GGUF conversion of OpenMed/privacy-filter-nemotron, a fine-grained PII token-classification model — a fine-tune of openai/privacy-filter on the nvidia/Nemotron-PII dataset. It labels every token with a BIOES tag over 55 PII categories (221 classes) in a single forward pass, then decodes coherent spans with a constrained Viterbi procedure — so it can be served locally with no Python as the encoder/NER tier of a PII redactor. Where the base openai/privacy-filter covers 8 coarse categories, this fine-tune trades multilingual breadth for category depth: 55 fine-grained English categories (first/last name, government IDs, financial, healthcare, vehicle, digital, …). For the full model…
Open weights
apache-2.0
gguf
GGUF conversion of OpenMed/privacy-filter-multilingual, a multilingual PII token-classification model (a fine-tune of openai/privacy-filter). It labels every token with a BIOES tag over 54 PII categories (217 classes) across 16 languages, so it can be served locally with no Python as the encoder/NER tier of a PII redactor. For the full model description, label space, evaluation, limitations, and citations, see the source model card — this card only covers the GGUF packaging and how to run it. This GGUF uses a custom architecture, openai-privacy-filter, that is not (yet) part of 1. privacy-filter.cpp (recommended) — a small standalone GGML engine for exactly this model family, on stock…
Open weights
apache-2.0
gguf
