Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
cross
de-identification
Instructions to use flowxai/kybextract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/kybextract with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/kybextract")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/kybextract") model = AutoModelForTokenClassification.from_pretrained("flowxai/kybextract", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41920fd041d1a562928572f24307ebfe5be484a5a7b4fca238d2b98f782444b0
- Size of remote file:
- 5.2 kB
- SHA256:
- 14fc3d40dad693858ae144e0599884abb5215088d9695456262b2aee21d834cf
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