metadata
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: urgency_classifier
results: []
urgency_classifier
This model is a fine-tuned version of 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