kobert – AI Model by Gahyoung | AlphaNeural AI

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Gahyoung/
kobert

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text-classification

generated_from_trainer

monologg/kobert

endpoints_compatible

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kobert

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

Loss: 0.6936
Accuracy: 0.4775

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: 16
eval_batch_size: 16
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: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	100	0.6962	0.4775
No log	2.0	200	0.6918	0.5225
No log	3.0	300	0.6954	0.4775
No log	4.0	400	0.6949	0.4775
0.6962	5.0	500	0.6936	0.5225
0.6962	6.0	600	0.6922	0.5225
0.6962	7.0	700	0.6946	0.4775
0.6962	8.0	800	0.6942	0.4775
0.6962	9.0	900	0.6948	0.4775
0.6952	10.0	1000	0.6936	0.4775

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2