Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
insurance
de-identification
Instructions to use flowxai/claimextract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/claimextract with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/claimextract")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/claimextract") model = AutoModelForTokenClassification.from_pretrained("flowxai/claimextract", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d5fcfd9c355e7a20b8c2f2f97ed3aba520756782af913a4bb5d7ec6255b7d14
- Size of remote file:
- 5.2 kB
- SHA256:
- 2ba219d326191ca5d3bfecc3bf88d2030709359b079cc5295ea52ae970003d98
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