| --- |
| library_name: transformers |
| license: mit |
| base_model: xlm-roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| model-index: |
| - name: xlm-roberta-base_42 |
| 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_42 |
| |
| 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: 0.3930 |
| - F1-score: 0.8657 |
| - Accuracy: 0.8657 |
| - Precision: 0.8658 |
| - Recall: 0.8659 |
| |
| ## 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: 5e-06 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - 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: 6 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1-score | Accuracy | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:| |
| | No log | 1.0 | 379 | 0.4004 | 0.8284 | 0.8287 | 0.8299 | 0.8282 | |
| | 0.5426 | 2.0 | 758 | 0.3531 | 0.8502 | 0.8503 | 0.8508 | 0.8500 | |
| | 0.4202 | 3.0 | 1137 | 0.3569 | 0.8564 | 0.8565 | 0.8566 | 0.8563 | |
| | 0.3646 | 4.0 | 1516 | 0.3520 | 0.8688 | 0.8688 | 0.8689 | 0.8687 | |
| | 0.3646 | 5.0 | 1895 | 0.4078 | 0.8564 | 0.8565 | 0.8577 | 0.8569 | |
| | 0.3229 | 6.0 | 2274 | 0.3930 | 0.8657 | 0.8657 | 0.8658 | 0.8659 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.47.1 |
| - Pytorch 2.5.1+cu124 |
| - Datasets 3.2.0 |
| - Tokenizers 0.21.0 |
| |
