| | --- |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: roberta-mqa-rat |
| | 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. --> |
| |
|
| | # roberta-mqa-rat |
| |
|
| | This model was trained from scratch on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.1161 |
| | - Accuracy: 0.5512 |
| | - F1: 0.5492 |
| | - Precision: 0.5522 |
| | - Recall: 0.5478 |
| |
|
| | ## 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: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 3 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | 1.4516 | 0.3233 | 1200 | 1.4043 | 0.4042 | 0.4014 | 0.4111 | 0.4008 | |
| | | 1.3834 | 0.6466 | 2400 | 1.3420 | 0.4434 | 0.4417 | 0.4447 | 0.4418 | |
| | | 1.3342 | 0.9698 | 3600 | 1.3308 | 0.4513 | 0.4489 | 0.4540 | 0.4470 | |
| | | 1.263 | 1.2931 | 4800 | 1.2413 | 0.4907 | 0.4897 | 0.4941 | 0.4881 | |
| | | 1.2209 | 1.6164 | 6000 | 1.2098 | 0.5095 | 0.5079 | 0.5134 | 0.5059 | |
| | | 1.1856 | 1.9397 | 7200 | 1.1804 | 0.5174 | 0.5159 | 0.5200 | 0.5139 | |
| | | 1.1134 | 2.2629 | 8400 | 1.1527 | 0.5337 | 0.5316 | 0.5373 | 0.5294 | |
| | | 1.0924 | 2.5862 | 9600 | 1.1307 | 0.5456 | 0.5440 | 0.5475 | 0.5425 | |
| | | 1.0556 | 2.9095 | 10800 | 1.1161 | 0.5512 | 0.5492 | 0.5522 | 0.5478 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.40.2 |
| | - Pytorch 2.3.0+cu121 |
| | - Datasets 2.19.1 |
| | - Tokenizers 0.19.1 |
| |
|
