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OpenMed/OpenMed-NER-GenomicDetect-BioClinical-108M-mlx
OpenMed-NER-GenomicDetect-BioClinical-108M-mlx is a token classification model from OpenMed. Use it when you need labels on individual words, such as names. It is set up for openmed. The card lists the license as apache-2.0.
This repository contains an OpenMed MLX conversion of OpenMed/OpenMed-NER-GenomicDetect-BioClinical-108M for Apple Silicon inference with OpenMed.
Downloads · 30 days
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.safetensors431 MB · 100%
From the Hugging Face model README
This repository contains an OpenMed MLX conversion of OpenMed/OpenMed-NER-GenomicDetect-BioClinical-108M for Apple Silicon inference with OpenMed.
Artifact metadata:
token-classificationbertsafetensorsOpenMed MLX token-classification backendOpenMed/OpenMed-NER-GenomicDetect-BioClinical-108Mconfig.json, id2label.json, openmed-mlx.json, MLX weights, and tokenizer assetsDownload this OpenMed MLX artifact directly from the Hub:
hf download OpenMed/OpenMed-NER-GenomicDetect-BioClinical-108M-mlx --local-dir ./OpenMed-NER-GenomicDetect-BioClinical-108M-mlx
Use the downloaded directory when you want to pin this exact MLX artifact in an offline or local Apple Silicon workflow.
pip install openmed
pip install "openmed[mlx]"
from openmed import analyze_text
from openmed.core.config import OpenMedConfig
result = analyze_text(
"Patient John Doe, DOB 1990-05-15, SSN 123-45-6789",
model_name="OpenMed/OpenMed-NER-GenomicDetect-BioClinical-108M",
config=OpenMedConfig(backend="mlx"),
)
for entity in result.entities:
print(entity.label, entity.text, round(entity.confidence, 4))
Use Swift with OpenMedKit, not with MLX weight files directly.
https://github.com/maziyarpanahi/openmedid2label.json to your app target.This MLX model is for Python services on Apple Silicon, local MLX inference on macOS, and Hub-hosted model distribution. If a given environment cannot write weights.safetensors, OpenMed falls back to weights.npz so the model remains usable.
OpenMed/OpenMed-NER-GenomicDetect-BioClinical-108M