| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/deberta-v3-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: deberta_rse |
| 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_rse |
| |
| 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: 0.0243 |
| - Accuracy: 0.9961 |
| - F1: 0.9961 |
| |
| ## 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: 16 |
| - seed: 42 |
| - 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: 15 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
| | 0.8808 | 1.0 | 276 | 0.2620 | 0.9237 | 0.9242 | |
| | 0.3108 | 2.0 | 552 | 0.2273 | 0.9471 | 0.9470 | |
| | 0.2543 | 3.0 | 828 | 0.1193 | 0.9700 | 0.9700 | |
| | 0.1788 | 4.0 | 1104 | 0.1284 | 0.9702 | 0.9705 | |
| | 0.1296 | 5.0 | 1380 | 0.0549 | 0.9891 | 0.9891 | |
| | 0.0669 | 6.0 | 1656 | 0.0398 | 0.9927 | 0.9927 | |
| | 0.0658 | 7.0 | 1932 | 0.0299 | 0.9957 | 0.9957 | |
| | 0.0379 | 8.0 | 2208 | 0.0216 | 0.9964 | 0.9964 | |
| | 0.0312 | 9.0 | 2484 | 0.0243 | 0.9961 | 0.9961 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.48.3 |
| - Pytorch 2.5.1+cu124 |
| - Datasets 3.3.1 |
| - Tokenizers 0.21.0 |
| |
