---
base_model: klue/roberta-base
tags:
- generated_from_trainer
- korean
- klue
widget:
- text: 환자는 심부전 진단을 받고 매일 아침 40mg의 푸로세미드를 복용하며, 지속적인 심전도 모니터링을 받습니다.
model-index:
- name: klue-roberta-base-ner-bio
  results: []
language:
- ko
pipeline_tag: token-classification
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# klue-roberta-base-ner-bio

This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/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