output

This model is a fine-tuned version of kisti/korscideberta on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1835
• Accuracy: 0.9280
• Precision: 0.9280
• Recall: 0.9280
• F1: 0.9279

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: 8
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.4034	0.3188	500	0.3371	0.8543	0.8737	0.8543	0.8538
0.2338	0.6376	1000	0.1964	0.9159	0.9159	0.9159	0.9159
0.2079	0.9563	1500	0.1719	0.9322	0.9323	0.9322	0.9323

Framework versions

• Transformers 4.45.0
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.20.3