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-Multi-209M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M") - Notebooks
- Google Colab
- Kaggle
Download spm.model from OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M: direct link, hf CLI and curl.
- Browser
- Download file 4.31 MB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M/resolve/main/spm.model
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M/spm.model
-
curl -L -o spm.model https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M/resolve/main/spm.model
4.31 MB
- Xet hash:
- c4b914ec5ab295fbee3b0e0fe62f22febb3c09d34dd3778745e0de034a76df33
- Size of remote file:
- 4.31 MB
- SHA256:
- 13c8d666d62a7bc4ac8f040aab68e942c861f93303156cc28f5c7e885d86d6e3
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