | 0.0083 | 0.2909 | 2000 | 0.0469 | 0.9541 | |
| | 0.0065 | 0.4364 | 3000 | 0.0439 | 0.9544 | |
| | 0.0063 | 0.5818 | 4000 | 0.0207 | 0.9785 | |
| | 0.007 | 0.7273 | 5000 | 0.0342 | 0.9647 | |
| | 0.0046 | 0.8727 | 6000 | 0.0389 | 0.9631 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.57.3 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 4.4.2 |
| - Tokenizers 0.22.1 |
| |
