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:
- 521c8aea9e7896c3b7e05eb6564a5d7fce492d1c5c509042140fdaea14627fdc
- Size of remote file:
- 598 MB
- SHA256:
- c61bc13e7f40430f894a1db86abd989e04228d9ec9efa90c005d12bbf1c45096
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.