| --- |
| library_name: transformers |
| license: mit |
| base_model: roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| - accuracy |
| model-index: |
| - name: test_model |
| 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. --> |
|
|
| # test_model |
| |
| 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: 0.1449 |
| - F1: 0.0 |
| - Roc Auc: 0.5 |
| - Accuracy: 0.8976 |
| |
| ## 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: 0.001 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - 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: 20 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:---:|:-------:|:--------:| |
| | 0.1509 | 1.0 | 3491 | 0.1449 | 0.0 | 0.5 | 0.8976 | |
| | 0.1472 | 2.0 | 6982 | 0.1478 | 0.0 | 0.5 | 0.8976 | |
| | 0.1454 | 3.0 | 10473 | 0.1532 | 0.0 | 0.5 | 0.8976 | |
| | 0.144 | 4.0 | 13964 | 0.1457 | 0.0 | 0.5 | 0.8976 | |
| | 0.1463 | 5.0 | 17455 | 0.1441 | 0.0 | 0.5 | 0.8976 | |
| | 0.1427 | 6.0 | 20946 | 0.1463 | 0.0 | 0.5 | 0.8976 | |
| | 0.1423 | 7.0 | 24437 | 0.1419 | 0.0 | 0.5 | 0.8976 | |
| | 0.143 | 8.0 | 27928 | 0.1428 | 0.0 | 0.5 | 0.8976 | |
| | 0.1417 | 9.0 | 31419 | 0.1434 | 0.0 | 0.5 | 0.8976 | |
| | 0.1485 | 10.0 | 34910 | 0.1443 | 0.0 | 0.5 | 0.8976 | |
| | 0.142 | 11.0 | 38401 | 0.1455 | 0.0 | 0.5 | 0.8976 | |
| | 0.1402 | 12.0 | 41892 | 0.1464 | 0.0 | 0.5 | 0.8976 | |
| | 0.1417 | 13.0 | 45383 | 0.1423 | 0.0 | 0.5 | 0.8976 | |
| | 0.1452 | 14.0 | 48874 | 0.1450 | 0.0 | 0.5 | 0.8976 | |
| | 0.1455 | 15.0 | 52365 | 0.1423 | 0.0 | 0.5 | 0.8976 | |
| | 0.1355 | 16.0 | 55856 | 0.1422 | 0.0 | 0.5 | 0.8976 | |
| | 0.1369 | 17.0 | 59347 | 0.1431 | 0.0 | 0.5 | 0.8976 | |
| | 0.1416 | 18.0 | 62838 | 0.1436 | 0.0 | 0.5 | 0.8976 | |
| | 0.1387 | 19.0 | 66329 | 0.1418 | 0.0 | 0.5 | 0.8976 | |
| | 0.143 | 20.0 | 69820 | 0.1416 | 0.0 | 0.5 | 0.8976 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.46.3 |
| - Pytorch 2.5.1+cu124 |
| - Datasets 3.1.0 |
| - Tokenizers 0.20.3 |
| |
