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")# pip install -U transformers accelerate # 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
Download model.safetensors from flowxai/sanctionscreen: direct link, hf CLI and curl.
- Browser
- Download file 598 MB
-
https://huggingface.co/flowxai/sanctionscreen/resolve/main/model.safetensors
- Command line
-
hf download hf://flowxai/sanctionscreen/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/flowxai/sanctionscreen/resolve/main/model.safetensors
598 MB
- Xet hash:
- 4b07e8490f02c73be3b71501cb8339f2450d7d96f9f9fa5026ebb6ddb00737a0
- Size of remote file:
- 598 MB
- SHA256:
- a3877235586fdd18d56f7abdaa66a66372aa45cb9d3fce592b8c51d92f02506d
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