RRepoGEO

REPOGEO REPORT · LITE

microsoft/DeBERTa

Default branch master · commit 4d7fe0bd · scanned 5/13/2026, 4:52:08 AM

GitHub: 2,219 stars · 239 forks

AI VISIBILITY SCORE
35 /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
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 microsoft/DeBERTa, 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's opening paragraph to clarify DeBERTa's role as an NLU model architecture

    Why:

    CURRENT
    This repository is the official implementation of DeBERTa: Decoding-enhanced BERT with Disentangled Attention and DeBERTa V3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
    COPY-PASTE FIX
    DeBERTa (Decoding-enhanced BERT with Disentangled Attention) is a state-of-the-art transformer model architecture designed for advanced natural language understanding (NLU) tasks, offering improved accuracy and robustness over models like BERT and RoBERTa. This repository provides the official implementation of DeBERTa and DeBERTa V3, which further enhances efficiency through ELECTRA-style pre-training.
  • highhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2006.03654
  • mediumtopics#3
    Expand repository topics to include more specific NLU and related model types

    Why:

    CURRENT
    bert, deeplearning, language-model, natural-language-understanding, representation-learning, roberta, self-attention, transformer-encoder
    COPY-PASTE FIX
    bert, deeplearning, language-model, natural-language-understanding, representation-learning, roberta, self-attention, transformer-encoder, nlu, electra, pre-trained-models, state-of-the-art

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 microsoft/DeBERTa
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4 · recommended 1×
  2. Claude 3 Opus · recommended 1×
  3. Gemini 1.5 Pro · recommended 1×
  4. Llama 3 · recommended 1×
  5. Mistral Large · recommended 1×
  • CATEGORY QUERY
    Seeking an advanced transformer model for improved natural language understanding tasks.
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Gemini 1.5 Pro
    4. Llama 3
    5. Mistral Large
    6. Cohere Command R+

    AI recommended 6 alternatives but never named microsoft/DeBERTa. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need an efficient deep learning language model for better text representation.
    you: not recommended
    AI recommended (in order):
    1. BERT
    2. RoBERTa
    3. DistilBERT
    4. XLM-RoBERTa
    5. ELECTRA
    6. Sentence-BERT

    AI recommended 6 alternatives but never named microsoft/DeBERTa. 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 microsoft/DeBERTa?
    pass
    AI named microsoft/DeBERTa explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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HTML
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microsoft/DeBERTa — 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