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bert-base-finetuned-ynat – AI Model by Doowon96 | AlphaNeural AI
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Doowon96
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bert-base-finetuned-ynat
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transformers
safetensors
bert
text-classification
generated_from_trainer
ko
klue/bert-base
finetune
cc-by-sa-4.0
autotrain_compatible
endpoints_compatible
us
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bert-base-finetuned-ynat
This model is a fine-tuned version of
klue/bert-base
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.3745
F1: 0.8704
Model description
뉴스 제목을 입력하면 뉴스의 카테고리를 예측
label_map = {
'LABEL_0': 'IT/과학',
'LABEL_1': '경제',
'LABEL_2': '사회',
'LABEL_3': '생활문화',
'LABEL_4': '세계',
'LABEL_5': '스포츠',
'LABEL_6': '정치'
}
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: 256
eval_batch_size: 256
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
Training results
Training Loss
Epoch
Step
Validation Loss
F1
No log
1.0
179
0.3909
0.8655
No log
2.0
358
0.3788
0.8684
0.3774
3.0
537
0.3629
0.8699
0.3774
4.0
716
0.3776
0.8667
0.3774
5.0
895
0.3745
0.8704
Framework versions
Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0
