google-deepmind/synthid-text

Health v2 · maintenance only

Flags: prerelease_only

Adoption not part of the score

1173 stars · 110 forks observed · 2026-10-15

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A reference Python implementation of Google DeepMind's SynthID Text watermarking and detection technology for identifying AI-generated text. It extends Hugging Face Transformers models like Gemma and GPT-2 with watermarking mixins and provides both weighted mean and Bayesian detectors.

Use cases

• watermark text generated by LLMs like Gemma or GPT-2
• detect whether text was generated by an AI model
• reproduce results from the SynthID Text research paper
• experiment with LLM watermarking techniques in PyTorch
• train a Bayesian detector for watermark detection
• integrate watermarking into Hugging Face Transformers pipelines

When to choose

• you need a reference implementation for research or reproducibility of the SynthID Text paper
• you want to experiment with watermarking and detection on Gemma or GPT-2 models
• you are exploring AI-generated text detection techniques in Python

When to avoid

• you need a production-ready watermarking solution (use the official SynthID Text implementation in Hugging Face Transformers instead)
• you require cryptographic security guarantees for the hashing functions used
• you are not working with PyTorch or Hugging Face Transformers

Facets

library · maturity experimental

nlp machine-learning security sdk large-language-models artificial-intelligence security python watermarking ai-generated-text-detection synthid huggingface-transformers pytorch llm-watermark text-detection deepmind natural-language-processing research gpu

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markdown · JSON · MCP: product_card(name="google-deepmind/synthid-text")