This release is a pre-release and may not be stable for production use.
jieba-next
jieba-next 是 jieba_fast 的一个现代化分支，旨在提供对 Python 3.9+ 的支持，并利用 Rust 进行了代码优化和加速。
jieba_fast 本身是经典中文分词库 jieba 的一个 CPython 加速版本。本项目在 jieba_fast 的基础上，更新了构建系统，并用 Rust (via PyO3) 重新实现了部分核心算法，进一步提升了性能，解决了内存泄漏问题，并提升了可维护性。
项目特点
- 现代化：支持 Python 3.9 及更高版本，不再支持 Python 2。
- 性能：利用 Rust (via PyO3) 重新实现了生成 DAG（有向无环图）、计算最优路径以及 Viterbi 算法，以提升分词速度。
- 兼容性：力求与原版
jieba和jieba_fast的分词结果保持一致。 - 易于安装：使用现代化的构建工具，提供多平台的预编译二进制包（wheels），简化安装过程。
- 易于使用：可以作为
jieba的直接替代品，只需import jieba_next as jieba。
当前状态
本项目目前处于早期开发阶段：
- 已完成基础功能测试，可以正确执行分词任务。
- 与原
jieba_fast仓库的分词结果具有一致性。 - 性能进一步领先于原
jieba_fast仓库，后续将持续进行优化。 - 测试覆盖尚不完整，欢迎贡献测试用例。
安装
对于大多数常见平台，您可以直接通过 pip 从 PyPI 安装：
pip install jieba-next
如果安装过程中遇到问题，可以尝试安装 Rust 工具链：
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
您也可以从源码安装（需要 Rust 工具链）：
git clone https://github.com/mxcoras/jieba-next.git
cd jieba-next
pip install .
使用示例
可以像使用 jieba 或 jieba_fast 一样使用 jieba-next。
import jieba_next as jieba
text = "在输出层后再增加CRF层，加强了文本间信息的相关性，针对序列标注问题，每个句子的每个词都有一个标注结果，对句子中第i个词进行高维特征的抽取，通过学习特征到标注结果的映射，可以得到特征到任意标签的概率，通过这些概率，得到最优序列结果"
print("-".join(jieba.lcut(text, HMM=True)))
print('-'.join(jieba.lcut(text, HMM=False)))
输出:
在-输出-层后-再-增加-CRF-层-，-加强-了-文本-间-信息-的-相关性-，-针对-序列-标注-问题-，-每个-句子-的-每个-词-都-有-一个-标注-结果-，-对-句子-中-第-i-个-词-进行-高维-特征-的-抽取-，-通过-学习-特征-到-标注-结果-的-映射-，-可以-得到-特征-到-任意-标签-的-概率-，-通过-这些-概率-，-得到-最优-序列-结果
在-输出-层-后-再-增加-CRF-层-，-加强-了-文本-间-信息-的-相关性-，-针对-序列-标注-问题-，-每个-句子-的-每个-词-都-有-一个-标注-结果-，-对-句子-中-第-i-个-词-进行-高维-特征-的-抽取-，-通过-学习-特征-到-标注-结果-的-映射-，-可以-得到-特征-到-任意-标签-的-概率-，-通过-这些-概率-，-得到-最优-序列-结果
算法
- 基于前缀词典实现高效的词图扫描，生成句子中汉字所有可能成词情况所构成的有向无环图 (DAG)。
- 采用动态规划查找最大概率路径, 找出基于词频的最大切分组合。
- 对于未登录词，采用了基于汉字成词能力的 HMM 模型，并使用了 Viterbi 算法。
鸣谢
"结巴"中文分词原作者: SunJunyi
jieba_fast 仓库作者: deepcs233
1 maintainer
Metadata
1 maintainer
Release files for jieba-next 1.0.0rc1
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