Token Classification
MLX
GLiNER
English
openmed
deberta-v2
apple-silicon
zero-shot-ner
medical
clinical
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Pathology-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-Pathology-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-Pathology-Multi-209M-mlx OpenMed/OpenMed-ZeroShot-NER-Pathology-Multi-209M-mlx
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Pathology-Multi-209M-mlx with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Pathology-Multi-209M-mlx") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- 3e32c89605f599259bddb0c0274bf4fd4890a1eb6fc4935f890d09817762c102
- Size of remote file:
- 1.16 GB
- SHA256:
- a8dab066fa7ab7f781b42d69426eb102aaa51b9311f42e972f26d1701a1697f6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.