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 provenance/pyproject.toml from DataFog/pii-en-65m: direct link, hf CLI and curl.
- Browser
- Download file 347 Bytes
-
https://huggingface.co/DataFog/pii-en-65m/resolve/main/provenance/pyproject.toml
- Command line
-
hf download hf://DataFog/pii-en-65m/provenance/pyproject.toml
-
curl -L -o pyproject.toml https://huggingface.co/DataFog/pii-en-65m/resolve/main/provenance/pyproject.toml
347 Bytes
| [project] | |
| name = "datafog-pii" | |
| version = "0.0.0" | |
| description = "Local environment for the DataFog PII prototype" | |
| requires-python = ">=3.12,<3.13" | |
| dependencies = [ | |
| "torch", | |
| "transformers", | |
| "datasets", | |
| "accelerate", | |
| "safetensors", | |
| "scikit-learn", | |
| "onnx", | |
| "onnxscript", | |
| "onnxruntime", | |
| ] | |
| [tool.uv] | |
| package = false | |