RRepoGEO

REPOGEO REPORT · LITE

bhoov/exbert

Default branch master · commit d27b6236 · scanned 6/9/2026, 10:32:35 PM

GitHub: 607 stars · 54 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
68 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
Rule findings
1 pass · 1 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 bhoov/exbert, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ['nlp', 'transformers', 'interpretability', 'visualization', 'attention', 'bert', 'llm-interpretability', 'debugging-llm']
  • highreadme#2
    Broaden the README's H3 description to include LLM interpretability and debugging

    Why:

    CURRENT
    ### A Visual Analysis Tool to Explore Learned Representations in Transformers Models
    COPY-PASTE FIX
    ### A Visual Analysis Tool to Interpret, Debug, and Explore Learned Representations in Transformer Models (LLMs)
  • mediumreadme#3
    Expand the "Overview" section's first sentence to highlight interpretability and debugging

    Why:

    CURRENT
    exBERT is a tool that enables users to explore the learned attention weights and contextual represent
    COPY-PASTE FIX
    exBERT is a powerful tool that enables users to interpret, debug, and explore the learned attention weights and contextual representations within transformer models.

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
1 / 2
50% of queries surface bhoov/exbert
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
5%
Of all named tools, what % are you?
Top rival
LIME
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LIME · recommended 2×
  2. SHAP · recommended 2×
  3. Captum · recommended 2×
  4. TensorBoard · recommended 2×
  5. Attention Is All You Need · recommended 1×
  • CATEGORY QUERY
    How can I visually analyze attention patterns and learned representations in transformer models?
    you: #1
    AI recommended (in order):
    1. exBERT ← you
    2. LIME
    3. SHAP
    4. Attention Is All You Need
    5. BertViz
    6. Captum
    7. TensorBoard
    Show full AI answer
  • CATEGORY QUERY
    What tools help interpret and debug internal workings of large language models?
    you: not recommended
    AI recommended (in order):
    1. LIME
    2. SHAP
    3. Captum
    4. TensorBoard
    5. Weights & Biases (W&B)
    6. Gradio
    7. PyTorch Debugger
    8. pdb
    9. OpenAI Playground
    10. LangSmith
    11. Concept Activation Vectors (CAVs)
    12. Rank-1 Model Editing

    AI recommended 12 alternatives but never named bhoov/exbert. 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 bhoov/exbert?
    pass
    AI named bhoov/exbert explicitly

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

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

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

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bhoov/exbert — 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