Token Classification
MLX
GLiNER
English
openmed
deberta-v2
apple-silicon
zero-shot-ner
medical
clinical
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Oncology-Medium-209M-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenMed/OpenMed-ZeroShot-NER-Oncology-Medium-209M-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir OpenMed-ZeroShot-NER-Oncology-Medium-209M-mlx OpenMed/OpenMed-ZeroShot-NER-Oncology-Medium-209M-mlx
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Oncology-Medium-209M-mlx with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Oncology-Medium-209M-mlx") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- 283b85c1916997be8ffb962b78c144162864e34057545fdab8423ac64c5d7304
- Size of remote file:
- 781 MB
- SHA256:
- 93152830a06b56e45b278712f3e186a188ee04b629f1b2b1ff9e5ae81b585d52
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