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OpenMed-NER-ProteinDetect-ElectraMed-109M-mlx

Token Classification
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openmed
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apple-silicon
pii
de-identification
medical
clinical
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metadata 
license: apache-2.0
base_model: OpenMed/OpenMed-NER-ProteinDetect-ElectraMed-109M
pipeline_tag: token-classification
library_name: openmed
tags:
  - openmed
  - mlx
  - apple-silicon
  - token-classification
  - pii
  - de-identification
  - medical
  - clinical

OpenMed-NER-ProteinDetect-ElectraMed-109M for OpenMed MLX

This repository contains an OpenMed MLX conversion of OpenMed/OpenMed-NER-ProteinDetect-ElectraMed-109M for Apple Silicon inference with OpenMed.

Artifact metadata:

  • OpenMed MLX task: token-classification
  • OpenMed MLX family: bert
  • Weight format: safetensors
  • Runtime API: OpenMed MLX token-classification backend

OpenMed is the main product experience:

  • Install the Python package with pip install openmed
  • Enable Apple Silicon acceleration with pip install "openmed[mlx]"
  • Load this MLX model directly from the Hub or from a local snapshot
  • For Apple apps, use OpenMedKit from the same GitHub repository with a compatible CoreML bundle

This MLX repo is meant to pair with:

Quick Start 

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-ProteinDetect-ElectraMed-109M",
    config=OpenMedConfig(backend="mlx"),
)

for entity in result.entities:
    print(entity.label, entity.text, round(entity.confidence, 4))

Swift and Apple Apps

Use Swift with OpenMedKit, not with MLX weight files directly.

  1. Open Xcode and go to File > Add Package Dependencies.
  2. Paste the OpenMed repository URL: https://github.com/maziyarpanahi/openmed
  3. Choose the package product OpenMedKit from the repository.
  4. Add a compatible CoreML model bundle plus id2label.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.

Credits