klue-roberta-base-ner-bio

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

• Loss: 0.0057
• Precision: 0.9888
• Recall: 1.0
• F1: 0.9944
• Accuracy: 0.9998

Model description

간단한 의료 관련 개체명 인식을 제공합니다.

• 약물명 [DR]
• 질병명 [DS]
• 유전자/단백질 명 [GN]
• 임상 증상 [CS]
• 의료 기기 [MD]

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	30	0.0363	0.8056	0.8898	0.8456	0.9886
No log	2.0	60	0.0079	0.9888	1.0	0.9944	0.9998
No log	3.0	90	0.0057	0.9888	1.0	0.9944	0.9998

Framework versions

• Transformers 4.40.2
• Pytorch 2.3.0+cu118
• Datasets 2.19.1
• Tokenizers 0.19.1