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:
- 33c51aef472812aae94bff3b0bd63472e34e3160b4a1e47441abdc712b7d435d
- Size of remote file:
- 5.2 kB
- SHA256:
- 9e37c2a499b3e46e4c2270b7c4d7ecacece26dbe469f80174b69e7ff5d4b4b4c
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