| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/deberta-v3-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: deberta-misconception |
| 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-misconception |
|
|
| 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: 0.1066 |
| - Macro F1: 0.5639 |
| - Weighted F1: 0.7517 |
| - Accuracy: 0.7177 |
|
|
| ## 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: 32 |
| - eval_batch_size: 32 |
| - 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: 10 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Weighted F1 | Accuracy | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:-----------:|:--------:| |
| | 0.4896 | 0.4840 | 500 | 0.4428 | 0.2597 | 0.1155 | 0.2245 | |
| | 0.2755 | 0.9681 | 1000 | 0.2203 | 0.4467 | 0.6258 | 0.5809 | |
| | 0.1658 | 1.4521 | 1500 | 0.1576 | 0.5330 | 0.7263 | 0.6850 | |
| | 0.1688 | 1.9361 | 2000 | 0.1388 | 0.5112 | 0.6329 | 0.5902 | |
| | 0.0482 | 2.4201 | 2500 | 0.1152 | 0.5605 | 0.7041 | 0.6700 | |
| | 0.0269 | 2.9042 | 3000 | 0.1368 | 0.5653 | 0.6868 | 0.6480 | |
| | 0.1069 | 3.3882 | 3500 | 0.1131 | 0.5633 | 0.7404 | 0.7054 | |
| | 0.0304 | 3.8722 | 4000 | 0.1527 | 0.5592 | 0.7287 | 0.6965 | |
| | 0.0577 | 4.3562 | 4500 | 0.1066 | 0.5639 | 0.7517 | 0.7177 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.53.3 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 4.0.0 |
| - Tokenizers 0.21.2 |
|
|
