RRepoGEO

REPOGEO REPORT · LITE

lucidrains/reformer-pytorch

Default branch master · commit 66a19b6c · scanned 6/23/2026, 10:01:57 PM

GitHub: 2,193 stars · 253 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)

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

AI VISIBILITY SCORE
28 /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
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 lucidrains/reformer-pytorch, 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 the README H1 to highlight core differentiators

    Why:

    CURRENT
    ## Reformer, the Efficient Transformer, in Pytorch
    COPY-PASTE FIX
    ## Reformer, the Memory-Efficient Transformer for Long Sequences in Pytorch (with LSH Attention & Reversible Networks)
  • mediumtopics#2
    Add more specific topics for better categorization

    Why:

    CURRENT
    artificial-intelligence, attention-mechanism, machine-learning, pytorch, transformers
    COPY-PASTE FIX
    artificial-intelligence, attention-mechanism, machine-learning, pytorch, transformers, memory-efficient, lsh-attention, scalable-nlp, long-sequence-modeling
  • lowhomepage#3
    Add the original paper's URL as the homepage

    Why:

    COPY-PASTE FIX
    https://openreview.net/pdf?id=rkgNKkHtvB

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 lucidrains/reformer-pytorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 4×
  2. Dao-AILab/flash-attention · recommended 2×
  3. facebookresearch/xformers · recommended 1×
  4. microsoft/DeepSpeed · recommended 1×
  5. lucidrains/performer-pytorch · recommended 1×
  • CATEGORY QUERY
    How to implement memory-efficient transformer models for very long sequences in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. FlashAttention (Dao-AILab/flash-attention)
    2. xFormers (facebookresearch/xformers)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. LongFormer / BigBird Attention (huggingface/transformers)
    5. Performer (lucidrains/performer-pytorch)
    6. Reformer (huggingface/transformers)
    7. Gradient Checkpointing (pytorch/pytorch)

    AI recommended 7 alternatives but never named lucidrains/reformer-pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a PyTorch library that implements LSH attention for scalable NLP tasks.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Longformer (huggingface/transformers)
    3. Performer
    4. LSH Attention
    5. FlashAttention (Dao-AILab/flash-attention)

    AI recommended 5 alternatives but never named lucidrains/reformer-pytorch. 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 lucidrains/reformer-pytorch?
    pass
    AI did not name lucidrains/reformer-pytorch — 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?

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

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

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lucidrains/reformer-pytorch — 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