RRepoGEO

REPOGEO REPORT · LITE

vec2text/vec2text

Default branch master · commit abe48a56 · scanned 6/30/2026, 6:58:45 AM

GitHub: 1,124 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

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 to clarify its unique purpose

    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: Invert Deep Representations Back to Text

This specialized library provides tools for **inverting deep representations (like sentence embeddings) back into their original text**. Unlike general text generation models or embedding tools, vec2text uniquely focuses on reconstructing human-readable text from numerical vector embeddings, offering a crucial tool for interpreting and analyzing deep representations. This repository contains code for the papers "Text Embeddings Reveal (Almost) As Much As Text" and "Language Model Inversion."
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the URL for the Colab Demo mentioned in the README, or a dedicated project website.

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
GPT-3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-3 · recommended 1×
  2. GPT-4 · recommended 1×
  3. Llama 2 · recommended 1×
  4. Mistral · recommended 1×
  5. Sentence-BERT (SBERT) · recommended 1×
  • CATEGORY QUERY
    How to reconstruct original text from a given sentence embedding vector?
    you: not recommended
    AI recommended (in order):
    1. GPT-3
    2. GPT-4
    3. Llama 2
    4. Mistral
    5. Sentence-BERT (SBERT)
    6. Universal Sentence Encoder (USE)
    7. all-MiniLM-L6-v2
    8. paraphrase-mpnet-base-v2
    9. Faiss
    10. Annoy
    11. Variational Autoencoders (VAEs)
    12. Denoising Autoencoders (DAEs)
    13. T5
    14. BART
    15. Word2Vec
    16. GloVe
    17. FastText

    AI recommended 17 alternatives but never named vec2text/vec2text. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools can help decode semantic vector representations into natural language?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch
    3. TensorFlow
    4. OpenNMT
    5. fairseq
    6. Keras

    AI recommended 6 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?

Embed your GEO score

Drop this badge into the README of vec2text/vec2text. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/vec2text/vec2text.svg)](https://repogeo.com/en/r/vec2text/vec2text)
HTML
<a href="https://repogeo.com/en/r/vec2text/vec2text"><img src="https://repogeo.com/badge/vec2text/vec2text.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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