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
Commit History
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Use canonical PII EN 65M name and current installation instructions
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Expand model card with measured performance, usage and native qualification
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Stage qualified native runtime 0.2.0
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Complete self-contained runtime licensing inventory and release validation (part 2)
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Complete self-contained runtime licensing inventory and release validation
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Stage DataFog PII v0.1.0: epoch-2 checkpoint, frozen MCP bundle, provenance and licenses (part 2)
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Stage DataFog PII v0.1.0: epoch-2 checkpoint, frozen MCP bundle, provenance and licenses
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initial commit
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