| --- |
| library_name: transformers |
| license: mit |
| base_model: roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: checkpoints |
| 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. --> |
|
|
| # Qna classifier |
|
|
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4574 |
| - Accuracy: 0.8526 |
| - F1: 0.8402 |
| - Precision: 0.8566 |
| - Recall: 0.8526 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - 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: 3 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | No log | 0.1485 | 500 | 3.3156 | 0.2530 | 0.1877 | 0.1772 | 0.2530 | |
| | No log | 0.2971 | 1000 | 2.5424 | 0.3081 | 0.2274 | 0.2078 | 0.3081 | |
| | No log | 0.4456 | 1500 | 2.1390 | 0.3414 | 0.2649 | 0.2471 | 0.3414 | |
| | No log | 0.5942 | 2000 | 1.8717 | 0.4001 | 0.3334 | 0.3560 | 0.4001 | |
| | No log | 0.7427 | 2500 | 1.6311 | 0.4522 | 0.3897 | 0.3845 | 0.4522 | |
| | No log | 0.8913 | 3000 | 1.4911 | 0.5069 | 0.4460 | 0.4512 | 0.5069 | |
| | 2.3757 | 1.0398 | 3500 | 1.3221 | 0.5510 | 0.4840 | 0.4818 | 0.5510 | |
| | 2.3757 | 1.1884 | 4000 | 1.1827 | 0.6173 | 0.5630 | 0.5779 | 0.6173 | |
| | 2.3757 | 1.3369 | 4500 | 1.0504 | 0.6518 | 0.6041 | 0.6272 | 0.6518 | |
| | 2.3757 | 1.4854 | 5000 | 0.9556 | 0.6752 | 0.6272 | 0.6453 | 0.6752 | |
| | 2.3757 | 1.6340 | 5500 | 0.8580 | 0.7113 | 0.6642 | 0.6795 | 0.7113 | |
| | 2.3757 | 1.7825 | 6000 | 0.7818 | 0.7364 | 0.6984 | 0.7270 | 0.7364 | |
| | 2.3757 | 1.9311 | 6500 | 0.7009 | 0.7669 | 0.7324 | 0.7616 | 0.7669 | |
| | 1.0183 | 2.0796 | 7000 | 0.6271 | 0.7957 | 0.7685 | 0.7883 | 0.7957 | |
| | 1.0183 | 2.2282 | 7500 | 0.5864 | 0.8051 | 0.7812 | 0.8061 | 0.8051 | |
| | 1.0183 | 2.3767 | 8000 | 0.5439 | 0.8237 | 0.8008 | 0.8104 | 0.8237 | |
| | 1.0183 | 2.5253 | 8500 | 0.5116 | 0.8297 | 0.8118 | 0.8268 | 0.8297 | |
| | 1.0183 | 2.6738 | 9000 | 0.4916 | 0.8394 | 0.8237 | 0.8450 | 0.8394 | |
| | 1.0183 | 2.8223 | 9500 | 0.4696 | 0.8486 | 0.8346 | 0.8514 | 0.8486 | |
| | 1.0183 | 2.9709 | 10000 | 0.4574 | 0.8526 | 0.8402 | 0.8566 | 0.8526 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.53.1 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 2.14.4 |
| - Tokenizers 0.21.2 |
|
|
