| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: FacebookAI/xlm-roberta-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: trainer_output |
| | 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. --> |
| |
|
| | # trainer_output |
| | |
| | This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 2.0023 |
| | - Accuracy: 0.8598 |
| | - F1: 0.8668 |
| | - Precision: 0.8770 |
| | - Recall: 0.8598 |
| | |
| | ## 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: 32 |
| | - 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: 3 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | No log | 0.2439 | 20 | 1.6724 | 0.8537 | 0.8597 | 0.8721 | 0.8537 | |
| | | No log | 0.4878 | 40 | 2.1847 | 0.8598 | 0.8618 | 0.8640 | 0.8598 | |
| | | 0.3126 | 0.7317 | 60 | 2.0168 | 0.8598 | 0.8630 | 0.8673 | 0.8598 | |
| | | 0.3126 | 0.9756 | 80 | 2.4119 | 0.8780 | 0.8804 | 0.8904 | 0.8780 | |
| | | 0.2521 | 1.2195 | 100 | 2.2020 | 0.8902 | 0.8884 | 0.8899 | 0.8902 | |
| | | 0.2521 | 1.4634 | 120 | 2.2252 | 0.8902 | 0.8893 | 0.8908 | 0.8902 | |
| | | 0.2521 | 1.7073 | 140 | 1.9534 | 0.8476 | 0.8579 | 0.8738 | 0.8476 | |
| | | 0.2102 | 1.9512 | 160 | 2.0566 | 0.8963 | 0.8952 | 0.8948 | 0.8963 | |
| | | 0.2102 | 2.1951 | 180 | 2.1647 | 0.8659 | 0.8714 | 0.8799 | 0.8659 | |
| | | 0.0475 | 2.4390 | 200 | 2.2178 | 0.8659 | 0.8713 | 0.8795 | 0.8659 | |
| | | 0.0475 | 2.6829 | 220 | 2.2616 | 0.8659 | 0.8713 | 0.8795 | 0.8659 | |
| | | 0.0475 | 2.9268 | 240 | 2.2667 | 0.8659 | 0.8713 | 0.8795 | 0.8659 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.56.2 |
| | - Pytorch 2.8.0+cu126 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.22.1 |
| |
|
