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ynat_model – AI Model by yebita | AlphaNeural AI
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yebita
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ynat_model
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transformers
safetensors
electra
text-classification
korean_NLP
KoELECTRA
generated_from_trainer
ko
autotrain_compatible
endpoints_compatible
us
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ynat_model
This model is a fine-tuned version of
brina-4
on the klue-ynat dataset. It achieves the following results on the evaluation set:
Loss: 0.5193
Accuracy: 0.8567
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: 5
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.4049
1.0
714
0.4515
0.8425
0.3132
2.0
1428
0.4136
0.8497
0.2289
3.0
2142
0.4638
0.8452
0.1833
4.0
2856
0.4686
0.8598
0.1155
5.0
3570
0.5193
0.8567
Framework versions
Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0
