RRepoGEO

REPOGEO REPORT · LITE

monologg/KoELECTRA

Default branch master · commit 024fbdd6 · scanned 6/1/2026, 12:02:46 AM

GitHub: 635 stars · 136 forks

AI VISIBILITY SCORE
61 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 monologg/KoELECTRA, 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to highlight Hugging Face Transformers compatibility

    Why:

    CURRENT
    The current English README excerpt introduces ELECTRA architecture and KoELECTRA's training data.
    COPY-PASTE FIX
    KoELECTRA provides high-quality, pre-trained ELECTRA models for Korean, designed for seamless integration and strong performance with the Hugging Face `Transformers` library.
  • mediumreadme#2
    Expand the 'About KoELECTRA' section to detail its unique value

    Why:

    CURRENT
    The 'About KoELECTRA' section is currently a table of model sizes.
    COPY-PASTE FIX
    KoELECTRA is uniquely trained on 34GB of diverse Korean text, offering `KoELECTRA-Base` and `KoELECTRA-Small` models. These models are optimized for robust Korean NLP tasks, providing superior performance compared to BERT-style models through the Replaced Token Detection pre-training objective.
  • lowtopics#3
    Add 'huggingface' to repository topics

    Why:

    CURRENT
    electra, korean-nlp, language-model, nlp, pytorch, tensorflow, tensorflow2, transformers
    COPY-PASTE FIX
    electra, korean-nlp, language-model, nlp, pytorch, tensorflow, tensorflow2, transformers, huggingface

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
1 / 2
50% of queries surface monologg/KoELECTRA
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
KR-BERT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. KR-BERT · recommended 2×
  2. XLM-RoBERTa · recommended 2×
  3. KoBERT · recommended 1×
  4. KoGPT · recommended 1×
  5. Hugging Face Transformers library · recommended 1×
  • CATEGORY QUERY
    Need a robust pre-trained language model for advanced Korean NLP tasks.
    you: #3
    AI recommended (in order):
    1. KoBERT
    2. KR-BERT
    3. KoELECTRA ← you
    4. KoGPT
    5. XLM-RoBERTa
    Show full AI answer
  • CATEGORY QUERY
    Which pre-trained Korean text models offer strong performance with the Transformers library?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers library
    2. KLUE/RoBERTa
    3. KR-BERT
    4. ELECTRA
    5. snunlp/KR-ELECTRA-discriminator
    6. KoBART
    7. XLM-RoBERTa
    8. KcBERT
    9. HanBert

    AI recommended 9 alternatives but never named monologg/KoELECTRA. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 monologg/KoELECTRA?
    pass
    AI did not name monologg/KoELECTRA — 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 monologg/KoELECTRA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named monologg/KoELECTRA 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 monologg/KoELECTRA solve, and who is the primary audience?
    pass
    AI named monologg/KoELECTRA explicitly

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

Embed your GEO score

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MARKDOWN (README)
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monologg/KoELECTRA — 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