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rjieba

jieba-rs Python binding

rjieba v0.2.1 334.7K downloads/30d#7,487 on PyPI35
Permissive license MIT Active released

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

A Python binding to the Rust-based jieba-rs Chinese text segmentation library, providing fast word tokenization and part-of-speech tagging for Chinese text.

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

pip

pip install rjieba

uv

uv add rjieba

poetry

poetry add rjieba

Installing rjieba

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

Tags

chinese text segmentationjieba tokenization pythonchinese word tokenizerchinese nlp tokenizationfast chinese text splittingjieba-rs python bindingchinese pos tagging
chinese-nlptokenizationrust-binding

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