Examples & Copy/Paste Recipes¶

This page curates the most useful samples already in the repository so you can jump straight to runnable notebooks or scripts.

Notebooks (`examples/notebooks/`)¶

Notebook	Highlights
`getting_started.ipynb`	Mirrors the Quick Start guide with step-by-step installation, registry exploration, and a first call to `analyze_text`.
`Sentence_Detection_Batching.ipynb`	Demonstrates pySBD-based segmentation, batching, and how to align predictions back to the original paragraphs.
`ZeroShot_NER_Tour.ipynb`	Walks through GLiNER indexing, domain defaults, inference API usage, and the adapter that converts spans into BIO/BILOU schemes.

Run them with VS Code, Jupyter, or Google Colab—each relies on the same uv pip install ".[hf]" baseline.

Scripts & tools¶

Path	What it does
`examples/pii_model_comparison.py`	Compares multiple PII models across shared sample text and summarizes extraction quality.
`examples/pii_multilingual_new_languages.py`	Exercises Dutch, Hindi, Telugu, and Portuguese registry entries, locale-specific regex matches, and optional live extraction with the new public checkpoints.
`scripts/smoke_gliner.py`	Runs a bounded set of GLiNER models/texts to confirm zero-shot dependencies are installed before releasing.
`tests/run-tests.sh`	Convenience runner that stitches together unit, integration, and smoke tests; extend it to include docs builds and API smoke checks.

Apple Silicon & Swift recipes¶

OpenMed 1.0.0 adds two release-critical Apple entry points:

MLX Backend for Python on Apple Silicon Macs
OpenMedKit (Swift Package) for macOS, iOS, and iPadOS apps

Python MLX quick check:

uv pip install ".[mlx]"
uv run python -c "from openmed.core.backends import get_backend; print(type(get_backend()).__name__)"

Swift MLX quick check:

import OpenMedKit

let modelDirectory = try await OpenMedModelStore.downloadMLXModel(
    repoID: "OpenMed/OpenMed-PII-LiteClinical-Small-66M-v1-mlx",
    authToken: "<token-if-private>"
)

let openmed = try OpenMed(backend: .mlx(modelDirectoryURL: modelDirectory))
let entities = try openmed.extractPII("Patient John Doe, DOB 1990-05-15")

Copy-ready snippets¶

You can find these directly in the docs:

Analyze Text Helper — dict/JSON/HTML/CSV outputs with metadata.
REST Service (MVP) — FastAPI endpoints and Docker runbook.
MLX Backend — Apple Silicon Python runtime and artifact packaging.
OpenMedKit (Swift) — native app runtime for macOS, iOS, and iPadOS.
ModelLoader & Pipelines — caching, token helpers, multi-model setups.
Advanced NER & Output Formatting — span filtering and conversions.
Zero-shot Toolkit — indexing, label defaults, and inference APIs.

Sample automation pipeline¶

#!/usr/bin/env bash
set -euo pipefail

uv pip install ".[hf,docs]"
python examples/pii_model_comparison.py > artifacts/result.txt
uv run mkdocs build --strict
python scripts/smoke_gliner.py --limit 1 --threshold 0.5

Use this pattern in CI to guarantee models, docs, and zero-shot flows stay healthy before publishing.