RRepoGEO

REPOGEO REPORT · LITE

aub-mind/arabert

Default branch master · commit 6fcebaeb · scanned 6/14/2026, 6:41:41 PM

GitHub: 723 stars · 148 forks

AI VISIBILITY SCORE
62 /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
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 aub-mind/arabert, 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 README H1 to highlight core capabilities

    Why:

    CURRENT
    # AraBERTv2 / AraGPT2 / AraELECTRA
    COPY-PASTE FIX
    # AraBERT: State-of-the-Art Pre-trained Transformer Models for Arabic Language Understanding and Generation
  • highlicense#2
    Add a LICENSE file and reference it in the README

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the Apache-2.0 license text. Then, add a '## License' section to the README with the text: 'This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.'
  • mediumtopics#3
    Add specific topics for text classification and generation

    Why:

    CURRENT
    arabert, arabic, arabic-classification, arabic-nlp, bert, electra, farasa, gpt2, huggingface-transformer
    COPY-PASTE FIX
    arabert, arabic, arabic-classification, arabic-nlp, bert, electra, farasa, gpt2, huggingface-transformer, arabic-text-classification, arabic-text-generation, 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 aub-mind/arabert
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
9%
Of all named tools, what % are you?
Top rival
MARBERT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MARBERT · recommended 1×
  2. Arb-BERT · recommended 1×
  3. CAMeLBERT · recommended 1×
  4. XLMRoberta · recommended 1×
  5. mBERT · recommended 1×
  • CATEGORY QUERY
    What are robust pre-trained transformer models available for Arabic natural language processing?
    you: #1
    AI recommended (in order):
    1. AraBERT ← you
    2. MARBERT
    3. Arb-BERT
    4. CAMeLBERT
    5. XLMRoberta
    6. mBERT
    Show full AI answer
  • CATEGORY QUERY
    Seeking a library to perform advanced text classification and generation for Arabic language data.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. FastText
    3. Keras/TensorFlow
    4. PyTorch
    5. NLTK

    AI recommended 5 alternatives but never named aub-mind/arabert. 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 aub-mind/arabert?
    pass
    AI named aub-mind/arabert explicitly

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

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