RRepoGEO

REPOGEO REPORT · LITE

Separius/awesome-sentence-embedding

Default branch master · commit 514c14af · scanned 6/20/2026, 9:27:41 PM

GitHub: 2,289 stars · 263 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
22 /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
1 / 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 Separius/awesome-sentence-embedding, 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 opening to clarify purpose as a resource list

    Why:

    CURRENT
    A curated list of pretrained sentence and word embedding models
    COPY-PASTE FIX
    This repository is a comprehensive, curated list of pretrained sentence and word embedding models, designed for researchers and practitioners to explore and compare various approaches. It serves as a guide to the landscape of embedding techniques, rather than a deployable library or framework.
  • mediumabout#2
    Add homepage URL to repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/Separius/awesome-sentence-embedding
  • lowreadme#3
    Strengthen 'About This Repo' section's value proposition

    Why:

    CURRENT
    * well there are some awesome-lists for word embeddings and sentence embeddings, but all of them are outdated and more importantly incomplete
    COPY-PASTE FIX
    * While other awesome-lists for word and sentence embeddings exist, many are outdated or incomplete. This repository aims to be a continuously updated and comprehensive resource, meticulously curating papers and pretrained 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
0 / 2
0% of queries surface Separius/awesome-sentence-embedding
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. UKPLab/sentence-transformers · recommended 1×
  3. RaRe-Technologies/gensim · recommended 1×
  4. tensorflow/hub · recommended 1×
  5. pytorch/hub · recommended 1×
  • CATEGORY QUERY
    I need to find a curated list of pretrained sentence and word embedding models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. sentence-transformers (UKPLab/sentence-transformers)
    3. Gensim (RaRe-Technologies/gensim)
    4. TensorFlow Hub (tensorflow/hub)
    5. PyTorch Hub (pytorch/hub)
    6. spaCy (explosion/spaCy)
    7. FastText (facebookresearch/fastText)
    8. GloVe

    AI recommended 8 alternatives but never named Separius/awesome-sentence-embedding. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good resources for exploring and comparing various contextualized language embedding models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Models Hub
    2. BERT
    3. RoBERTa
    4. DistilBERT
    5. XLNet
    6. ALBERT
    7. GPT-2
    8. GPT-3
    9. T5
    10. BART
    11. ELECTRA
    12. Hugging Face Leaderboards
    13. DeBERTaV3
    14. RoBERTa-large
    15. ELECTRA-large
    16. Papers With Code
    17. PaLM
    18. Chinchilla
    19. BLOOM
    20. GPT-3.5
    21. Llama 2
    22. NLP Progress (sebastianruder/NLP-progress)
    23. ELMo
    24. ULMFiT
    25. Distill.pub
    26. Kaggle
    27. XLM-RoBERTa

    AI recommended 27 alternatives but never named Separius/awesome-sentence-embedding. 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 Separius/awesome-sentence-embedding?
    pass
    AI did not name Separius/awesome-sentence-embedding — 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 Separius/awesome-sentence-embedding in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Separius/awesome-sentence-embedding 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 Separius/awesome-sentence-embedding solve, and who is the primary audience?
    pass
    AI did not name Separius/awesome-sentence-embedding — 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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Separius/awesome-sentence-embedding — 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