OpenMed-PII-Turkish-SuperClinical-Large-434M-v1 for OpenMed MLX

This repository contains an MLX packaging of OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1 for Apple Silicon inference with OpenMed.

At a Glance

• Source checkpoint: OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1
• Model family: deberta-v2 (DebertaV2ForTokenClassification)
• Primary language hint: Turkish (tr)
• Artifact layout: legacy-compatible MLX (config.json, id2label.json, MLX weight files)
• Weight format: safetensors
• Python MLX: supported through openmed[mlx] on Apple Silicon Macs

Python Quick Start

Use the standard OpenMed API if you want OpenMed to choose the right runtime automatically:

pip install "openmed[mlx]"

from openmed import extract_pii

text = "<your clinical note here>"
result = extract_pii(
 text,
 model_name="OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1",
 use_smart_merging=True,
)

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

On Apple Silicon, OpenMed can use this preconverted MLX artifact when openmed[mlx] is installed. On other systems, OpenMed falls back to the Hugging Face / PyTorch backend.

Use This Preconverted MLX Repo Directly

If you want to use this MLX snapshot explicitly, download it locally and point OpenMed at the directory:

pip install "openmed[mlx]"
hf download OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1-mlx --local-dir ./OpenMed-PII-Turkish-SuperClinical-Large-434M-v1-mlx

If this repo is private in your environment, authenticate first with hf auth login or set HF_TOKEN.

from openmed import extract_pii
from openmed.core import OpenMedConfig

text = "<your clinical note here>"
result = extract_pii(
 text,
 model_name="./OpenMed-PII-Turkish-SuperClinical-Large-434M-v1-mlx",
 config=OpenMedConfig(backend="mlx"),
 use_smart_merging=True,
)

print(result.entities)

Swift Status

This repo is based on deberta-v2. Python MLX supports this artifact today, but the current public OpenMedKit Swift MLX rollout is limited to bert, distilbert, roberta, xlm-roberta, and electra.

If you are building an Apple app today, the recommended paths for this model are:

• Python MLX for evaluation or local workflows on Apple Silicon
• CoreML in OpenMedKit if you already have a compatible bundled Apple export
• Track the current Swift support matrix in the OpenMedKit docs

Artifact Notes

This repo uses the current legacy-compatible MLX layout:

• config.json
• id2label.json
• MLX weight files (weights.safetensors and/or weights.npz)

Tokenizer assets are bundled in this repo.

Links

• Source checkpoint: OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1
• OpenMed GitHub: https://github.com/maziyarpanahi/openmed
• MLX backend docs: https://openmed.life/docs/mlx-backend/
• OpenMedKit docs: https://openmed.life/docs/swift-openmedkit/

Base model

microsoft/deberta-v3-large

OpenMed/OpenMed-PII-Turkish-SuperClinical-Large-434M-v1

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