Token Classification
MLX
GLiNER
English
openmed
deberta-v2
apple-silicon
zero-shot-ner
medical
clinical
Instructions to use OpenMed/OpenMed-ZeroShot-NER-BloodCancer-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-BloodCancer-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-BloodCancer-Medium-209M-mlx OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Medium-209M-mlx
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Medium-209M-mlx with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Medium-209M-mlx") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- a1851b08e06115d788a716d95e7669f86a22cd45984ffccb2c0f08e0e751ebaf
- Size of remote file:
- 781 MB
- SHA256:
- 58b779a59c3b0ef6550a596e35017a96ec6f4ce593bb8fa7cc8d4c956578a671
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