RRepoGEO

REPOGEO REPORT · LITE

huggingface/autotrain-advanced

Default branch main · commit 1873aca3 · scanned 5/11/2026, 11:52:33 AM

GitHub: 4,573 stars · 624 forks

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 huggingface/autotrain-advanced, 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
  • highabout#1
    Update repository description to reflect original purpose and key features

    Why:

    CURRENT
    🤗 AutoTrain Advanced
    COPY-PASTE FIX
    🤗 AutoTrain Advanced: A no-code solution for faster and easier training and deployment of state-of-the-art machine learning models, including advanced LLM finetuning (SFT, ORPO, DPO, Reward).
  • mediumtopics#2
    Add more specific topics related to no-code and LLM finetuning

    Why:

    CURRENT
    autotrain, deep-learning, huggingface, machine-learning, natural-language-processing, natural-language-understanding, python
    COPY-PASTE FIX
    autotrain, deep-learning, huggingface, machine-learning, natural-language-processing, natural-language-understanding, python, no-code-ml, llm-finetuning, automated-ml
  • lowreadme#3
    Rephrase the README's opening sentence to clearly state its original purpose in past tense

    Why:

    CURRENT
    AutoTrain Advanced: faster and easier training and deployments of state-of-the-art machine learning models. AutoTrain Advanced is a no-code solution that allows you to train machine learning models in just a few clicks.
    COPY-PASTE FIX
    AutoTrain Advanced was a no-code solution designed for faster and easier training and deployments of state-of-the-art machine learning models, including advanced LLM finetuning (SFT, ORPO, DPO, Reward).

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 huggingface/autotrain-advanced
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ludwig
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ludwig · recommended 1×
  2. Hugging Face AutoTrain · recommended 1×
  3. Google Cloud Vertex AI Workbench · recommended 1×
  4. RunwayML · recommended 1×
  5. LlamaIndex · recommended 1×
  • CATEGORY QUERY
    How can I easily finetune large language models without extensive coding?
    you: not recommended
    AI recommended (in order):
    1. Ludwig
    2. Hugging Face AutoTrain
    3. Google Cloud Vertex AI Workbench
    4. RunwayML
    5. LlamaIndex
    6. OpenAI API

    AI recommended 6 alternatives but never named huggingface/autotrain-advanced. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some no-code solutions for rapid machine learning model training and deployment?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud AutoML
    2. Microsoft Azure Machine Learning Studio (classic) / Azure Machine Learning Designer
    3. Amazon SageMaker Canvas
    4. DataRobot
    5. H2O.ai Driverless AI
    6. Teachable Machine by Google

    AI recommended 6 alternatives but never named huggingface/autotrain-advanced. 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 huggingface/autotrain-advanced?
    pass
    AI did not name huggingface/autotrain-advanced — 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 huggingface/autotrain-advanced in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huggingface/autotrain-advanced 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 huggingface/autotrain-advanced solve, and who is the primary audience?
    pass
    AI did not name huggingface/autotrain-advanced — 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

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