| --- |
| library_name: transformers |
| license: mit |
| base_model: xlm-roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: trigger_id |
| 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. --> |
|
|
| # trigger_id |
| |
| 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: 0.0634 |
| - Accuracy: 0.9780 |
| - Precision: 0.7114 |
| - Recall: 0.6376 |
| - F1: 0.6725 |
| |
| ## 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: 32 |
| - eval_batch_size: 32 |
| - 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: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | No log | 1.0 | 38 | 0.1618 | 0.9513 | 0.0 | 0.0 | 0.0 | |
| | No log | 2.0 | 76 | 0.0873 | 0.9742 | 0.7385 | 0.5685 | 0.6424 | |
| | No log | 3.0 | 114 | 0.0693 | 0.9773 | 0.7357 | 0.5968 | 0.6590 | |
| | No log | 4.0 | 152 | 0.0665 | 0.9771 | 0.6768 | 0.6777 | 0.6773 | |
| | No log | 5.0 | 190 | 0.0634 | 0.9780 | 0.7114 | 0.6376 | 0.6725 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.52.4 |
| - Pytorch 2.7.1+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.21.1 |
| |
