Token Classification
Transformers
ONNX
Safetensors
English
distilbert
pii
privacy
experimental
datafog
Instructions to use DataFog/pii-en-65m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataFog/pii-en-65m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="DataFog/pii-en-65m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DataFog/pii-en-65m") model = AutoModelForTokenClassification.from_pretrained("DataFog/pii-en-65m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download NOTICE from DataFog/pii-en-65m: direct link, hf CLI and curl.
- Browser
- Download file 709 Bytes
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https://huggingface.co/DataFog/pii-en-65m/resolve/5683ef559df6618f563375b632fdab7352ac1628/NOTICE
- Command line
-
hf download hf://DataFog/pii-en-65m@5683ef559df6618f563375b632fdab7352ac1628/NOTICE
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curl -L -o NOTICE https://huggingface.co/DataFog/pii-en-65m/resolve/5683ef559df6618f563375b632fdab7352ac1628/NOTICE
709 Bytes
| DataFog PII v0.1.0 — Copyright 2026 DataFog contributors. | |
| Derived from distilbert/distilbert-base-cased (Apache-2.0), revision 6ea81172465e8b0ad3fddeed32b986cdcdcffcf0. | |
| Fine-tuned with NVIDIA Nemotron-PII (2025), Amy Steier, Andre Manoel, Alexa Haushalter, Maarten Van Segbroeck. CC BY 4.0. | |
| https://huggingface.co/datasets/nvidia/Nemotron-PII | |
| https://creativecommons.org/licenses/by/4.0/ | |
| DataFog modifications: structural filtering, seven-label projection, grouped partitions, DistilBERT fine-tuning, windowing, span decoding and pooled calibration. This attribution does not imply NVIDIA endorsement. | |
| ONNX Runtime and Rust dependencies retain their respective notices under licenses/ and bundle/licenses/. | |