rjieba
jieba-rs Python binding
Decision gist · record as of 2026-08-14
Yes, if you need fast Chinese text segmentation. The package is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and offers significant performance gains over pure-Python alternatives. Install friction is moderate but manageable via prebuilt wheels. Verify that your platform and Python version are covered by available wheels before committing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Medium install friction due to compiled wheels; however, prebuilt binaries are available for common platforms (x86_64, ARM, ppc64le, s390x, i686) and Python versions, making installation straightforward on supported systems.
- Repository is actively maintained with recent commits.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; you must include a copy of the license in distributions but face no copyleft obligations.
last release 2026-04-25 (111 days) · last repo commit 2026-08-10 · 35 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 334,661 downloads/mo, #7,487 on PyPI
Alternatives
Verify before relying
pip install rjieba
import rjieba
print(rjieba.cut('我们中出了一个叛徒'))
print(rjieba.tag('我们中出了一个叛徒'))- Whether Python version support extends beyond what prebuilt wheels cover (requires_python is unspecified)
- Dictionary customization or user-defined vocabulary support beyond default behavior
- Memory usage and performance characteristics on large-scale production workloads
What it is and what it does
rjieba is a Python wrapper around jieba-rs, a Rust implementation of the jieba Chinese text segmentation algorithm. It provides two main functions: cut() for word tokenization and tag() for part-of-speech tagging of Chinese text. The package trades the pure-Python simplicity of the original jieba library for substantially faster performance by delegating the heavy lifting to compiled Rust code.
The library has no runtime dependencies and installs via precompiled wheels for most common platforms and Python versions. It's designed for developers working with Chinese natural language processing tasks who need reliable tokenization without the performance overhead of pure-Python alternatives.
Use it for
- Tokenizing Chinese text for search indexing or information retrieval systems
- Preprocessing Chinese documents before machine learning or NLP model training
- Extracting part-of-speech tags for Chinese text analysis and linguistic research
- Building Chinese text processing pipelines where performance is a constraint
- Integrating Chinese language support into web applications or APIs
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need fast Chinese text segmentation.
The package is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and offers significant performance gains over pure-Python alternatives. Install friction is moderate but manageable via prebuilt wheels. Verify that your platform and Python version are covered by available wheels before committing.
Install
rjieba on PyPI
Before you install
Medium install friction due to compiled wheels; however, prebuilt binaries are available for common platforms (x86_64, ARM, ppc64le, s390x, i686) and Python versions, making installation straightforward on supported systems. Repository is actively maintained with recent commits.
License in practice
MIT license permits commercial and private use with minimal restrictions; you must include a copy of the license in distributions but face no copyleft obligations.
Quickstart
pip install rjieba
import rjieba
print(rjieba.cut('我们中出了一个叛徒'))
print(rjieba.tag('我们中出了一个叛徒'))Verify before relying
- Whether Python version support extends beyond what prebuilt wheels cover (requires_python is unspecified)
- Dictionary customization or user-defined vocabulary support beyond default behavior
- Memory usage and performance characteristics on large-scale production workloads
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 111 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 334,661 / month, #7,487 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: rjieba-0.2.1-cp314-cp314t-macosx_10_12_x86_64.whl; rjieba-0.2.1-cp314-cp314t-macosx_11_0_arm64.whl; rjieba-0.2.1-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; rjieba-0.2.1-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl; rjieba-0.2.1-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl; rjieba-0.2.1-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl; rjieba-0.2.1-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; rjieba-0.2.1-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl; rjieba-0.2.1-cp314-cp314t-musllinux_1_2_aarch64.whl; rjieba-0.2.1-cp314-cp314t-musllinux_1_2_armv7l.whl; rjieba-0.2.1-cp314-cp314t-musllinux_1_2_i686.whl; rjieba-0.2.1-cp314-cp314t-musllinux_1_2_x86_64.whl; rjieba-0.2.1-cp314-cp314t-win32.whl; rjieba-0.2.1-cp314-cp314t-win_amd64.whl; rjieba-0.2.1-cp38-abi3-macosx_10_12_x86_64.whl; rjieba-0.2.1-cp38-abi3-macosx_11_0_arm64.whl; rjieba-0.2.1-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; rjieba-0.2.1-cp38-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl; rjieba-0.2.1-cp38-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl; rjieba-0.2.1-cp38-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl
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See also jieba3k · jieba · spacy-pkuseg · rouge-chinese · mecab · curated-tokenizers · segtok · textblob · Janome · pyvi