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--- |
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base_model: klue/roberta-base |
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tags: |
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- generated_from_trainer |
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- korean |
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- klue |
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widget: |
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- text: 환자는 심부전 진단을 받고 매일 아침 40mg의 푸로세미드를 복용하며, 지속적인 심전도 모니터링을 받습니다. |
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model-index: |
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- name: klue-roberta-base-ner-bio |
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results: [] |
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language: |
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- ko |
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pipeline_tag: token-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# klue-roberta-base-ner-bio |
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This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0057 |
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- Precision: 0.9888 |
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- Recall: 1.0 |
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- F1: 0.9944 |
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- Accuracy: 0.9998 |
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## Model description |
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간단한 의료 관련 개체명 인식을 제공합니다. |
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- 약물명 [DR] |
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- 질병명 [DS] |
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- 유전자/단백질 명 [GN] |
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- 임상 증상 [CS] |
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- 의료 기기 [MD] |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 30 | 0.0363 | 0.8056 | 0.8898 | 0.8456 | 0.9886 | |
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| No log | 2.0 | 60 | 0.0079 | 0.9888 | 1.0 | 0.9944 | 0.9998 | |
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| No log | 3.0 | 90 | 0.0057 | 0.9888 | 1.0 | 0.9944 | 0.9998 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu118 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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