kobert_baseline_model – AI Model by mskang1120 | AlphaNeural AI

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mskang1120/
kobert_baseline_model

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text-classification

generated_from_trainer

monologg/kobert

endpoints_compatible

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kobert_baseline_model

This model is a fine-tuned version of monologg/kobert on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6932
Accuracy: 0.5046
Precision: 0.2523
Recall: 0.5
F1: 0.3354

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: 32
eval_batch_size: 32
seed: 42
optimizer: Use 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: 20
label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6937	1.0	18336	0.6935	0.4954	0.2477	0.5	0.3313
0.6935	2.0	36672	0.6932	0.5046	0.2523	0.5	0.3354
0.6932	3.0	55008	0.6931	0.5046	0.2523	0.5	0.3354
0.6932	4.0	73344	0.6931	0.5046	0.2523	0.5	0.3354
0.6933	5.0	91680	0.6931	0.5046	0.2523	0.5	0.3354

Framework versions

Transformers 5.3.0
Pytorch 2.10.0+cu128
Datasets 4.6.1
Tokenizers 0.22.2