Source checkpoint: OpenMed/OpenMed-PII-Telugu-BiomedELECTRA-Large-335M-v1
Model family: bert (BertForTokenClassification)
Primary language hint: Telugu (te)
Artifact layout: legacy-compatible MLX (config.json, id2label.json, MLX weight files)
Python MLX: supported through openmed[mlx] on Apple Silicon Macs
Swift MLX: supported today in OpenMedKit on Apple Silicon macOS and real iPhone/iPad hardware

Python Quick Start

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

pip install "openmed[mlx]"

python

from openmed import extract_pii

text = "<your Telugu clinical note here>"
result = extract_pii(
    text,
    model_name="OpenMed/OpenMed-PII-Telugu-BiomedELECTRA-Large-335M-v1",
    lang="te",
    use_smart_merging=True,
)

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

On Apple Silicon, OpenMed auto-selects MLX when openmed[mlx] is installed. On other systems it 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:

bash

pip install "openmed[mlx]"
hf download OpenMed/OpenMed-PII-Telugu-BiomedELECTRA-Large-335M-v1-mlx --local-dir ./OpenMed-PII-Telugu-BiomedELECTRA-Large-335M-v1-mlx

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

python

from openmed import extract_pii
from openmed.core import OpenMedConfig

text = "<your Telugu clinical note here>"
result = extract_pii(
    text,
    model_name="./OpenMed-PII-Telugu-BiomedELECTRA-Large-335M-v1-mlx",
    config=OpenMedConfig(backend="mlx"),
    lang="te",
    use_smart_merging=True,
)

print(result.entities)

Swift Quick Start with OpenMedKit

OpenMedKit can download and run this MLX repo directly.

swift

import OpenMedKit

let modelDirectory = try await OpenMedModelStore.downloadMLXModel(
    repoID: "OpenMed/OpenMed-PII-Telugu-BiomedELECTRA-Large-335M-v1-mlx",
    authToken: "<hugging-face-token-while-private>"
)

let openmed = try OpenMed(
    backend: .mlx(modelDirectoryURL: modelDirectory)
)

let entities = try openmed.extractPII(
    "<your Telugu clinical note here>"
)

Notes:

Swift MLX targets Apple Silicon macOS and physical iPhone/iPad hardware.
iOS Simulator is not a Swift MLX target.
If you already have a bundled .mlmodelc or .mlpackage, use the CoreML backend in OpenMedKit instead.

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 not bundled in this repo layout. OpenMed and OpenMedKit keep backward compatibility by falling back to the source tokenizer reference in config.json when needed.

OpenMed/OpenMed-PII-Telugu-BiomedELECTRA-Large-335M-v1-mlx

OpenMed-PII-Telugu-BiomedELECTRA-Large-335M-v1 for OpenMed MLX

At a Glance

Python Quick Start

Use This Preconverted MLX Repo Directly

Swift Quick Start with OpenMedKit

Artifact Notes

Links

OpenMed/
OpenMed-PII-Telugu-BiomedELECTRA-Large-335M-v1-mlx