| --- |
| library_name: transformers |
| license: mit |
| base_model: xlm-roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| model-index: |
| - name: xlm-roberta-base-orm |
| 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. --> |
|
|
| # xlm-roberta-base-orm |
|
|
| This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1489 |
| - Accuracy: 0.7726 |
| - F1 Binary: 0.3856 |
| - Precision: 0.3070 |
| - Recall: 0.5185 |
|
|
| ## 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: 32 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 51 |
| - num_epochs: 4 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Binary | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:---------:|:------:| |
| | 0.1794 | 1.0 | 517 | 0.1732 | 0.3619 | 0.2385 | 0.1427 | 0.7258 | |
| | 0.1723 | 2.0 | 1034 | 0.1690 | 0.5982 | 0.2995 | 0.1970 | 0.6239 | |
| | 0.1522 | 3.0 | 1551 | 0.1818 | 0.8566 | 0.2847 | 0.4538 | 0.2074 | |
| | 0.1436 | 4.0 | 2068 | 0.1489 | 0.7726 | 0.3856 | 0.3070 | 0.5185 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.48.0 |
| - Pytorch 2.5.1+cu124 |
| - Datasets 3.1.0 |
| - Tokenizers 0.21.0 |
| |
