bert-base-finetuned-ynat

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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

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0