Token Classification
MLX
GLiNER
English
openmed
deberta-v2
apple-silicon
zero-shot-ner
medical
clinical
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-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-Multi-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-Multi-209M-mlx OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M-mlx
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M-mlx with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M-mlx") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- d382d3df6e8a2c861fa5ceca6a2a072f695decbbd768bac17b1a1f20bcea1951
- Size of remote file:
- 1.16 GB
- SHA256:
- 7bccb36c067aecec8d73a34881cd050e0507c9578cd615bf6a317b109ec1375e
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