---
license: apache-2.0
base_model: OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-434M
pipeline_tag: token-classification
library_name: openmed
tags:
  - openmed
  - mlx
  - apple-silicon
  - token-classification
  - pii
  - de-identification
  - medical
  - clinical
---

# OpenMed-NER-ChemicalDetect-SuperClinical-434M for OpenMed MLX

This repository contains an OpenMed MLX conversion of [`OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-434M`](https://huggingface.co/OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-434M) for Apple Silicon inference with [OpenMed](https://github.com/maziyarpanahi/openmed).

Artifact metadata:

- OpenMed MLX task: `token-classification`
- OpenMed MLX family: `deberta-v2`
- Weight format: `safetensors`
- Runtime API: `OpenMed MLX token-classification backend`

[OpenMed](https://github.com/maziyarpanahi/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:

- OpenMed GitHub: [https://github.com/maziyarpanahi/openmed](https://github.com/maziyarpanahi/openmed)
- OpenMed website: [https://openmed.life](https://openmed.life)
- Source checkpoint: [`OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-434M`](https://huggingface.co/OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-434M)

## Quick Start

```bash
pip install openmed
pip install "openmed[mlx]"
```

```python
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-ChemicalDetect-SuperClinical-434M",
    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

- Base checkpoint: [`OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-434M`](https://huggingface.co/OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-434M)
- OpenMed GitHub: [https://github.com/maziyarpanahi/openmed](https://github.com/maziyarpanahi/openmed)
- OpenMed website: [https://openmed.life](https://openmed.life)
- MLX conversion and runtime support: OpenMed
- Swift runtime for Apple apps: OpenMedKit from the OpenMed repository