vec2text

name	vec2text
description	vec2text: embedding-inversion library for reconstructing approximate text from sentence embeddings. Use when working with saved embedding tensors, privacy/inversion research, or AI/ML challenge workflows where you need to load a corrector model and invert strings or embeddings directly.
compatibility	Linux, macOS, Windows; Python 3; PyTorch and Hugging Face stack; GPU strongly recommended for larger runs
metadata	{"author":"AeonDave","version":"1.0"}

vec2text

Use vec2text when embeddings are the artifact and text recovery is the question.

When to use vec2text

Use vec2text when you need to:

• invert sentence embeddings back into approximate text
• load a pretrained corrector for supported embedding families
• work from saved embedding tensors rather than original source text
• tune inversion quality with multiple refinement steps or beam search

Quick Start

import vec2text

corrector = vec2text.load_pretrained_corrector("gtr-base")

High-Value Workflows

Invert known strings through the matching embedder workflow

vec2text.invert_strings(
    ["example text"],
    corrector=corrector,
    num_steps=20,
    sequence_beam_width=4,
)

Invert embeddings directly

import torch

embeddings = torch.load("embeddings.pt", map_location="cpu")
texts = vec2text.invert_embeddings(
    embeddings=embeddings,
    corrector=corrector,
    num_steps=20,
)
print(texts)

Practical Notes

• Corrector choice must match the embedding family; mismatched models produce garbage.
• num_steps improves refinement quality, while sequence_beam_width improves search at higher memory cost.
• The project supports both direct string inversion workflows and embedding-only inversion workflows.

Caveats

• Reconstruction is approximate, not guaranteed exact text recovery.
• Beam search raises memory usage quickly.
• Model support and pretrained aliases evolve; prefer current upstream docs over old examples.

Resources

No bundled scripts/, references/, or assets/. Use the upstream vec2text README for current pretrained corrector aliases, API entry points, and embedding-family matching guidance.