| | --- |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: roberta-mqa-formrat |
| | 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-formrat |
| |
|
| | This model was trained from scratch on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.1135 |
| | - Accuracy: 0.5671 |
| | - F1: 0.5659 |
| | - Precision: 0.5683 |
| | - Recall: 0.5650 |
| |
|
| | ## 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: 4 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 8 |
| | - 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 | |
| | |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | 1.451 | 0.3233 | 1200 | 1.4125 | 0.4105 | 0.4093 | 0.4151 | 0.4107 | |
| | | 1.416 | 0.6466 | 2400 | 1.3482 | 0.4412 | 0.4394 | 0.4438 | 0.4385 | |
| | | 1.3157 | 0.9698 | 3600 | 1.2933 | 0.4788 | 0.4772 | 0.4776 | 0.4773 | |
| | | 1.2616 | 1.2931 | 4800 | 1.2389 | 0.5032 | 0.5022 | 0.5053 | 0.5011 | |
| | | 1.221 | 1.6164 | 6000 | 1.2049 | 0.5053 | 0.5039 | 0.5060 | 0.5029 | |
| | | 1.1556 | 1.9397 | 7200 | 1.1792 | 0.5288 | 0.5276 | 0.5295 | 0.5265 | |
| | | 1.082 | 2.2629 | 8400 | 1.1593 | 0.5451 | 0.5434 | 0.5487 | 0.5415 | |
| | | 1.0692 | 2.5862 | 9600 | 1.1153 | 0.5613 | 0.5606 | 0.5641 | 0.5594 | |
| | | 1.0066 | 2.9095 | 10800 | 1.1135 | 0.5671 | 0.5659 | 0.5683 | 0.5650 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.40.2 |
| | - Pytorch 2.3.0+cu121 |
| | - Datasets 2.19.1 |
| | - Tokenizers 0.19.1 |
| | |
