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
File size: 709 Bytes
aedc4c6 | 1 2 3 4 5 6 7 8 | 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/.
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