RRepoGEO

REPOGEO REPORT · LITE

SpursGoZmy/Tabular-LLM

Default branch main · commit 2e097979 · scanned 6/6/2026, 12:38:08 PM

GitHub: 643 stars · 45 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 SpursGoZmy/Tabular-LLM, 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
  • hightopics#1
    Add more specific topics to improve categorization

    Why:

    CURRENT
    llm, tabular-data
    COPY-PASTE FIX
    llm, tabular-data, llm-fine-tuning, tabular-llm, table-qa, table-to-text, instruction-tuning, dataset-collection
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root containing the text of the MIT License.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a `homepage` URL in the repository settings, linking to a dedicated project page, documentation, or a relevant section of the README if no external page exists.

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 SpursGoZmy/Tabular-LLM
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 Transformers · recommended 1×
  3. BERT · recommended 1×
  4. RoBERTa · recommended 1×
  5. DeBERTa · recommended 1×
  • CATEGORY QUERY
    How to fine-tune large language models to better understand and process complex tabular data?
    you: not recommended
    AI recommended (in order):
    1. Ludwig
    2. Hugging Face Transformers
    3. BERT
    4. RoBERTa
    5. DeBERTa
    6. TabPFN
    7. TabTransformer
    8. PyTorch
    9. TensorFlow
    10. DistilBERT
    11. TinyBERT
    12. AutoGluon
    13. DeepTables

    AI recommended 13 alternatives but never named SpursGoZmy/Tabular-LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find datasets and models for building LLMs specialized in table question answering or generation?
    you: not recommended
    AI recommended (in order):
    1. WikiTableQuestions (WTQ)
    2. TabFact (wenhuchen/Table-Fact-Checking)
    3. ToTaL (Table-to-Text and Text-to-Table) (microsoft/ToTaL)
    4. Totto (Table-to-Text)
    5. Spider
    6. TAPAS (Table Parser)
    7. Open-domain Question Answering over Tables (ODQA-Tables) (laituan245/ODQA-Tables)

    AI recommended 7 alternatives but never named SpursGoZmy/Tabular-LLM. 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 SpursGoZmy/Tabular-LLM?
    pass
    AI named SpursGoZmy/Tabular-LLM explicitly

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

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