Upload 3 files

README.md

@@ -1,25 +1,61 @@
+---
+pretty_name: JMID
+language: ja
+license: cc-by-4.0
+task_categories:
+- text-classification
+- text-generation
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files:
+    - split: train
+      path: jmid.jsonl
+- config_name: preview # first 100 samples
+  data_files:
+    - split: train
+      path: jmid_preview.jsonl
+
+---
+
 # JMID: Japanese Medical Incident Dataset
 
+
+**日本語**
+
 本データセットは、[公益財団法人日本医療機能評価機構](https://www.med-safe.jp/)の医療事故報告書に書かれている医療事故内容から、医療事故の「具体的内容」「背景・要因」「改善策」とその他の情報をまとめたものである。
 
 使い方の例は以下に載せる。
 
 
+**English**
+
+This dataset is compiled from the medical incident reports published by [the Japan Council for Quality Health Care](https://www.med-safe.jp/). It summarizes the contents of medical incidents, including the specific details, background and contributing factors, and proposed improvements, along with other related information.
+
+An example of how to use the dataset is provided below.
+
+
+
 ```python:read_jmid.py
-import json
-
-file_path = r"ここにパスを書く"
-
-with open(file_path, "r", encoding="utf-8") as f:
-    for line_number, line in enumerate(f, start=1):
-        try:
-            data = json.loads(line)
-            specific_content = data.get("具体的内容", "")
-            print(f"{line_number}行目：{specific_content}")
-        except json.JSONDecodeError as e:
-            print(f"[行{line_number}] JSONエラー: {e}")
+from datasets import load_dataset
+
+# Load the preview dataset (first 100 samples)
+ds_preview = load_dataset("Sakaji-Lab/JMID", "preview")
+
+print(ds_preview)
+print(ds_preview["train"][0])
+
+# Load the full dataset
+# ds = load_dataset("Sakaji-Lab/JMID")
+
+# print(ds) # or print()
+# print(ds["train"][0])
+
 ```
 
+**日本語**
+
 医療事故の具体的内容、背景・要因、改善策のほかに、本データセットに含まれている追加情報は以下のようなものがある。
 
 - 分類
@@ -31,9 +67,23 @@ with open(file_path, "r", encoding="utf-8") as f:
 - 具体情報
 etc...
 
+**English**
+
+In addition to the specific details, background and contributing factors, and proposed improvements of each medical incident, this dataset also includes the following supplementary information:
+
+- Classification
+- Severity of the incident
+- Discussion by the expert analysis team
+- Discussion by the expert analysis team and the General Evaluation Committee
+- Reported cases
+- Descriptive information
+- Specific information
+etc...
+
 
 ### 医療事故内容から背景・要因、改善策を出力した時のBERTScoreの値
 
+**日本語**
 以下は、実際に本データセットを用い、医療事故の内容から、背景・要因、改善策を生成させたときの結果である。（第8回～第21回報告書の全2017件）
 
 - Zero-shot
@@ -43,8 +93,22 @@ etc...
     - All Prediction：事故内容を入力し、few-shot（n=5）で出力として背景・要因、改善策を一括生成させるタスク
     - Each Prediction：事故内容を入力し、few-shot（n=5）で出力として背景・要因、改善策を段階的に生成させるタスク
 
+
+**English**
+
+The following shows the results obtained using this dataset to generate background and contributing factors as well as proposed improvements from the descriptions of medical incidents (a total of 2,017 cases from the 8th to the 21st reports):
+
+- Zero-shot
+  - All Prediction: A task in which the incident description is given as input,and both the background/contributing factors and proposed improvements are generated at once.
+  - Each Prediction: A task in which the incident description is given as input, and the background/contributing factors and proposed improvements are generated step by step.
+
+- Few-shot
+  - All Prediction: A task in which the incident description is given as input, and both the background/contributing factors and proposed improvements are generated at once using few-shot learning (n=5).
+  - Each Prediction: A task in which the incident description is given as input, and the background/contributing factors and proposed improvements are generated step by step using few-shot learning (n=5).
+
 <!-- BERTScores for generated background and causal factors from medical incident -->
-<b>医療事故の内容から、背景・要因を生成させた時のBERTScoreの平均値</b>
+<b>医療事故の内容から、背景・要因を生成させた時のBERTScoreの平均値
+(Average BERTScore when generating background and contributing factors from the description of medical incidents)</b>
 <table border="1">
   <tr>
     <th rowspan="2">モデル</th>
@@ -76,7 +140,8 @@ etc...
   </tr>
 </table>
 
-<summary><b>医療事故の��容から、改善策を生成させた時のBERTScoreの平均値</b></summary>
+<b>医療事故の内容から、改善策を生成させた時のBERTScoreの平均値
+(Average BERTScore when generating proposed improvements from the description of medical incidents)</b>
 
 <table border="1">
   <tr>
@@ -130,8 +195,9 @@ etc...
 </table>
 
 #### 具体情報を用いた際の、医療事故内容別の結果は以下である。
+The following shows the results for each type of medical incident when utilizing the specific information.
 
-## 背景・要因
+## 背景・要因(background and contributing factors)
 
 <details>
 <summary>調剤（71件）</summary>
@@ -1109,7 +1175,7 @@ etc...
 </details>
 
 
-## 改善策
+## 改善策(proposed improvements)
 
 <details>
 <summary>調剤（71件）</summary>