| --- |
| library_name: transformers |
| license: mit |
| base_model: roberta-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: roberta-large-ToM2 |
| 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-ToM2 |
|
|
| 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: 0.2561 |
| - Accuracy: 0.9540 |
| - F1: 0.9604 |
| - Precision: 0.97 |
| - Recall: 0.9510 |
|
|
| ## 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: 2015 |
| - 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: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.6475 | 1.0 | 93 | 0.3671 | 0.9103 | 0.9195 | 0.8696 | 0.9756 | |
| | 0.3063 | 2.0 | 186 | 0.2410 | 0.9103 | 0.9176 | 0.8864 | 0.9512 | |
| | 0.1717 | 3.0 | 279 | 0.2763 | 0.9231 | 0.9302 | 0.8889 | 0.9756 | |
| | 0.1159 | 4.0 | 372 | 0.4473 | 0.9231 | 0.9318 | 0.8723 | 1.0 | |
| | 0.0568 | 5.0 | 465 | 0.4014 | 0.9359 | 0.9412 | 0.9091 | 0.9756 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.56.0 |
| - Pytorch 2.8.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.0 |
| |
