---
library_name: transformers
base_model: klue/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-unfair
  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. -->

# roberta-unfair

This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1969
- Accuracy: 0.9679
- F1 Macro: 0.9620
- Precision Macro: 0.9559
- Recall Macro: 0.9688
- Recall Unfair: 0.9710

## 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: 16
- seed: 20
- 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: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | Recall Unfair |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:-------------:|
| 0.1406        | 0.4237 | 200  | 0.2180          | 0.9390   | 0.9284   | 0.9197          | 0.9389       | 0.9384        |
| 0.11          | 0.8475 | 400  | 0.1185          | 0.9690   | 0.9632   | 0.9582          | 0.9685       | 0.9674        |
| 0.044         | 1.2712 | 600  | 0.1787          | 0.9615   | 0.9544   | 0.9484          | 0.9611       | 0.9601        |
| 0.0487        | 1.6949 | 800  | 0.1957          | 0.9583   | 0.9507   | 0.9437          | 0.9588       | 0.9601        |
| 0.0222        | 2.1186 | 1000 | 0.1170          | 0.9754   | 0.9707   | 0.9674          | 0.9741       | 0.9710        |
| 0.0139        | 2.5424 | 1200 | 0.2559          | 0.9594   | 0.9522   | 0.9434          | 0.9627       | 0.9710        |
| 0.0165        | 2.9661 | 1400 | 0.1969          | 0.9679   | 0.9620   | 0.9559          | 0.9688       | 0.9710        |


### Framework versions

- Transformers 4.55.1
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4