| --- |
| license: mit |
| base_model: microsoft/deberta-v3-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: Classifier_with_external_sets_04 |
| 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. --> |
|
|
| # Classifier_with_external_sets_04 |
|
|
| This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2741 |
| - Accuracy: 0.9193 |
|
|
| ## 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: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 26 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-------:|:----:|:---------------:|:--------:| |
| | No log | 0.9983 | 289 | 0.3540 | 0.8893 | |
| | 0.5256 | 2.0 | 579 | 0.3874 | 0.8673 | |
| | 0.5256 | 2.9983 | 868 | 0.3275 | 0.8991 | |
| | 0.378 | 4.0 | 1158 | 0.3244 | 0.9028 | |
| | 0.378 | 4.9983 | 1447 | 0.4013 | 0.8312 | |
| | 0.4029 | 6.0 | 1737 | 0.4052 | 0.8428 | |
| | 0.3932 | 6.9983 | 2026 | 0.3667 | 0.8801 | |
| | 0.3932 | 8.0 | 2316 | 0.3972 | 0.8385 | |
| | 0.3972 | 8.9983 | 2605 | 0.3983 | 0.8648 | |
| | 0.3972 | 10.0 | 2895 | 0.3805 | 0.8587 | |
| | 0.3734 | 10.9983 | 3184 | 0.3735 | 0.8746 | |
| | 0.3734 | 12.0 | 3474 | 0.3256 | 0.8893 | |
| | 0.3752 | 12.9983 | 3763 | 0.2800 | 0.9101 | |
| | 0.3169 | 14.0 | 4053 | 0.3071 | 0.8979 | |
| | 0.3169 | 14.9983 | 4342 | 0.3083 | 0.9052 | |
| | 0.312 | 16.0 | 4632 | 0.2894 | 0.9168 | |
| | 0.312 | 16.9983 | 4921 | 0.3725 | 0.8624 | |
| | 0.3162 | 18.0 | 5211 | 0.3163 | 0.8979 | |
| | 0.3185 | 18.9983 | 5500 | 0.3030 | 0.8991 | |
| | 0.3185 | 20.0 | 5790 | 0.3045 | 0.8997 | |
| | 0.2951 | 20.9983 | 6079 | 0.2944 | 0.9076 | |
| | 0.2951 | 22.0 | 6369 | 0.2693 | 0.9199 | |
| | 0.2916 | 22.9983 | 6658 | 0.2711 | 0.9187 | |
| | 0.2916 | 24.0 | 6948 | 0.2651 | 0.9211 | |
| | 0.2593 | 24.9983 | 7237 | 0.2696 | 0.9193 | |
| | 0.2646 | 25.9551 | 7514 | 0.2741 | 0.9193 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.40.0 |
| - Pytorch 2.2.2+cu121 |
| - Datasets 2.19.0 |
| - Tokenizers 0.19.1 |
| |
