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-Medium-209M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Protein-Medium-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Protein-Medium-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
Download pytorch_model.bin from OpenMed/OpenMed-ZeroShot-NER-Protein-Medium-209M: direct link, hf CLI and curl.
- Browser
- Download file 781 MB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Protein-Medium-209M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Protein-Medium-209M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Protein-Medium-209M/resolve/main/pytorch_model.bin
781 MB
- Xet hash:
- 96083293a2bc37799fd8cce3bed89874fd1446afe4c67966abdfe8d16fde6d97
- Size of remote file:
- 781 MB
- SHA256:
- 2fb74043137a9a24a11a152149a5f842d87d6e9adfe3405545cc645ba57436cc
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