Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
cancer-genetics
oncology
gene-regulation
cancer-research
amino_acid
anatomical_system
cancer
cell
cellular_component
developing_anatomical_structure
gene_or_gene_product
immaterial_anatomical_entity
multi-tissue_structure
organ
organism
organism_subdivision
organism_substance
pathological_formation
simple_chemical
tissue
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Oncology-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-Oncology-Base-220M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Oncology-Base-220M") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from OpenMed/OpenMed-ZeroShot-NER-Oncology-Base-220M: direct link, hf CLI and curl.
- Browser
- Download file 16.3 MB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Oncology-Base-220M/resolve/main/tokenizer.json
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Oncology-Base-220M/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Oncology-Base-220M/resolve/main/tokenizer.json
16.3 MB
- Xet hash:
- f0e808ea906f00c59d0bf5864f6a6594120a2b3997dd0ba45002a43e5e16c1b7
- Size of remote file:
- 16.3 MB
- SHA256:
- c23b87e1609c72116a5aea222f983df99723cb2afa554d9d137f289840c3097b
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