papagan 0.1.5

Fast language detection for Python powered by Rust

papagan

Fast language detection for Python, powered by Rust (via PyO3 + maturin).

10 languages bundled, weighted per-word output, fully typed (PEP 561).

Install

pip install papagan

Pre-built wheels ship for Linux (x86_64, aarch64), macOS (x86_64, arm64), and Windows (x86_64). Python 3.10+.

Quick start

from papagan import Detector

detector = Detector()

# Document-level detection
output = detector.detect("Die Katze sitzt auf der Matte")
lang, confidence = output.top()
print(f"{lang}: {confidence:.3f}")
# de: 0.996

# Full distribution
for lang, score in output.distribution():
    print(f"  {lang}: {score:.3f}")

Per-word detail

Useful for mixed-language text or debugging:

detailed = detector.detect_detailed("The cat is black. Die Katze ist schwarz.")

for word in detailed.words:
    top_lang, top_score = max(word.scores, key=lambda x: x[1])
    print(f"  {word.token:<10} [{word.source}]  {top_lang} ({top_score:.2f})")
# the        [dict]   en (0.85)
# cat        [ngram]  en (0.99)
# ...
# katze      [ngram]  de (1.00)

# The aggregate handles mixed input gracefully:
print(detailed.aggregate.distribution())
# [('de', 0.52), ('en', 0.48)]

Restrict to specific languages

Faster and more confident when you know the input's language set in advance:

detector = Detector(only=["en", "de"])
# or with the builder:
detector = Detector.builder().only(["en", "de"]).build()

Configuration

detector = Detector(
    only=["en", "de", "fr"],       # restrict to a subset
    unknown_threshold=0.25,         # below this => ("?", ...) aka Lang.Unknown
    parallel_threshold=128,         # parallelize at 128+ words
)

Supported languages

Code	Language	Code	Language
de	German	it	Italian
en	English	nl	Dutch
es	Spanish	pl	Polish
fr	French	pt	Portuguese
ru	Russian	tr	Turkish

All 10 languages are bundled — no feature flags to set.

Type hints

The package ships .pyi stubs and a py.typed marker (PEP 561):

from papagan import Detector, Lang, Output, WordScore, LangCode, MatchSource

def classify(text: str) -> LangCode:
    lang, _score = Detector().detect(text).top()
    return lang  # typed as Literal["de", "en", ..., "?"]

Your type checker (mypy, pyright) will see full signatures for all classes, including the LangCode and MatchSource Literal types.

Accuracy

~99.4% on a 5000-sentence Tatoeba evaluation across the 10 supported languages. Per-language precision/recall is best on isolated scripts (Russian, Turkish — perfect) and slightly weaker on the close Iberian pair (Spanish/Portuguese — about 1.5% cross-confusion at dict-5k).

License

Dual-licensed under MIT or Apache-2.0, at your option.

Related

• Rust crate — the core library
• Node.js package — Node.js bindings
• GitHub — source, issues, development