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---
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
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