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 pytorch_model.bin from OpenMed/OpenMed-ZeroShot-NER-Oncology-Base-220M: direct link, hf CLI and curl.
- Browser
- Download file 1.21 GB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Oncology-Base-220M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Oncology-Base-220M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Oncology-Base-220M/resolve/main/pytorch_model.bin
1.21 GB
- Xet hash:
- 464c607dabed751b222b656b884cfe127a88696f6f626cb7882e37433bc05111
- Size of remote file:
- 1.21 GB
- SHA256:
- 54c9159f647e50e3f969a6b4f27fe58b8bc72bc7f93d81d6419234bf1801f330
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.