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 paramete…
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