RRepoGEO

REPOGEO REPORT · LITE

gururise/AlpacaDataCleaned

Default branch main · commit d03c782b · scanned 5/10/2026, 6:33:04 PM

GitHub: 1,597 stars · 155 forks

AI VISIBILITY SCORE
28 /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
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 gururise/AlpacaDataCleaned, 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
  • highabout#1
    Refine the 'About' description for clarity and specificity

    Why:

    CURRENT
    Alpaca dataset from Stanford, cleaned and curated
    COPY-PASTE FIX
    A high-quality, cleaned, and curated instruction dataset for fine-tuning Large Language Models (LLMs), addressing issues in the original Stanford Alpaca dataset.
  • mediumhomepage#2
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/gururise/AlpacaDataCleaned

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 gururise/AlpacaDataCleaned
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/datasets
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/datasets · recommended 1×
  2. EleutherAI/The-Pile · recommended 1×
  3. Google's C4 (Colossal Clean Crawled Corpus) · recommended 1×
  4. OpenWebText2 · recommended 1×
  5. Stanford Question Answering Dataset (SQuAD) · recommended 1×
  • CATEGORY QUERY
    Where can I find a high-quality, curated dataset for fine-tuning large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Datasets (huggingface/datasets)
    2. EleutherAI's The Pile (EleutherAI/The-Pile)
    3. Google's C4 (Colossal Clean Crawled Corpus)
    4. OpenWebText2
    5. Stanford Question Answering Dataset (SQuAD)
    6. GLUE (General Language Understanding Evaluation) Benchmark
    7. Common Crawl

    AI recommended 7 alternatives but never named gururise/AlpacaDataCleaned. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a reliable, pre-processed instruction dataset to improve LLM fine-tuning results.
    you: not recommended
    AI recommended (in order):
    1. Alpaca-GPT4
    2. ShareGPT
    3. Dolly 2.0
    4. FLAN
    5. Self-Instruct

    AI recommended 5 alternatives but never named gururise/AlpacaDataCleaned. 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 gururise/AlpacaDataCleaned?
    pass
    AI named gururise/AlpacaDataCleaned explicitly

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

  • If a team adopts gururise/AlpacaDataCleaned in production, what risks or prerequisites should they evaluate first?
    pass
    AI named gururise/AlpacaDataCleaned 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 gururise/AlpacaDataCleaned solve, and who is the primary audience?
    pass
    AI did not name gururise/AlpacaDataCleaned — 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?

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gururise/AlpacaDataCleaned — 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