Token Classification
MLX
GLiNER
English
openmed
deberta-v2
apple-silicon
zero-shot-ner
medical
clinical
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Protein-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-Protein-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-Protein-Multi-209M-mlx OpenMed/OpenMed-ZeroShot-NER-Protein-Multi-209M-mlx
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Protein-Multi-209M-mlx with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Protein-Multi-209M-mlx") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- 6fb5b5c9e9e94378ccf4e45261b4fcc4a40c5c20bbd806cc2be6439fcb7adffc
- Size of remote file:
- 1.16 GB
- SHA256:
- ed7f3359f427d2afc71267c586ec2d646dd8de89e16702e437242b6dadc8caa7
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