Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
gene-recognition
genetics
genomics
molecular-biology
gene
genetic_variant
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Genomic-Small-166M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Genomic-Small-166M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Genomic-Small-166M") 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-Genomic-Small-166M: direct link, hf CLI and curl.
- Browser
- Download file 611 MB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Genomic-Small-166M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Genomic-Small-166M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Genomic-Small-166M/resolve/main/pytorch_model.bin
611 MB
- Xet hash:
- ada11207d2cd2ccfa0e1c689922cbb75417a57c205c97c191df64daabac335f2
- Size of remote file:
- 611 MB
- SHA256:
- c4cd735ca6edc6fee3d1db0c83b680c5f881034cd65e7509168107e9a7ffc5e9
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