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: 982 Bytes
aedc4c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | {
"id2label": {
"0": "O",
"1": "B-first_name",
"2": "I-first_name",
"3": "B-last_name",
"4": "I-last_name",
"5": "B-email",
"6": "I-email",
"7": "B-phone_number",
"8": "I-phone_number",
"9": "B-street_address",
"10": "I-street_address",
"11": "B-customer_id",
"12": "I-customer_id",
"13": "B-account_number",
"14": "I-account_number"
},
"label2id": {
"B-account_number": 13,
"B-customer_id": 11,
"B-email": 5,
"B-first_name": 1,
"B-last_name": 3,
"B-phone_number": 7,
"B-street_address": 9,
"I-account_number": 14,
"I-customer_id": 12,
"I-email": 6,
"I-first_name": 2,
"I-last_name": 4,
"I-phone_number": 8,
"I-street_address": 10,
"O": 0
},
"mcp": {
"first_name": "PERSON",
"last_name": "PERSON",
"street_address": "STREET_ADDRESS"
},
"other_model_labels": "Ignored by MCP model adapter; existing Core detectors remain responsible."
}
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