Token Classification
Transformers
ONNX
Safetensors
modernbert
ner
on-device
privacy
flowx
openner
insurance
de-identification
Instructions to use flowxai/medclaimner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowxai/medclaimner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="flowxai/medclaimner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("flowxai/medclaimner") model = AutoModelForTokenClassification.from_pretrained("flowxai/medclaimner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d39c7a67a27889b097f0cc18a52706b53ec2a8e35cdc6685e816158b0b5f399
- Size of remote file:
- 5.2 kB
- SHA256:
- 0f26f6aa5a23319ec4bf955a6f5586acda9fbc8f993fb46c2fd0170f5edf0aca
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