Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
banking
de-identification
Instructions to use flowxai/ibandetect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/ibandetect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/ibandetect")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/ibandetect") model = AutoModelForTokenClassification.from_pretrained("flowxai/ibandetect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05686730040f7709c4bea6e96ff2b988bf763441b2f73e4c32caa030cdf2baf8
- Size of remote file:
- 598 MB
- SHA256:
- 46d5072bf628bf7d50e8301ff7702019d98d33b0cb972c4de8e92b5174c90f66
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