ynat_model

This model is a fine-tuned version of monologg/koelectra-base-v3-discriminator on the klue-ynat dataset. It achieves the following results on the evaluation set:

• Loss: 0.4102
• Accuracy: 0.8601
• Precision: 0.8494
• Recall: 0.8746
• F1-score: 0.8612

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: 5e-05
• train_batch_size: 64
• 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
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1-score
0.4002	1.0	714	0.4580	0.8396	0.8205	0.8736	0.8435
0.2917	2.0	1428	0.3983	0.8585	0.8512	0.8726	0.8606
0.2227	3.0	2142	0.4102	0.8601	0.8494	0.8746	0.8612

Framework versions

• Transformers 4.57.1
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1