Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
leukemia
hematology
cancer
clinical-medicine
disease
gene
protein
treatment
Instructions to use OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Multi-209M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Multi-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Multi-209M") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-ZeroShot-NER-BloodCancer-Multi-209M
551b876 verified Download tokenizer.json from OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Multi-209M: direct link, hf CLI and curl.
- Browser
- Download file 16.4 MB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Multi-209M/resolve/main/tokenizer.json
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Multi-209M/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Multi-209M/resolve/main/tokenizer.json
16.4 MB
- Xet hash:
- 1450c6a05ef768fc4b01489b843aceb98e293d985e922f1993b1ccd7a26609c2
- Size of remote file:
- 16.4 MB
- SHA256:
- 3d81efd0311d8d5900181604ebe2b6216b7f7f0dd1e209cf69a42bfbed21b97a
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