Token Classification
Transformers
Core ML
ONNX
Safetensors
bert
pii
pii-detection
pii-masking
redaction
privacy
on-device
distillation
Eval Results (legacy)
Instructions to use divergentlabs/masker-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use divergentlabs/masker-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="divergentlabs/masker-mini")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("divergentlabs/masker-mini") model = AutoModelForTokenClassification.from_pretrained("divergentlabs/masker-mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spm.model from divergentlabs/masker-mini: direct link, hf CLI and curl.
- Browser
- Download file 1.21 MB
-
https://huggingface.co/divergentlabs/masker-mini/resolve/v2/spm.model
- Command line
-
hf download hf://divergentlabs/masker-mini@v2/spm.model
-
curl -L -o spm.model https://huggingface.co/divergentlabs/masker-mini/resolve/v2/spm.model
1.21 MB
- Xet hash:
- 21f8be8ff64563043c24ddcc4b0eecd71120f54a1c474870154f98275a7db045
- Size of remote file:
- 1.21 MB
- SHA256:
- 3f85516c138eb26db0b15c5d54337797838320061a542ca4913fce8bc32d06b0
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