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 config.json from DataFog/pii-en-65m: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/DataFog/pii-en-65m/resolve/5683ef559df6618f563375b632fdab7352ac1628/config.json
- Command line
-
hf download hf://DataFog/pii-en-65m@5683ef559df6618f563375b632fdab7352ac1628/config.json
-
curl -L -o config.json https://huggingface.co/DataFog/pii-en-65m/resolve/5683ef559df6618f563375b632fdab7352ac1628/config.json
1.38 kB
| { | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForTokenClassification" | |
| ], | |
| "attention_dropout": 0.1, | |
| "bos_token_id": null, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "hidden_dim": 3072, | |
| "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" | |
| }, | |
| "initializer_range": 0.02, | |
| "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 | |
| }, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 12, | |
| "n_layers": 6, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "tie_weights_": true, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.17.0", | |
| "vocab_size": 28996 | |
| } | |