Token Classification
Transformers
Safetensors
English
distilbert
named-entity-recognition
biomedical-nlp
leukemia
hematology
cancer
clinical-medicine
cl
Instructions to use OpenMed/OpenMed-NER-BloodCancerDetect-TinyMed-65M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-BloodCancerDetect-TinyMed-65M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-BloodCancerDetect-TinyMed-65M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-BloodCancerDetect-TinyMed-65M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-BloodCancerDetect-TinyMed-65M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-BloodCancerDetect-TinyMed-65M
b7ced10 verified - Xet hash:
- a0528c4685f03a15a2325f0c782019c202c09f31f0a3b8ff49f34e04c24e1709
- Size of remote file:
- 130 MB
- SHA256:
- 903a33336858381538ce1e047daee288e576a1625d889c08606fd16efdca0f0b
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