| --- |
| library_name: transformers |
| license: mit |
| base_model: xlm-roberta-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: urgency_classifier |
| 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. --> |
|
|
| # urgency_classifier |
| |
| 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: 0.0258 |
| - Accuracy: 0.9187 |
| - F1 Macro: 0.9201 |
| - F1 Weighted: 0.9185 |
| - Precision Macro: 0.9203 |
| - Recall Macro: 0.9221 |
| - Precision Weighted: 0.9204 |
| - Recall Weighted: 0.9187 |
| |
| ## 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 Macro | F1 Weighted | Precision Macro | Recall Macro | Precision Weighted | Recall Weighted | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:|:------------------:|:---------------:| |
| | 0.0969 | 0.625 | 50 | 0.0419 | 0.7625 | 0.7566 | 0.7447 | 0.7724 | 0.7877 | 0.7773 | 0.7625 | |
| | 0.031 | 1.25 | 100 | 0.0272 | 0.8688 | 0.8704 | 0.8658 | 0.8697 | 0.8804 | 0.8725 | 0.8688 | |
| | 0.0202 | 1.875 | 150 | 0.0232 | 0.9 | 0.9022 | 0.8993 | 0.9015 | 0.9060 | 0.9015 | 0.9 | |
| | 0.0132 | 2.5 | 200 | 0.0258 | 0.9187 | 0.9201 | 0.9185 | 0.9203 | 0.9221 | 0.9204 | 0.9187 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.57.1 |
| - Pytorch 2.9.0+cu128 |
| - Datasets 4.3.0 |
| - Tokenizers 0.22.1 |
|
|
