Token Classification
MLX
GLiNER
English
openmed
deberta-v2
apple-silicon
zero-shot-ner
medical
clinical
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Genomic-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-Genomic-Multi-209M-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download OpenMed/OpenMed-ZeroShot-NER-Genomic-Multi-209M-mlx --local-dir OpenMed-ZeroShot-NER-Genomic-Multi-209M-mlx
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Genomic-Multi-209M-mlx with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Genomic-Multi-209M-mlx") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download tokenizer.json from OpenMed/OpenMed-ZeroShot-NER-Genomic-Multi-209M-mlx: direct link, hf CLI and curl.
- Browser
- Download file 16 MB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Genomic-Multi-209M-mlx/resolve/main/tokenizer.json
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Genomic-Multi-209M-mlx/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Genomic-Multi-209M-mlx/resolve/main/tokenizer.json
16 MB
- Xet hash:
- d9878a1967f8c3ed8ec9384f54b646ed92ea98f7de774bbdf10a89a27acedd8a
- Size of remote file:
- 16 MB
- SHA256:
- 4429a446347c64e61dae3f01f56cb59b4ac4afa78b72dd04cd9a28475f122202
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