| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/deberta-v3-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: deberta_mcq |
| 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_mcq |
| |
| This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0868 |
| - Accuracy: 0.9962 |
| - F1 Macro: 0.9960 |
| - Map At 3: 0.9981 |
| |
| ## 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: 16 |
| - 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 |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 6 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Map At 3 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:--------:| |
| | 1.6085 | 0.9840 | 46 | 1.5929 | 0.6061 | 0.5985 | 0.7506 | |
| | 1.3478 | 1.9893 | 93 | 0.7228 | 0.9242 | 0.9211 | 0.9558 | |
| | 0.4559 | 2.9947 | 140 | 0.2588 | 0.9848 | 0.9839 | 0.9924 | |
| | 0.3132 | 4.0 | 187 | 0.1472 | 0.9924 | 0.9920 | 0.9962 | |
| | 0.2326 | 4.9840 | 233 | 0.0957 | 0.9962 | 0.9960 | 0.9981 | |
| | 0.201 | 5.9037 | 276 | 0.0868 | 0.9962 | 0.9960 | 0.9981 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.44.2 |
| - Pytorch 2.13.0+cu130 |
| - Datasets 4.8.5 |
| - Tokenizers 0.19.1 |
| |
