Datasets:

sbintuitions
/

JamC-QA

@@ -49,7 +49,7 @@ English/[Japanese](README_ja.md)
 ## Dataset Summary
 This benchmark evaluates knowledge specific to Japan through multiple-choice questions.
-It covers eight categories: culture, custom, regional identity, geography, history, government, law, and healthcare.
 Achieving high performance requires broad and detailed understanding of Japan across these categories.
 ## Leaderboard
@@ -59,7 +59,7 @@ Achieving high performance requires broad and detailed understanding of Japan ac
 In this multiple-choice question answering task, the LLM outputs the option string rather than the option label,
 and accuracy is calculated as the proportion of questions whose output exactly matches the gold correct option string.
-| Model | Micro-average | culture | custom | regional identity | geography | history | government | law | healthcare |
 |:---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
 | [sarashina2-8x70b](https://huggingface.co/sbintuitions/sarashina2-8x70b) | **0.7254** | 0.7141 | **0.7750** | **0.7607** | 0.6544 | **0.7843** | 0.7364 | 0.6321 | **0.9167** |
 | [sarashina2-70b](https://huggingface.co/sbintuitions/sarashina2-70b) | 0.7246 | **0.7188** | 0.7450 | 0.7355 | **0.6728** | 0.7638 | 0.7636 | 0.6656 | **0.9167** |
@@ -97,7 +97,7 @@ An example from culture category looks as follows:
 - `qid (str)`: A unique identifier for each question.
 - `category (str)`: The category of the question.
-  - culture, custom, regional identity, geography, history, government, law, and healthcare
 - `question (str)`: The question text.
   - Converted from full-width to half-width characters, excluding katakana characters.
   - Does not contain any line breaks (`\n`).
@@ -119,7 +119,7 @@ Number of Examples:
 | --- | ---: | ---: |
 | culture | 4 | 640 |
 | custom | 4 | 200 |
-| regional identity | 4 | 397 |
 | geography | 4 | 272 |
 | history | 4 | 343 |
 | government | 4 | 110 |

 ## Dataset Summary
 This benchmark evaluates knowledge specific to Japan through multiple-choice questions.
+It covers eight categories: culture, custom, regional_identity, geography, history, government, law, and healthcare.
 Achieving high performance requires broad and detailed understanding of Japan across these categories.
 ## Leaderboard
 In this multiple-choice question answering task, the LLM outputs the option string rather than the option label,
 and accuracy is calculated as the proportion of questions whose output exactly matches the gold correct option string.
+| Model | Micro-average | culture | custom | regional_identity | geography | history | government | law | healthcare |
 |:---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
 | [sarashina2-8x70b](https://huggingface.co/sbintuitions/sarashina2-8x70b) | **0.7254** | 0.7141 | **0.7750** | **0.7607** | 0.6544 | **0.7843** | 0.7364 | 0.6321 | **0.9167** |
 | [sarashina2-70b](https://huggingface.co/sbintuitions/sarashina2-70b) | 0.7246 | **0.7188** | 0.7450 | 0.7355 | **0.6728** | 0.7638 | 0.7636 | 0.6656 | **0.9167** |
 - `qid (str)`: A unique identifier for each question.
 - `category (str)`: The category of the question.
+  - culture, custom, regional_identity, geography, history, government, law, and healthcare
 - `question (str)`: The question text.
   - Converted from full-width to half-width characters, excluding katakana characters.
   - Does not contain any line breaks (`\n`).
 | --- | ---: | ---: |
 | culture | 4 | 640 |
 | custom | 4 | 200 |
+| regional_identity | 4 | 397 |
 | geography | 4 | 272 |
 | history | 4 | 343 |
 | government | 4 | 110 |