py-rust-stemmers
Fast and parallel snowball stemmer
Decision gist · record as of 2026-08-14
Yes, with a license verification step. The package is actively maintained, has no known vulnerabilities, and offers genuine performance benefits for text stemming at scale via parallelism and Rust compilation. Install friction is moderate but manageable via prebuilt wheels on standard platforms. Before production use, confirm the actual license terms (metadata is unclear despite the MIT claim in the description).AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Medium install friction due to compiled wheels; prebuilt binaries available for Python 3.10 and 3.11 on Linux, macOS, and Windows.
- Package is actively maintained with recent release activity.
License · maintenance · safety
(unclear) — License treatment is unclear—no SPDX identifier or raw license metadata provided in the package record, despite the description mentioning MIT. Verify the actual license terms before use in proprietary or restricted contexts.
last release 2026-05-22 (84 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 5,173,435 downloads/mo, #2,150 on PyPI
Alternatives
Verify before relying
pip install py-rust-stemmers
from py_rust_stemmers import SnowballStemmer
s = SnowballStemmer('english')
stemmed = s.stem_word('running')
stemmed_words = s.stem_words(['running', 'jumps', 'easily'])
stemmed_parallel = s.stem_words_parallel(['running', 'jumps', 'easily'])- Actual license terms and enforceability (description claims MIT but metadata is absent)
- Performance improvement magnitude for parallel stemming relative to sequential on typical workloads
- Language support breadth beyond English (Snowball supports multiple languages but not documented here)
- Minimum word-list size threshold where parallel processing becomes beneficial
What it is and what it does
py-rust-stemmers wraps the Rust-based rust-stemmers library to bring Snowball stemming algorithms into Python with high performance. It exposes three main methods: stem_word() for single words, stem_words() for sequential batch processing, and stem_words_parallel() for parallel batch processing of larger word lists. The package is compiled to native code using maturin, eliminating the Python GIL and leveraging Rust's speed for text normalization tasks.
The library is designed for text preprocessing pipelines where word reduction to a common stem form improves downstream search, clustering, or classification. It requires Python 3.10 or later and has no runtime dependencies beyond the compiled extension itself. Installation is straightforward on common platforms via prebuilt wheels, though less common architectures may require building from source.
Use it for
- Preprocess large document collections for search indexing by reducing words to stems in parallel batches
- Normalize user queries in real-time search systems using the single-word stemmer for low-latency responses
- Batch-stem word vocabularies during NLP model training or feature engineering pipelines
- Reduce memory footprint in text classification by conflating morphological variants before vectorization
- Build stemmed inverted indexes for full-text search engines with parallel processing of corpus text
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with a license verification step.
The package is actively maintained, has no known vulnerabilities, and offers genuine performance benefits for text stemming at scale via parallelism and Rust compilation. Install friction is moderate but manageable via prebuilt wheels on standard platforms. Before production use, confirm the actual license terms (metadata is unclear despite the MIT claim in the description).
Install
py-rust-stemmers on PyPI
Before you install
Medium install friction due to compiled wheels; prebuilt binaries available for Python 3.10 and 3.11 on Linux, macOS, and Windows. Package is actively maintained with recent release activity.
Requires Python 3.10 or later.
License in practice
License treatment is unclear—no SPDX identifier or raw license metadata provided in the package record, despite the description mentioning MIT. Verify the actual license terms before use in proprietary or restricted contexts.
Quickstart
pip install py-rust-stemmers
from py_rust_stemmers import SnowballStemmer
s = SnowballStemmer('english')
stemmed = s.stem_word('running')
stemmed_words = s.stem_words(['running', 'jumps', 'easily'])
stemmed_parallel = s.stem_words_parallel(['running', 'jumps', 'easily'])Verify before relying
- Actual license terms and enforceability (description claims MIT but metadata is absent)
- Performance improvement magnitude for parallel stemming relative to sequential on typical workloads
- Language support breadth beyond English (Snowball supports multiple languages but not documented here)
- Minimum word-list size threshold where parallel processing becomes beneficial
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 84 days since the last release |
| First released | |
| Downloads | 5,173,435 / month, #2,150 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: py_rust_stemmers-0.1.8-cp310-cp310-macosx_10_12_x86_64.whl; py_rust_stemmers-0.1.8-cp310-cp310-macosx_11_0_arm64.whl; py_rust_stemmers-0.1.8-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; py_rust_stemmers-0.1.8-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl; py_rust_stemmers-0.1.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; py_rust_stemmers-0.1.8-cp310-cp310-manylinux_2_28_x86_64.whl; py_rust_stemmers-0.1.8-cp310-cp310-musllinux_1_2_aarch64.whl; py_rust_stemmers-0.1.8-cp310-cp310-musllinux_1_2_armv7l.whl; py_rust_stemmers-0.1.8-cp310-cp310-musllinux_1_2_x86_64.whl; py_rust_stemmers-0.1.8-cp310-cp310-win_amd64.whl; py_rust_stemmers-0.1.8-cp311-cp311-macosx_10_12_x86_64.whl; py_rust_stemmers-0.1.8-cp311-cp311-macosx_11_0_arm64.whl; py_rust_stemmers-0.1.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; py_rust_stemmers-0.1.8-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl; py_rust_stemmers-0.1.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; py_rust_stemmers-0.1.8-cp311-cp311-manylinux_2_28_x86_64.whl; py_rust_stemmers-0.1.8-cp311-cp311-musllinux_1_2_aarch64.whl; py_rust_stemmers-0.1.8-cp311-cp311-musllinux_1_2_armv7l.whl; py_rust_stemmers-0.1.8-cp311-cp311-musllinux_1_2_x86_64.whl; py_rust_stemmers-0.1.8-cp311-cp311-win_amd64.whl
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