Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
protein-recognition
gene-recognition
molecular-biology
genomics
protein
dna
rna
cell_line
cell_type
Instructions to use OpenMed/OpenMed-ZeroShot-NER-DNA-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-DNA-Multi-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-DNA-Multi-209M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31174ca597a629a5de33473ae5ba20cf34f1dd5f8af8b28f4a470f442d888157
- Size of remote file:
- 1.16 GB
- SHA256:
- 79451f7d5e74ae71e11447ff1c96d46736b5e31c2c307edcab8d937db45cc54a
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