rehuman 0.2.0

Python bindings for rehuman: Unicode-safe text cleaning & normalization

rehuman (Python)

Unicode-safe text cleaning & normalization for Python, backed by the native Rust rehuman core (PyO3 + maturin). Strip invisible characters, normalize typography, and enforce ASCII/keyboard-safe output for text sourced from web scraping, user input, or LLMs.

Install

The package is published on PyPI as rehuman:

pip install rehuman

• Requires Python 3.9+ (abi3 wheels: one wheel per platform covers every supported CPython).
• Prebuilt wheels: Linux (x86_64, aarch64), macOS (arm64), Windows (x86_64).
• Other platforms install from the sdist, which compiles the native module and needs a Rust toolchain. Wheel coverage details live in Release Automation.
• Published wheels are built with the default Rust features: rehuman.HAS_STATS is True and rehuman.HAS_SECURITY is False.

Quickstart

clean() and humanize() both return str but target different outputs: clean produces ASCII/keyboard-safe text, humanize keeps Unicode and tidies typography and whitespace (exact policy differences).

import rehuman

text = "A   B 👍 Café"
cleaned = rehuman.clean(text)       # "A   B Cafe"
humanized = rehuman.humanize(text)  # "A B 👍 Café"

assert cleaned == "A   B Cafe"
assert humanized == "A B 👍 Café"

# Use Cleaner for change counts and stats
cleaner = rehuman.Cleaner()
result = cleaner.clean("Hi\u200bthere \U0001f44d")
print(result.text)         # "Hithere"
print(result.changes_made) # e.g. 3
print(result.stats)        # dict with per-operation counters

Option-level control (non_ascii_policy, presets, line endings, ...) goes through Options and Cleaner.

Advanced usage: cleaning a text corpus

A concrete case study: normalizing model-generated text in a training corpus (SFT conversations, synthetic data, scraped documents). Such text tends to carry invisible codepoints (zero-width spaces, soft hyphens), typographic quotes/dashes/ellipses, and exotic whitespace. Normalizing it removes stylistic tells and collapses spurious token variants without touching content.

Two decisions matter up front:

• Do not use default clean() on corpora you care about. It targets ASCII/keyboard-safe output and deletes what it cannot transliterate, so non-Latin scripts vanish ("日本語テキスト hello" becomes "hello").
• Derive from the code_safe preset instead. It keeps every script, emoji, and diagram glyph, strips hidden/control characters, and normalizes typographic quotes and dashes. Use replace to adjust it:

import rehuman

OPTIONS = rehuman.Options.code_safe_preset().replace(
    normalize_other=True,        # also fold ellipsis -> "..." etc.
    unicode_normalization="nfc", # canonical form, no visual change
)

def clean_batch(batch):
    cleaner = rehuman.Cleaner(OPTIONS)  # construction is ~1 microsecond
    results = [cleaner.clean(t) for t in batch["text"]]
    return {
        "text": [r.text for r in results],
        "rehuman_changes": [r.changes_made for r in results],
    }

This shape drops straight into map-style pipelines, for example Hugging Face datasets: ds.map(clean_batch, batched=True, num_proc=8). Options and Cleaner pickle, so worker processes and cache fingerprinting work; clean releases the GIL, so thread pools scale too. Throughput is roughly a thousand few-KB documents per second per core, linear in processes.

Keeping changes_made as a column is deliberate: sort by it descending and eyeball the most-rewritten samples before training on the output.

One caveat: transforms apply to the whole string with no markdown or code awareness. A fenced code block whose literals contain typographic characters gets rewritten like any other text (text.replace("“", '"') breaks). If your corpus is code-heavy, split out fenced blocks and clean only the prose.

Documentation

• Python API Reference: functions, classes, presets, constants, and errors.
• Rust core semantics: what each option and preset actually does; the bindings map onto these one-to-one.
• Release Automation: wheel/sdist build and PyPI publish flows.

Development

Build the extension into a local venv and run the test suite:

cd python
python -m venv .venv
source .venv/bin/activate
pip install -U pip maturin pytest
maturin develop
pytest -q