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roberta-base-klue-ynat
hyeonwoo112sad/roberta-base-klue-ynat
roberta-base-klue-ynat is a text classification model from hyeonwoo112sad. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Jul 22, 2026
- Updated
- Jul 22, 2026
- SHA
- ae69e380
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Base models
Model card
From the Hugging Face model README
<!-- 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-base-klue-ynat
This model is a fine-tuned version of klue/roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3726
- Accuracy: 0.8721
- Precision: 0.8684
- Recall: 0.8763
- F1: 0.8721
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: 32
- eval_batch_size: 64
- 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
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.3826 | 1.0 | 1428 | 0.4227 | 0.8518 | 0.8350 | 0.8788 | 0.8521 |
| 0.3051 | 2.0 | 2856 | 0.3762 | 0.8690 | 0.8730 | 0.8587 | 0.8648 |
| 0.2320 | 3.0 | 4284 | 0.3726 | 0.8721 | 0.8684 | 0.8763 | 0.8721 |
Framework versions
- Transformers 5.14.1
- Pytorch 2.13.0+cpu
- Datasets 5.0.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Jul 22, 2026
- Updated
- Jul 22, 2026