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. - 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.

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