| --- |
| library_name: transformers |
| license: mit |
| base_model: xlm-roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: sap_predictions_model |
| 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. --> |
|
|
| # sap_predictions_model |
|
|
| This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 6.3177 |
| - Accuracy: 0.1599 |
| - F1: 0.0713 |
|
|
| ## 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: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 128 |
| - 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_ratio: 0.1 |
| - num_epochs: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| |
| | 8.7072 | 0.6425 | 1000 | 8.5615 | 0.0156 | 0.0018 | |
| | 7.9463 | 1.2846 | 2000 | 7.8865 | 0.0445 | 0.0110 | |
| | 7.3576 | 1.9271 | 3000 | 7.2356 | 0.1019 | 0.0376 | |
| | 6.8566 | 2.5692 | 4000 | 6.7092 | 0.1424 | 0.0591 | |
| | 6.3983 | 3.2114 | 5000 | 6.3177 | 0.1599 | 0.0713 | |
| | 6.1392 | 3.8538 | 6000 | 6.0647 | 0.1756 | 0.0821 | |
| | 6.0378 | 4.4960 | 7000 | 5.9330 | 0.1819 | 0.0866 | |
|
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|
| ### Framework versions |
|
|
| - Transformers 4.50.3 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.5.0 |
| - Tokenizers 0.21.1 |
|
|
