| --- |
| library_name: transformers |
| license: mit |
| base_model: roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: roberta-base_binary |
| 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-base_binary |
| |
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1729 |
| - Precision: 0.8178 |
| - Recall: 0.6136 |
| - F1: 0.7012 |
| - F0.5: 0.7668 |
| - Macro Precision: 0.8824 |
| - Macro Recall: 0.7971 |
| - Macro F1: 0.8323 |
| - Macro F0.5: 0.8602 |
| |
| ## 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: 3e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 32 |
| - 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: 2 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | F0.5 | Macro Precision | Macro Recall | Macro F1 | Macro F0.5 | |
| |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:------:|:---------------:|:------------:|:--------:|:----------:| |
| | 0.1963 | 1.0 | 1926 | 0.1702 | 0.8148 | 0.6179 | 0.7028 | 0.7660 | 0.8814 | 0.7991 | 0.8333 | 0.8601 | |
| | 0.1621 | 1.9992 | 3850 | 0.1698 | 0.8027 | 0.6472 | 0.7166 | 0.7659 | 0.8772 | 0.8124 | 0.8405 | 0.8613 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.50.3 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.5.0 |
| - Tokenizers 0.21.1 |
|
|
