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: 0.4140
Accuracy: 0.8587
Precision: 0.8469
Recall: 0.8738
F1: 0.8595

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 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
0.3963	1.0	714	0.4208	0.8575	0.8443	0.8694	0.8561
0.3073	2.0	1428	0.4018	0.8584	0.8471	0.8711	0.8578
0.2317	3.0	2142	0.4140	0.8587	0.8469	0.8738	0.8595

Framework versions

Transformers 4.54.0
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.2

riranaha/ynat-model

ynat-model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

riranaha/
ynat-model