| --- |
| library_name: transformers |
| license: mit |
| base_model: roberta-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: roberta-large-binary-classification |
| 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-large-binary-classification |
|
|
| This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6983 |
| - Accuracy: 0.7580 |
| - F1 Macro: 0.7453 |
| - Precision Macro: 0.7498 |
| - Recall Macro: 0.7425 |
| - Auc: 0.7941 |
|
|
| ## 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: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | Auc | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:------:| |
| | No log | 1.0 | 79 | 0.6751 | 0.5955 | 0.3733 | 0.2978 | 0.5 | 0.6194 | |
| | No log | 2.0 | 158 | 0.6642 | 0.5955 | 0.3733 | 0.2978 | 0.5 | 0.6210 | |
| | No log | 3.0 | 237 | 0.5609 | 0.7102 | 0.6895 | 0.7003 | 0.6859 | 0.7701 | |
| | No log | 4.0 | 316 | 0.5676 | 0.7070 | 0.6907 | 0.6954 | 0.6883 | 0.7734 | |
| | No log | 5.0 | 395 | 0.6983 | 0.7580 | 0.7453 | 0.7498 | 0.7425 | 0.7941 | |
| | No log | 6.0 | 474 | 0.7766 | 0.7420 | 0.7319 | 0.7322 | 0.7316 | 0.7802 | |
| | 0.4887 | 7.0 | 553 | 1.1879 | 0.7452 | 0.7266 | 0.7399 | 0.7217 | 0.7761 | |
| | 0.4887 | 8.0 | 632 | 1.6676 | 0.7484 | 0.7242 | 0.7504 | 0.7180 | 0.7789 | |
| | 0.4887 | 9.0 | 711 | 1.6440 | 0.7548 | 0.7364 | 0.7511 | 0.7310 | 0.7889 | |
| | 0.4887 | 10.0 | 790 | 1.7092 | 0.7548 | 0.7364 | 0.7511 | 0.7310 | 0.7928 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.57.1 |
| - Pytorch 2.8.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.1 |
| |
