RRepoGEO

REPOGEO REPORT · LITE

abertsch72/unlimiformer

Default branch main · commit e38b0149 · scanned 5/15/2026, 3:58:45 AM

GitHub: 1,065 stars · 77 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 abertsch72/unlimiformer, 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

2 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 highlight the core differentiator

    Why:

    CURRENT
    Unlimiformer is a method for augmenting pretrained encoder-decoder models with retrieval-based attention, without changing the mathematical definition of attention.
    COPY-PASTE FIX
    Unlimiformer is a novel method that augments *any existing pretrained encoder-decoder model* with retrieval-based attention, enabling unlimited length inputs *without requiring architectural changes or retraining the base model*.
  • mediumhomepage#2
    Add the NeurIPS paper URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://neurips.cc/virtual/2023/poster/70000

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 abertsch72/unlimiformer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
BigBird
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. BigBird · recommended 2×
  2. BERT · recommended 2×
  3. RoBERTa · recommended 2×
  4. LongFormer · recommended 1×
  5. LED - Longformer-Encoder-Decoder · recommended 1×
  • CATEGORY QUERY
    How can I process extremely long documents with existing transformer models effectively?
    you: not recommended
    AI recommended (in order):
    1. LongFormer
    2. LED - Longformer-Encoder-Decoder
    3. BigBird
    4. Perceiver IO
    5. H-Transformer
    6. Long-T5
    7. BERT
    8. RoBERTa
    9. BERT
    10. RoBERTa
    11. T5
    12. Reformer
    13. FlashAttention
    14. LLaMA 2
    15. Falcon
    16. Mistral

    AI recommended 16 alternatives but never named abertsch72/unlimiformer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are methods to extend the context window of large language models for better understanding?
    you: not recommended
    AI recommended (in order):
    1. GPT-3.5 Turbo
    2. Llama 2
    3. YaRN
    4. ALiBi
    5. BLOOM
    6. LangChain
    7. LlamaIndex
    8. Longformer
    9. BigBird
    10. Transformer-XL
    11. Differentiable Neural Computers

    AI recommended 11 alternatives but never named abertsch72/unlimiformer. 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 abertsch72/unlimiformer?
    pass
    AI named abertsch72/unlimiformer explicitly

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

  • If a team adopts abertsch72/unlimiformer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named abertsch72/unlimiformer 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 abertsch72/unlimiformer solve, and who is the primary audience?
    pass
    AI named abertsch72/unlimiformer explicitly

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

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abertsch72/unlimiformer — 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