RRepoGEO

REPOGEO REPORT · LITE

abertsch72/unlimiformer

Default branch main · commit e38b0149 · scanned 6/25/2026, 7:33:10 PM

GitHub: 1,063 stars · 77 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)

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

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

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
    long-range-transformers, nlp, large-language-models, llm, context-window, retrieval-augmented-generation, neurips-2023, attention-mechanisms
  • mediumhomepage#2
    Set the repository homepage URL to the paper link

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2305.14721
  • lowreadme#3
    Clarify the problem statement in the README's opening paragraph

    Why:

    CURRENT
    Unlimiformer is a method for augmenting pretrained encoder-decoder models with retrieval-based attention, without changing the mathematical definition of attention. This allows the use of unlimited length inputs with any pretrained encoder-decoder!
    COPY-PASTE FIX
    Unlimiformer solves the critical problem of limited context windows in existing Transformer models. It is a method for augmenting pretrained encoder-decoder models with retrieval-based attention, enabling the use of unlimited length inputs with any pretrained encoder-decoder without changing their mathematical definition.

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. FlashAttention · recommended 2×
  3. LongT5 · recommended 1×
  4. Longformer · recommended 1×
  5. Performer · recommended 1×
  • CATEGORY QUERY
    How can I process extremely long text inputs with existing transformer models efficiently?
    you: not recommended
    AI recommended (in order):
    1. LongT5
    2. Longformer
    3. BigBird
    4. Performer
    5. Hugging Face Transformers
    6. LangChain
    7. LlamaIndex
    8. Haystack
    9. Transformer-XL
    10. Compressive Transformer
    11. FlashAttention

    AI recommended 11 alternatives but never named abertsch72/unlimiformer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What methods extend transformer context windows for models like large language models?
    you: not recommended
    AI recommended (in order):
    1. LongFormer
    2. Reformer
    3. BigBird
    4. Perceiver IO
    5. FlashAttention
    6. xFormers
    7. RoPE

    AI recommended 7 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?

Embed your GEO score

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MARKDOWN (README)
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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