Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
protein-interactions
molecular-biology
biochemistry
systems-biology
protein
protein_complex
protein_family
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Protein-Base-220M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Protein-Base-220M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Protein-Base-220M") 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
Download spiece.model from OpenMed/OpenMed-ZeroShot-NER-Protein-Base-220M: direct link, hf CLI and curl.
- Browser
- Download file 4.31 MB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Protein-Base-220M/resolve/main/spiece.model
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Protein-Base-220M/spiece.model
-
curl -L -o spiece.model https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Protein-Base-220M/resolve/main/spiece.model
4.31 MB
- Xet hash:
- c002d43daac2cff8ed238836274d14498980e85c24cfd4076159b27131e91e77
- Size of remote file:
- 4.31 MB
- SHA256:
- ef78f86560d809067d12bac6c09f19a462cb3af3f54d2b8acbba26e1433125d6
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