Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
leukemia
hematology
cancer
clinical-medicine
cl
Instructions to use OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-33M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-33M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-33M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-33M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-BloodCancerDetect-ElectraMed-33M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-BloodCancerDetect-ElectraMed-33M
ddbfa9d verified - Xet hash:
- e9216b12bad983b6184a57c6a62914269b69dbadb0cfbbd1b01c772524a4943f
- Size of remote file:
- 66.4 MB
- SHA256:
- 5aaa973bd144cb3b483bfdffa6eb113d7b83f2a033be2316192adb7c026bb4a8
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