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:
- 8a9844395a2ff582ab702b69d61756c409b70b27c4385409afc58c2fda322cd6
- Size of remote file:
- 598 MB
- SHA256:
- 0ebca8015fcd5ad200f108e0ea044a0326176c29c3110992d1e2395a8b43bd5e
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