RRepoGEO

REPOGEO REPORT · LITE

alexa/massive

Default branch main · commit f966f218 · scanned 5/27/2026, 7:48:13 AM

GitHub: 559 stars · 57 forks

AI VISIBILITY SCORE
69 /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 alexa/massive, 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
    Clarify project identity in README H1

    Why:

    CURRENT
    # MASSIVE
    COPY-PASTE FIX
    # MASSIVE: A Massively Multilingual Dataset for NLU Research
  • hightopics#2
    Add descriptive topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    multilingual-nlu, nlu-dataset, intent-recognition, slot-filling, voice-assistants, natural-language-understanding, machine-learning-datasets, large-scale-data
  • mediumhomepage#3
    Add a project homepage link

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://mmnlu-22.github.io/

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 alexa/massive
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
MultiATIS++
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MultiATIS++ · recommended 1×
  2. MTOP · recommended 1×
  3. XLU · recommended 1×
  4. WikiLingua · recommended 1×
  5. Common Voice · recommended 1×
  • CATEGORY QUERY
    Where can I find a large multilingual dataset for intent and slot recognition?
    you: #1
    AI recommended (in order):
    1. Massive ← you
    2. MultiATIS++
    3. MTOP
    4. XLU
    5. WikiLingua
    6. Common Voice
    Show full AI answer
  • CATEGORY QUERY
    What are good resources for training multilingual NLU models for voice assistants?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. datasets
    3. Rasa Open Source
    4. spaCy
    5. Google Cloud AI Platform
    6. AutoML Natural Language
    7. Vertex AI
    8. Microsoft Azure Cognitive Services
    9. Language Service
    10. Amazon Comprehend

    AI recommended 10 alternatives but never named alexa/massive. 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 alexa/massive?
    pass
    AI named alexa/massive explicitly

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

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

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

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alexa/massive — 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