Protegrity releases 278M-parameter open-weights PII detector
Protegrity announced a 278M-parameter, open-weights PII detection model, a supporting script, the PII-SCOPE benchmark and a research paper. The developer says the RoBERTa fine-tune was trained on 1.2B synthetic text samples containing varied PII and can identify PII instances by context, returning each instance's index and type or redacting all detected PII. PII-SCOPE was designed to address common issues in public LLM-generated datasets and is available on Hugging Face.
The release provides an open-weights model and reference benchmark for identifying and redacting PII, including a benchmark designed to address issues found in public LLM-generated datasets.
Hi everyone,
I wanted to share a new open-weights PII detection model, benchmark and research paper. It's a fine-tune of RoBERTa using 1.2B samples of synthetically generated text that contain a variety of PII. The benchmark, PII-SCOPE (Synthetic Context and Ontology for PII Extraction), was built to address common issues in public LLM-generated datasets and is on Hugging Face too. The model and script identify instances of PII based on surrounding context, and then return the index and type of each instance. Alternatively, it can return the input with all PII redacted.
Technical report (research paper) https://arxiv.org/html/2610.30518v2
Hugging Face model card https://huggingface.co/Protegrity/protegrity-pii
submitted by /u/shanekj [link] [comments]
#Protegrity#Protegrity PII Detection Model#RoBERTa#PII-SCOPE#Hugging Face