Modelpublic

mrfakename/ReverseBERT-EmbeddingGemma-300M

sourceHugging Facecc-by-4.0updated 9mo agoView on Hugging Face

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Model Card

An experiment, see details: https://github.com/fakerybakery/ReverseBERT. Inspired by https://github.com/vec2text/vec2text

Overview	Details
Embedding Model	https://huggingface.co/google/embeddinggemma-300m
LLM Backbone	https://huggingface.co/Qwen/Qwen3-0.6B-Base

Overview

Can you go from embeddings back to text?

The setup is pretty simple: take a sentence encoder and freeze it. Then train a small projection layer that maps those embeddings into "soft prompt" tokens for a language model. The LLM learns to reconstruct the original text from just those projected embeddings.

It's far from perfect. You probably can't reconstruct the exact meaning of the text, but you can get the general idea/vibe of the original input.

Usage

See: https://github.com/fakerybakery/ReverseBERT/blob/main/infer.py

Reconstruction samples

Coming soon

Credits

As always, huge thanks to Hugging Face 🤗 for supporting the compute used to train this model!