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:
- bf2364e7e4cc0501a97e644f6a592d0958c52910af447e49f52ae540998e4e2f
- Size of remote file:
- 598 MB
- SHA256:
- cc2f520b35aae884371ef8519a8f576400816ae8dbf547faa313ddba05a2e852
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.