Token Classification
Transformers
Safetensors
English
bert
ner
clinical
medical
healthcare
biomedical
pubmedbert
named-entity-recognition
diagnosis
icd-10
icd-coding
snomed
sapbert
entity-linking
disease
ehr
hipaa
Eval Results (legacy)
Instructions to use genzeonplatform/healthcare-brain-diagnosis-icd-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use genzeonplatform/healthcare-brain-diagnosis-icd-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="genzeonplatform/healthcare-brain-diagnosis-icd-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("genzeonplatform/healthcare-brain-diagnosis-icd-ner") model = AutoModelForTokenClassification.from_pretrained("genzeonplatform/healthcare-brain-diagnosis-icd-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f54b2da11098167741506d4d2076c45bce312f494ef736db77d0f55abcb346b7
- Size of remote file:
- 436 MB
- SHA256:
- f578f2462adb82f96e315bf30fd6d48a1688a10afbc742706abdc961c1637831
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