RRepoGEO

REPOGEO REPORT · LITE

PAIR-code/lit

Default branch main · commit 3debb609 · scanned 5/9/2026, 3:26:48 AM

GitHub: 3,654 stars · 372 forks

AI VISIBILITY SCORE
40 /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
3 / 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 PAIR-code/lit, 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 the README's opening sentence to emphasize 'platform'

    Why:

    CURRENT
    The Learning Interpretability Tool (🔥LIT, formerly known as the Language Interpretability Tool) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data.
    COPY-PASTE FIX
    The Learning Interpretability Tool (🔥LIT, formerly known as the Language Interpretability Tool) is a visual, interactive **platform** for ML model understanding that supports text, image, and tabular data.
  • mediumtopics#2
    Add 'interpretability' and 'explainable-ai' to repository topics

    Why:

    CURRENT
    machine-learning, natural-language-processing, visualization
    COPY-PASTE FIX
    machine-learning, natural-language-processing, visualization, interpretability, explainable-ai, xai
  • lowreadme#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, such as '## Comparison with other Interpretability Tools' or '## Why LIT?', explicitly outlining how LIT differs from and complements tools like SHAP, LIME, InterpretML, and the What-If Tool, focusing on its interactive, platform-based approach.

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 PAIR-code/lit
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
InterpretML
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. InterpretML · recommended 2×
  2. ELI5 · recommended 2×
  3. SHAP (SHapley Additive exPlanations) · recommended 1×
  4. LIME (Local Interpretable Model-agnostic Explanations) · recommended 1×
  5. What-If Tool (WIT) · recommended 1×
  • CATEGORY QUERY
    How can I interactively analyze machine learning model predictions and understand their behavior?
    you: not recommended
    AI recommended (in order):
    1. SHAP (SHapley Additive exPlanations)
    2. LIME (Local Interpretable Model-agnostic Explanations)
    3. What-If Tool (WIT)
    4. InterpretML
    5. Yellowbrick
    6. TensorBoard (with What-If Tool Plugin)
    7. ELI5

    AI recommended 7 alternatives but never named PAIR-code/lit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help visualize and debug machine learning model interpretability, especially for NLP and image data?
    you: not recommended
    AI recommended (in order):
    1. SHAP
    2. LIME
    3. Captum
    4. InterpretML
    5. Grad-CAM
    6. ELI5

    AI recommended 6 alternatives but never named PAIR-code/lit. 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 PAIR-code/lit?
    pass
    AI named PAIR-code/lit explicitly

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

  • If a team adopts PAIR-code/lit in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PAIR-code/lit 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 PAIR-code/lit solve, and who is the primary audience?
    pass
    AI named PAIR-code/lit explicitly

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

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PAIR-code/lit — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite