Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
banking
de-identification
Instructions to use flowxai/sanctionscreen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/sanctionscreen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/sanctionscreen")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/sanctionscreen") model = AutoModelForTokenClassification.from_pretrained("flowxai/sanctionscreen", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 524822e27cef55e33639d823fd19d3ac8027a98a531c83fddaddacd95f1ebaa9
- Size of remote file:
- 2.62 MB
- SHA256:
- 506c604382d07b9937f2df1ef4b80af5f3cd0cf1313d6d1491c35da28d15cf16
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