RRepoGEO

REPOGEO REPORT · LITE

pyg-team/pytorch-frame

Default branch master · commit f6ff914e · scanned 5/31/2026, 6:51:44 PM

GitHub: 785 stars · 71 forks

AI VISIBILITY SCORE
33 /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
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 pyg-team/pytorch-frame, 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
    Emphasize graph integration in README's opening statement

    Why:

    CURRENT
    A modular deep learning framework for building neural network models on heterogeneous tabular data.
    COPY-PASTE FIX
    A modular deep learning framework for building neural network models on heterogeneous tabular data, with seamless integration of graph-structured information via PyTorch Geometric.
  • mediumtopics#2
    Add graph-related topics

    Why:

    CURRENT
    data-frame, deep-learning, pytorch, tabular-learning
    COPY-PASTE FIX
    data-frame, deep-learning, pytorch, tabular-learning, graph-neural-networks, pytorch-geometric
  • mediumcomparison#3
    Add a 'Comparison with Alternatives' section to README

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled 'Comparison with Alternatives' or 'Why PyTorch Frame?', explicitly contrasting its features (e.g., graph integration, modularity for diverse column types) with common tabular deep learning libraries.

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 pyg-team/pytorch-frame
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch-Tabular
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch-Tabular · recommended 1×
  2. PyTorch · recommended 1×
  3. TabNet · recommended 1×
  4. NODE · recommended 1×
  5. FT-Transformer · recommended 1×
  • CATEGORY QUERY
    How can I apply deep learning models to complex tabular datasets using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch-Tabular
    2. PyTorch
    3. TabNet
    4. NODE
    5. FT-Transformer
    6. DeepFM
    7. AutoGluon-Tabular

    AI recommended 7 alternatives but never named pyg-team/pytorch-frame. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best PyTorch libraries for building neural networks with mixed tabular data types?
    you: not recommended
    AI recommended (in order):
    1. PyTorch-Tabular (pytorch-tabular/pytorch-tabular)
    2. AutoGluon-Tabular (awslabs/autogluon)
    3. TabNet (dreamquark-ai/tabnet)
    4. Pytorch Lightning (Lightning-AI/lightning)
    5. FastAI (fastai/fastai)
    6. Catalyst (catalyst-team/catalyst)

    AI recommended 6 alternatives but never named pyg-team/pytorch-frame. 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 pyg-team/pytorch-frame?
    pass
    AI named pyg-team/pytorch-frame explicitly

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

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