---
license: cc-by-sa-4.0
task_categories:
- question-answering
language:
- ja
viewer: true
columns:
  - name: id
    type: int
  - name: question
    type: string
  - name: choice0
    type: string
  - name: choice1
    type: string
  - name: choice2
    type: string
  - name: choice3
    type: string
  - name: choice4
    type: string
  - name: label
    type: int
  - name: task
    type: string
---
# Dataset Card for Nayose-Bench-Instruction

This dataset was created as a benchmark for the entity resolution task in the pharmaceutical domain.

## Dataset Details

This dataset is designed for the entity resolution task in the pharmaceutical domain.
The entity resolution task refers to a paraphrasing task, such as rephrasing drug names, converting chemical substances into brand names, or rewriting chemical substances into chemical formulas.

## Uses

```python
from datasets import load_dataset
load_dataset("EQUES/Nayose-Bench-QA")
```
```json
{
  "id": 7542,
  "question": "イベルドミド塩酸塩の別表現は?",
  "choice0": "イムガツズマブ",
  "choice1": "Plusonermin (JAN)",
  "choice2": "Iberdomide hydrochloride (USAN)",
  "choice3": "ハートナップ病",
  "choice4": "カルメグリプチン二塩酸塩",
  "label": 2,
  "task": "replace"
}
```

## Dataset Structure

The data is stored in a JSONL file, where each record consists of the following fields: `"id"`, `"question"`, `"choice0"`, `"choice1"`, `"choice2"`, `"choice3"`, `"choice4"`, `"label"`, `"task"`.  

Example:  

```json
{"id": 30512, "question": "トリプロピオン酸エストリオールの別呼称は?", "choice0": "シデフェロン (JAN)", "choice1": "Lemildipine (JAN/INN)", "choice2": "アッシャー症候群", "choice3": "ナファゾリン塩酸塩・マレイン酸フェニラミン", "choice4": "Estriol tripropionate (JAN)", "label": 4, "task": "replace"}
```

## Source Data

This dataset was created by processing [the KEGG DRUG Database](https://www.kegg.jp/kegg/drug/drug_ja.html), a database that centrally aggregates pharmaceutical information from the perspective of chemical structures and components.


## Dataset Creater
Created by Takuro Fujii (tkr.fujii.ynu@gmail.com)


## Acknowledgement
本データセットは、経済産業省及び国立研究開発法人新エネルギー・産業技術総合開発機構（NEDO）による生成AI開発力強化プロジェクト「GENIAC」により支援を受けた成果の一部である。