privacy filter nemotron Fine tuned openai/privacy filter for fine grained PII extraction across 55 categories from nvidia/Nemotron PII . Base model : openai/privacy filter — 1.4B parameter MoE (50M active per token), BIOES token classification head Task : Token classification for PII detection (BIOES scheme) Training data : Full 100K rows of nvidia/Nemotron PII train split Held out val : 10K label stratified rows from the Nemotron test split (every label has ≥229 entities) Recipe : opf train (OpenAI's official fine tuning CLI) — full fine tune, AdamW, lr=1e 4, 5 epochs, bf16, weight decay 0.0 Labels : 55 fine grained PII categories → 221 BIOES classes (1 O + 55 × B/I/E/S) The base model ships with 8 coarse PII categories ( private person , private email , etc.). This model trades that coarse vocabulary for a 5× more granular one — first name , last name , medical record number , credit debit card , ssn , and so on — matching what downstream redaction and masking pipelines typically need. Family at a glance. Same architecture, three runtimes: PyTorch (this repo) — CPU + CUDA, anywhere transformers runs. MLX BF16 — OpenMed/privacy filter nemotron mlx — Apple Silicon, full precision.…
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