RRepoGEO

REPOGEO REPORT · LITE

songys/AwesomeKorean_Data

Default branch master · commit 49bbb78f · scanned 6/5/2026, 8:08:29 AM

GitHub: 914 stars · 108 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface songys/AwesomeKorean_Data, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README's opening to clarify it's a curated list of links

    Why:

    CURRENT
    # AwesomeKorean_Data

- 비교적 대부분의 사람들이 접근할 수 있는 오픈 데이터를 정리하였다. 구할 수 있는 모든 데이터를 쏟아 부어서 end to end로 모델을 만들어 보겠다는 포부를 가진 분들의 진입을 쉽게하기 위한 목적이고, 정교한 데이터 구축을 위해서는 이후에 어떠한 데이터가 필요한지를 살펴보기 위한 과정이다.
    COPY-PASTE FIX
    # AwesomeKorean_Data

이 저장소는 한국어 데이터 세트 링크를 모아놓은 큐레이션 목록입니다. (This repository is a curated list of links to Korean datasets.) 비교적 대부분의 사람들이 접근할 수 있는 오픈 데이터를 정리하였다. 구할 수 있는 모든 데이터를 쏟아 부어서 end to end로 모델을 만들어 보겠다는 포부를 가진 분들의 진입을 쉽게하기 위한 목적이고, 정교한 데이터 구축을 위해서는 이후에 어떠한 데이터가 필요한지를 살펴보기 위한 과정이다.
  • mediumreadme#2
    Clarify the license situation in the README

    Why:

    COPY-PASTE FIX
    이 저장소의 `LICENSE` 파일은 표준 SPDX 템플릿이 아닌 사용자 지정 또는 복합 라이선스를 나타냅니다. 여기에 링크된 각 데이터 세트의 라이선스는 해당 원본 소스를 참조하시기 바랍니다. (The `LICENSE` file in this repository indicates a custom or compound license, not a standard SPDX template. For the licenses of each dataset linked herein, please refer to their respective original sources.)

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface songys/AwesomeKorean_Data
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Naver sentiment movie corpus (NSMC)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Naver sentiment movie corpus (NSMC) · recommended 1×
  2. KorQuAD (Korean Question Answering Dataset) · recommended 1×
  3. Korean Hate Speech Dataset (NAVER AI LAB) · recommended 1×
  4. AI Hub Korean Datasets · recommended 1×
  5. Korean Parallel Corpus (National Institute of Korean Language) · recommended 1×
  • CATEGORY QUERY
    Looking for a curated list of publicly available Korean text datasets for AI training.
    you: not recommended
    AI recommended (in order):
    1. Naver sentiment movie corpus (NSMC)
    2. KorQuAD (Korean Question Answering Dataset)
    3. Korean Hate Speech Dataset (NAVER AI LAB)
    4. AI Hub Korean Datasets
    5. Korean Parallel Corpus (National Institute of Korean Language)
    6. NIKL (National Institute of Korean Language) Corpora
    7. Korean News Dataset

    AI recommended 7 alternatives but never named songys/AwesomeKorean_Data. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best open-source Korean language datasets suitable for various NLP applications?
    you: not recommended
    AI recommended (in order):
    1. KorQuAD
    2. Naver NLP Challenge Datasets
    3. NSMC - Naver Sentiment Movie Corpus
    4. KLUE
    5. AI Hub
    6. Korean Hate Speech Dataset
    7. Korean Parallel Corpora
    8. Sejong Corpus
    9. Wikitext-ko

    AI recommended 9 alternatives but never named songys/AwesomeKorean_Data. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of songys/AwesomeKorean_Data?
    pass
    AI did not name songys/AwesomeKorean_Data — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts songys/AwesomeKorean_Data in production, what risks or prerequisites should they evaluate first?
    pass
    AI named songys/AwesomeKorean_Data explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo songys/AwesomeKorean_Data solve, and who is the primary audience?
    pass
    AI did not name songys/AwesomeKorean_Data — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of songys/AwesomeKorean_Data. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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songys/AwesomeKorean_Data — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite