OpenAI Privacy Filter

OpenAI Privacy Filter is a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text. It is intended for high-throughput data sanitization workflows where teams need a model that they can run on-premises that is fast, context-aware, and tunable.

OpenAI Privacy Filter is pretrained autoregressively to arrive at a checkpoint with similar architecture to gpt-oss, albeit of a smaller size. We then converted that checkpoint into a bidirectional token classifier over a privacy label taxonomy, and post-trained with a supervised classification loss. (For architecture details about gpt-oss, please see the gpt-oss model card.) Instead of generating text token-by-token, this model labels an input sequence in a single forward pass, then decodes coherent spans with a constrained Viterbi procedure. For each input token, the model predicts a probability distribution over the label taxonomy which consists of 8 output categories described below.

Highlights:

• Permissive Apache 2.0 license: ideal for experimentation, customization, and commercial deployment.
• Small size: Runs in a web browser or on a laptop – 1.5B parameters total and 50M active parameters.
• Fine-tunable: Adapt the model to specific data distributions through easy and data efficient finetuning.
• Long-context: 128,000-token context window enables processing long text with high throughput and no chunking.
• Runtime control: configure precision/recall tradeoffs and detected span lengths through preset operating points.

This Repo

This repository contains the local code, CLI, and example assets used to run, evaluate, and finetune Privacy Filter checkpoints. It is meant for teams that want to inspect the implementation directly and operate the model in their own environment.

Repository resources: License and Security Policy.

How To Use

1. Install the package locally:

pip install -e .

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