---
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