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
