opencc-jieba-pyo3 0.7.4

High-performance Chinese text conversion (Simplified ↔ Traditional), segmentation and keyword extraction powered by Rust, PyO3, Jieba and OpenCC lexicons.

opencc_jieba_pyo3

opencc_jieba_pyo3 is a Python extension module powered by Rust, Jieba and PyO3, providing fast and accurate conversion between different Chinese text variants using opencc-jieba-rs and OpenCC algorithms.

Features

• Convert between Simplified, Traditional, Hong Kong, Taiwan, and Japanese Kanji Chinese text.
• Fast and memory-efficient, leveraging Rust's performance.
• Easy-to-use Python API.
• Supports punctuation conversion and automatic text code detection.
• Chinese word (Both Traditional and Simplified) segmentation (Jieba).
• Keyword extraction (TF-IDF, TextRank).
• Utility functions for punctuation handling and language detection.

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🔁 Supported Conversion Configs

Code	Description
s2t	Simplified → Traditional
t2s	Traditional → Simplified
s2tw	Simplified → Traditional (Taiwan)
tw2s	Traditional (Taiwan) → Simplified
s2twp	Simplified → Traditional (Taiwan) with idioms
tw2sp	Traditional (Taiwan) → Simplified with idioms
s2hk	Simplified → Traditional (Hong Kong)
hk2s	Traditional (Hong Kong) → Simplified
t2tw	Traditional → Traditional (Taiwan)
tw2t	Traditional (Taiwan) → Traditional
t2twp	Traditional → Traditional (Taiwan) with idioms
tw2tp	Traditional (Taiwan) → Traditional with idioms
t2hk	Traditional → Traditional (Hong Kong)
hk2t	Traditional (Hong Kong) → Traditional
t2jp	Japanese Kyujitai → Shinjitai
jp2t	Japanese Shinjitai → Kyujitai

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Installation

Build and install the Python wheel using maturin:

# In project root
maturin build --release
pip install ./target/wheels/opencc_jieba_pyo3-<version>-cp<pyver>-abi3-<platform>.whl

Or for development:

maturin develop -r

See BUILD.md for detailed build and install instructions.

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Usage

Python

from opencc_jieba_pyo3 import OpenCC

text = "“春眠不觉晓，处处闻啼鸟。”"
opencc = OpenCC("s2t")
converted = opencc.convert(text, punctuation=True)
print(converted)  # 「春眠不覺曉，處處聞啼鳥。」

# Segmentation
words = opencc.jieba_cut(text, hmm=True)
print(words)  # ['春眠', '不觉', '晓', '，', '处处', '闻', '啼鸟', '。']

# Segmentation and join
joined = opencc.jieba_cut_and_join(text, delimiter="/")
print(joined)  # 春眠/不觉/晓/，/处处/闻/啼鸟/。

# Keyword extraction (TextRank)
keywords = opencc.jieba_keyword_extract_textrank(text, top_k=3)
print(keywords)  # ['春眠', '啼鸟', '处处']

# Keyword extraction (TF-IDF)
keywords_tfidf = opencc.jieba_keyword_extract_tfidf(text, top_k=3)
print(keywords_tfidf)  # ['春眠', '啼鸟', '处处']

# Keyword weights (TextRank)
kw_weights = opencc.jieba_keyword_weight_textrank(text, top_k=3)
print(kw_weights)  # [('春眠', 1.23), ('啼鸟', 0.98), ('处处', 0.75)]

# Keyword weights (TF-IDF)
kw_weights_tfidf = opencc.jieba_keyword_weight_tfidf(text, top_k=3)
print(kw_weights_tfidf)  # [('春眠', 2.34), ('啼鸟', 1.56), ('处处', 1.12)]

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CLI

You can also use the CLI interface via Python module or Python script:
Features are:

• convert: Convert Chinese text using OpenCC + Jieba
• segment: Segment Chinese text using Jieba
• office: Convert Office document Chinese text using OpenCC + Jieba

convert

Module: python -m opencc_jieba_pyo3 convert --help
Script: opencc-jieba-pyo3 convert --help

usage: opencc_jieba_pyo3 convert [-h] [-i <file>] [-o <file>] [-c <conversion>] [-p] [--in-enc <encoding>] [--out-enc <encoding>]

options:
  -h, --help            show this help message and exit
  -i, --input <file>    Read original text from <file>.
  -o, --output <file>   Write converted text to <file>.
  -c, --config <conversion>
                        Conversion configuration: [s2t|s2tw|s2twp|s2hk|t2s|tw2s|tw2sp|hk2s|jp2t|t2jp]
  -p, --punct           Punctuation conversion
  --in-enc <encoding>   Encoding for input
  --out-enc <encoding>  Encoding for output

segment

python -m opencc_jieba_pyo3 segment --help
opencc-jieba-pyo3 convert segment --help

usage: opencc-jieba-pyo3 segment [-h] [-i <file>] [-o <file>] [-d <char>] [--mode {cut,search,full}] [--in-enc <encoding>] [--out-enc <encoding>]

options:
  -h, --help            show this help message and exit
  -i, --input <file>    Read input text from <file>. (default: None)
  -o, --output <file>   Write segmented text to <file>. (default: None)
  -d, --delim <char>    Delimiter to join segments (default: )
  --mode {cut,search,full}
                        Segmentation mode (default: cut)
  --in-enc <encoding>   Encoding for input (default: UTF-8)
  --out-enc <encoding>  Encoding for output (default: UTF-8)

office

python -m opencc_jieba_pyo3 office --help                                                     
usage: opencc_jieba_pyo3 office [-h] [-i <file>] [-o <file>] [-c <conversion>] [-p] [-f <format>] [--auto-ext] [--keep-font]

options:
  -h, --help            show this help message and exit
  -i, --input <file>    Input Office document from <file>.
  -o, --output <file>   Output Office document to <file>.
  -c, --config <conversion>
                        conversion: s2t|s2tw|s2twp|s2hk|t2s|tw2s|tw2sp|hk2s|jp2t|t2jp
  -p, --punct           Punctuation conversion
  -f, --format <format>
                        Target Office format (e.g., docx, xlsx, pptx, odt, ods, odp, epub)
  --auto-ext            Auto-append extension to output file
  --keep-font           Preserve font-family information in Office content)

python -m opencc_jieba_pyo3 convert -i input.txt -o output.txt -c s2t --punct
opencc-jieba-pyo3 convert -i input.txt -o output.txt -c s2t --punct

python -m opencc_jieba_pyo3 segment -i input.txt -o output.txt --delim "/"
opencc-jieba-pyo3 segment -i input.txt -o output.txt --delim "/" --mode search

python -m opencc_jieba_pyo3 office -i input.docx -o output.docx -c s2t --punct --keep-font
opencc-jieba-pyo3 office -i input.epub -o output.epub -c s2tw --punct

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API

Class: OpenCC

Unified Python interface for OpenCC and Jieba functionalities.

Constructor

• OpenCC(config: str = "s2t")
  • config: Conversion configuration (see above). Defaults to "s2t".

Attributes

• config: str
  • Current OpenCC conversion configuration.

Methods

• convert(input: str, punctuation: bool = False) -> str

  • Convert Chinese text using the current OpenCC config.
  • input: Input text.
  • punctuation: Whether to convert Chinese/Japanese punctuation to the target variant.
  • Returns: Converted text as a string.

• zho_check(input: str) -> int

  • Detect the type of Chinese in the input text.
  • Returns: Integer code (1: Traditional, 2: Simplified, 0: Others).

• jieba_cut(input: str, hmm: bool = True) -> list[str]

  • Segment Chinese text using Jieba.
  • input: Input text.
  • hmm: Whether to use HMM for new words.
  • Returns: List of segmented words.

• jieba_cut_and_join(input: str, delimiter: str = "/") -> str

  • Segment and join Chinese text using Jieba.
  • input: Input text.
  • delimiter: Delimiter for joining words.
  • Returns: Joined segmented string.

• jieba_keyword_extract_textrank(input: str, top_k: int) -> list[str]

  • Extract keywords using the TextRank algorithm.
  • input: Input text.
  • top_k: Number of keywords to extract.
  • Returns: List of keywords.

• jieba_keyword_extract_tfidf(input: str, top_k: int) -> list[str]

  • Extract keywords using the TF-IDF algorithm.
  • input: Input text.
  • top_k: Number of keywords to extract.
  • Returns: List of keywords.

• jieba_keyword_weight_textrank(input: str, top_k: int) -> list[tuple[str, float]]

  • Extract keywords and their weights using TextRank.
  • input: Input text.
  • top_k: Number of keywords to extract.
  • Returns: List of (keyword, weight) tuples.

• jieba_keyword_weight_tfidf(input: str, top_k: int) -> list[tuple[str, float]]

  • Extract keywords and their weights using TF-IDF.
  • input: Input text.
  • top_k: Number of keywords to extract.
  • Returns: List of (keyword, weight) tuples.

Development

• Rust source: src/lib.rs
• Python bindings: /opencc_jieba_pyo3/__init __.py, opencc_jieba_pyo3/opencc_jieba_pyo3.pyi
• CLI: opencc_jieba_pyo3/__main __.py

Rust Module Required

opencc-jieba-rs : A Rust implementation of Jieba + OpenCC

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Benchmarks

Package: opencc_jieba_pyo3
Python 3.13.4 (tags/v3.13.4:8a526ec, Jun  3 2025, 17:46:04) [MSC v.1943 64 bit (AMD64)]
Platform: Windows-11-10.0.26100-SP0
Processor: Intel64 Family 6 Model 191 Stepping 2, GenuineIntel

BENCHMARK RESULTS

Method	Config	TextSize	Mean	StdDev	Min	Max	Ops/sec	Chars/sec
Convert_Small	s2t	100	0.161 ms	0.109 ms	0.080 ms	0.794 ms	6,217	621,740
Convert_Medium	s2t	1,000	0.389 ms	0.092 ms	0.286 ms	0.829 ms	2,571	2,571,236
Convert_Large	s2t	10,000	1.261 ms	0.314 ms	1.072 ms	2.580 ms	793	7,932,120
Convert_XLarge	s2t	100,000	7.290 ms	0.464 ms	6.864 ms	9.848 ms	137	13,716,798
Convert_Small	s2tw	100	0.189 ms	0.104 ms	0.103 ms	0.620 ms	5,285	528,519
Convert_Medium	s2tw	1,000	0.442 ms	0.152 ms	0.322 ms	1.084 ms	2,264	2,264,206
Convert_Large	s2tw	10,000	1.508 ms	0.200 ms	1.367 ms	2.371 ms	663	6,631,682
Convert_XLarge	s2tw	100,000	9.403 ms	0.585 ms	9.009 ms	13.320 ms	106	10,635,363
Convert_Small	s2twp	100	0.235 ms	0.113 ms	0.129 ms	0.648 ms	4,256	425,586
Convert_Medium	s2twp	1,000	0.518 ms	0.112 ms	0.363 ms	0.913 ms	1,932	1,932,266
Convert_Large	s2twp	10,000	1.786 ms	0.209 ms	1.590 ms	2.739 ms	560	5,598,571
Convert_XLarge	s2twp	100,000	11.644 ms	0.979 ms	10.892 ms	17.130 ms	86	8,588,034

Throughput VS Size

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License

MIT

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Powered by Rust, Jieba, PyO3, OpenCC and opencc-jieba-rs.