RRepoGEO

REPOGEO REPORT · LITE

vec2text/vec2text

Default branch master · commit abe48a56 · scanned 5/18/2026, 11:48:43 PM

GitHub: 1,106 stars · 117 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
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 vec2text/vec2text, 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's opening paragraph to highlight unique value

    Why:

    CURRENT
    # vec2text

This library contains code for doing text embedding inversion. We can train various architectures that reconstruct text sequences from embeddings as well as run pre-trained models. This repository contains code for the papers "Text Embeddings Reveal (Almost) As Much As Text" and "Language Model Inversion".
    COPY-PASTE FIX
    # vec2text: The specialized library for text embedding inversion and reconstruction

`vec2text` provides specialized utilities and models to reconstruct human-readable text sequences directly from deep representations like sentence embeddings. Unlike general-purpose LLMs or NLP frameworks, `vec2text` focuses specifically on the challenging task of inverting embeddings back to their original text, enabling interpretation and analysis of vector spaces. This repository contains code for the papers "Text Embeddings Reveal (Almost) As Much As Text" and "Language Model Inversion".
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    text-embedding-inversion, embedding-reconstruction, nlp, deep-learning, sentence-embeddings, text-generation, vector-embeddings, machine-learning
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License

This project is licensed under [Specify License(s) Here, e.g., a custom license, or a combination of licenses like Apache 2.0 and MIT]. Please refer to the `LICENSE` file for full details.

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 vec2text/vec2text
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
T5
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. T5 · recommended 2×
  2. BART · recommended 2×
  3. Hugging Face Transformers · recommended 1×
  4. Pegasus · recommended 1×
  5. BERT · recommended 1×
  • CATEGORY QUERY
    How to reconstruct original text from a deep learning vector embedding?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. T5
    3. BART
    4. Pegasus
    5. BERT
    6. OpenNMT
    7. Fairseq
    8. TextGAN
    9. SeqGAN
    10. LeakGAN
    11. MaoGAN
    12. PyTorch-GAN
    13. Keras-GAN
    14. TensorFlow Probability
    15. PyTorch Lightning
    16. Pyro
    17. TextVAE
    18. Faiss
    19. Annoy
    20. scikit-learn
    21. OpenAI API
    22. GPT-3
    23. GPT-4
    24. Cohere API

    AI recommended 24 alternatives but never named vec2text/vec2text. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools can help decode sentence embeddings back into their source text?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-3 / GPT-4
    2. Claude
    3. Llama 2
    4. Hugging Face Transformers (huggingface/transformers)
    5. T5
    6. BART
    7. GPT-2
    8. Fairseq (facebookresearch/fairseq)
    9. OpenNMT (OpenNMT/OpenNMT-py)
    10. Keras (keras-team/keras)
    11. PyTorch (pytorch/pytorch)
    12. Faiss (facebookresearch/faiss)
    13. Annoy (spotify/annoy)
    14. Text-to-Text Transfer Transformer (T5)

    AI recommended 14 alternatives but never named vec2text/vec2text. 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 vec2text/vec2text?
    pass
    AI named vec2text/vec2text explicitly

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

  • If a team adopts vec2text/vec2text in production, what risks or prerequisites should they evaluate first?
    pass
    AI named vec2text/vec2text 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 vec2text/vec2text solve, and who is the primary audience?
    pass
    AI named vec2text/vec2text explicitly

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

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vec2text/vec2text — 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