Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
cross
de-identification
Instructions to use flowxai/cvner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/cvner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/cvner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/cvner") model = AutoModelForTokenClassification.from_pretrained("flowxai/cvner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4cd4ef73b76cc15aae0840f32ada84dfa43fe160bd29f3af5557376b6d9d963
- Size of remote file:
- 598 MB
- SHA256:
- 14bc67b472bbf33cff4b911f63def0d529502849bea03f19064691d82b119ac1
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