| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/deberta-v3-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: deberta-misconception-classifier |
| 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-classifier |
|
|
| 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: 0.2595 |
| - Macro F1: 0.6012 |
| - Weighted F1: 0.7862 |
| - Accuracy: 0.7823 |
| - Map@3: 0.8846 |
|
|
| ## 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: 1e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 16 |
| - 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 | Map@3 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:-----------:|:--------:|:------:| |
| | 1.3548 | 0.2422 | 500 | 1.0357 | 0.2067 | 0.4193 | 0.4221 | 0.5941 | |
| | 0.9062 | 0.4845 | 1000 | 0.7145 | 0.3536 | 0.6222 | 0.6183 | 0.7672 | |
| | 0.5924 | 0.7267 | 1500 | 0.4780 | 0.4251 | 0.7250 | 0.7368 | 0.8460 | |
| | 0.4113 | 0.9690 | 2000 | 0.4354 | 0.4210 | 0.7139 | 0.7354 | 0.8430 | |
| | 0.2906 | 1.2112 | 2500 | 0.3885 | 0.4757 | 0.7373 | 0.7559 | 0.8635 | |
| | 0.3248 | 1.4535 | 3000 | 0.3100 | 0.5215 | 0.7591 | 0.7589 | 0.8651 | |
| | 0.264 | 1.6957 | 3500 | 0.3245 | 0.5371 | 0.7838 | 0.7864 | 0.8852 | |
| | 0.3461 | 1.9380 | 4000 | 0.2863 | 0.5582 | 0.8036 | 0.8136 | 0.8988 | |
| | 0.202 | 2.1802 | 4500 | 0.2697 | 0.5758 | 0.8058 | 0.8147 | 0.9013 | |
| | 0.1641 | 2.4225 | 5000 | 0.2837 | 0.6015 | 0.8224 | 0.8245 | 0.9062 | |
| | 0.1642 | 2.6647 | 5500 | 0.2991 | 0.5559 | 0.8113 | 0.8139 | 0.9009 | |
| | 0.1857 | 2.9070 | 6000 | 0.2518 | 0.5931 | 0.8051 | 0.8109 | 0.8995 | |
| | 0.1322 | 3.1492 | 6500 | 0.2595 | 0.6012 | 0.7862 | 0.7823 | 0.8846 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.53.3 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 4.0.0 |
| - Tokenizers 0.21.2 |
| |
