Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
gene-recognition
protein-recognition
genomics
molecular-biology
gene
protein
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Genome-Tiny-60M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Genome-Tiny-60M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Genome-Tiny-60M") 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-Genome-Tiny-60M: direct link, hf CLI and curl.
- Browser
- Download file 689 MB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Genome-Tiny-60M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Genome-Tiny-60M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Genome-Tiny-60M/resolve/main/pytorch_model.bin
689 MB
- Xet hash:
- 5d66111754c74c408158b62c3941a29ff01a56f34fbf803c5808f3c29958433c
- Size of remote file:
- 689 MB
- SHA256:
- b7a74c44a2cd816a775723fcc49d702a9805a2418ec58d80fd5bbda673c9b4dc
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