Token Classification
Transformers
Safetensors
deberta-v2
pii
pii-detection
pii-masking
redaction
privacy
deberta-v3
Eval Results (legacy)
Instructions to use divergentlabs/masker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use divergentlabs/masker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="divergentlabs/masker")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("divergentlabs/masker") model = AutoModelForTokenClassification.from_pretrained("divergentlabs/masker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from divergentlabs/masker: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/divergentlabs/masker/resolve/main/tokenizer.json
- Command line
-
hf download hf://divergentlabs/masker/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/divergentlabs/masker/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- 4bd7635265abb38719b37695eb1dcdb66fd9e8ad8c1b788e5b9f7d8828b1ad7d
- Size of remote file:
- 16.3 MB
- SHA256:
- 8d5a786a62a72797fcd2f23663c3f8665bc81e6f10043004c66490810b3d0d82
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