radixor-c — Fast Scalar Stemming for Python
radixor-c is the C-backed Python runtime for Radixor, a dictionary-trained transformation stemmer supporting 20 languages. Its native engine is implemented directly against the CPython C API to keep the overhead of stemming individual words low.
It uses the same precompiled models and stemming semantics as the Java flagship
and the Python (PyO3) package, distributed as radixor. The distinction is scope: radixor-c focuses
on basic runtime stemming from prepared models. Text dictionary compilation,
trie modification and the broader model toolchain belong to Java and, as it
converges toward Java, the radixor Python package.
Which Radixor package should I use?
| Package | Choose it for | Model operations |
|---|---|---|
Python-C (radixor-c) |
Fast calls for individual Python words; simple deployment | Load standard or compiled Radixor models |
Python (PyO3) (radixor) |
High-throughput batch processing and Python-side compilation | Load compiled models and compile textual dictionaries; broader capabilities are added here first |
| Java Radixor | The complete, flagship API and model development | Full construction, reduction, extension and persistence |
Radixor-c is not a lower-quality stemmer. Given the same compiled trie it produces the same results; it currently exposes fewer ways to create or modify that trie.
Installation
python -m pip install --only-binary=:all: radixor-c
Published wheels support CPython 3.10–3.14 on Linux, macOS and Windows. The
installation also resolves radixor-models-standard, the shared package of 20
precompiled standard models.
Quick start
from radixor_c import Stemmer
stemmer = Stemmer("en")
stemmer.stem("running") # "run"
stemmer.stem("unknown_word") # None
Construct the stemmer once and reuse it. Scalar calls are the primary reason to select radixor-c, but batch and PyStemmer-compatible methods are also available:
words = ["running", "studies", "cars"]
stemmer.stem_batch(words)
stemmer.stemWord("running")
stemmer.stemWords(words)
stem() and stem_batch() return None where no patch applies.
stemWord() and stemWords() instead return unmatched input unchanged.
Custom models
Radixor-c loads compiled Radixor models:
custom = Stemmer(compiled="models/domain-english.rxc")
It deliberately does not compile a text dictionary. Prepare the interoperable
.rxc file with Java or the radixor package:
import radixor
radixor.compile("domain.tsv.gz", "models/domain-english.rxc", language="en")
The resulting model can be loaded by all three implementations.
API at a glance
| API | Purpose |
|---|---|
Stemmer(language) |
Load a bundled model by alias or model ID |
Stemmer(compiled=path) |
Load an application-owned compiled Radixor model |
stem(word) |
Return the dominant stem or None |
stem_batch(words) |
Stem a list with positional None results |
stemWord(word) / stemWords(words) |
PyStemmer-compatible unmatched-word fallback |
stem_all(word) / stem_all_batch(words) |
Return ranked alternative stems |
algorithms() / version() |
Compatibility and package information |
The default bounded cache holds 10,000 results. Use cache_size=0 to disable
it, or configure the PyStemmer-compatible maxCacheSize property.
Documentation
- Choose a Radixor runtime
- Python-C quick start
- Python-C usage and API
- Shared Python benchmark
- Shared data formats
Radixor software is available under the BSD 3-Clause License. Model data keeps its separately documented provenance and licensing.
1 maintainer
Release files for radixor-c 4.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| radixor_c-4.2.0.tar.gz | 22.0 kB | Details |
Built distributions (wheels)
Total release size: 1.7 MB
Release files / radixor_c-4.2.0.tar.gz
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