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 tokenizer.json from OpenMed/OpenMed-ZeroShot-NER-Protein-Base-220M: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Protein-Base-220M/resolve/main/tokenizer.json
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Protein-Base-220M/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Protein-Base-220M/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- f0e808ea906f00c59d0bf5864f6a6594120a2b3997dd0ba45002a43e5e16c1b7
- Size of remote file:
- 16.3 MB
- SHA256:
- c23b87e1609c72116a5aea222f983df99723cb2afa554d9d137f289840c3097b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.