| Language | version |
| ------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [Python](python/interface/README.md) | [](https://pypi.org/project/ltp) [](https://pypi.org/project/ltp-core) [](https://pypi.org/project/ltp-extension) |
| [Rust](rust/ltp/README.md) | [](https://crates.io/crates/ltp) |
# LTP extension For Python
LTP for Rust 对 Python 的绑定，用于提升 LTP 的速度，以及加入传统机器学习算法实现的中文信息处理工具。
| method | ltp 3.0(c++) | ap(1) | ap(8) | pa | pa-i(0.5) | pa-ii(0.5) |
| ------ | ------------ | ----- | ----- | ----- | --------- | ---------- |
| cws | 97.83 | 97.93 | 97.67 | 97.90 | 97.90 | 97.93 |
| pos | 98.35 | 98.41 | 98.30 | 98.39 | 98.39 | 98.38 |
| ner | 94.17 | 94.28 | 93.42 | 94.02 | 94.06 | 93.95 |
## 自行编译安装
```bash
maturin build --release -m python/extension/Cargo.toml --out dist --no-default-features --features="malloc"
# or 针对cpu优化
maturin build --release -m python/extension/Cargo.toml --out dist --no-default-features --features="malloc" -- -C target-cpu=native
```
## features
- [x] 分句
- [x] 任务
- [x] 中文分词(cws)
- [ ] 对数字、英文、网址、邮件的处理
- [x] 支持自定义词典
- [x] 词性标注(pos)
- [ ] 支持自定义词典
- [x] 命名实体识别(ner)
- [x] 算法
- [x] 平均感知机(ap)
- [x] 单线程平均感知机
- [x] 多线程平均感知机
- [x] 被动攻击算法(pa)
- [ ] 模型量化
- [ ] 在线学习
- [ ] 增量学习
## 性能测试
### 评测环境
- Python 3.10
- MacBook Pro (16-inch, 2019)
- 处理器: 2.6 GHz 六核Intel Core i7
- 内存: 16 GB 2667 MHz DDR4
> 注: 速度测试文件大小为 33.85 MB / 305041 行
### 分词
我们选择Jieba、Pkuseg、Thulac等国内代表分词软件与 LTP 做性能比较，根据第二届国际汉语分词测评发布的国际中文分词测评标准，对不同软件进行了速度和准确率测试。
在第二届国际汉语分词测评中，共有四家单位提供的测试语料（Academia Sinica、 City University 、Peking University(PKU)
、Microsoft Research(MSR)）, 在评测提供的资源[icwb2-data](http://sighan.cs.uchicago.edu/bakeoff2005/)
中包含了来自这四家单位的训练集（icwb2-data/training）、测试集（icwb2-data/testing）,
以及根据各自分词标准而提供的相应测试集的标准答案（icwb2-data/gold）．在icwb2-data/scripts目录下含有对分词进行自动评分的perl脚本score。
我们在统一测试环境下，对若干流行分词软件和 LTP 进行了测试，使用的模型为各分词软件自带模型。在PKU和MSR测试集评测结果如下：
| Algorithm | Speed(KB/s) | PKU(F1) | MSR(F1) |
| ---------------------------------------------------------------------------- | ----------: | -------: | -------: |
| [Jieba](https://github.com/fxsjy/jieba) | 982.49 | 81.8 | 81.3 |
| [Pkuseg](https://github.com/lancopku/pkuseg-python) | 109.72 | 93.4 | 87.3 |
| [Thulac](https://github.com/thunlp/THULAC-Python) | 48.13 | 94.0 | 87.9 |
| [Thulac\[Fast\]](https://github.com/thunlp/THULAC-Python) | 1133.21 | 同上 | 同上 |
| [LTP 3(pyltp)](https://github.com/HIT-SCIR/pyltp) | 451.20 | **95.3** | **88.3** |
| [LTP legacy(1)](https://github.com/HIT-SCIR/ltp/tree/main/python/extension) | **1603.63** | 95.2 | 87.7 |
| [LTP legacy(2)](https://github.com/HIT-SCIR/ltp/tree/main/python/extension) | 2869.42 | 同上 | 同上 |
| [LTP legacy(4)](https://github.com/HIT-SCIR/ltp/tree/main/python/extension) | 4949.38 | 同上 | 同上 |
| [LTP legacy(8)](https://github.com/HIT-SCIR/ltp/tree/main/python/extension) | 6803.88 | 同上 | 同上 |
| [LTP legacy(16)](https://github.com/HIT-SCIR/ltp/tree/main/python/extension) | **7745.16** | 同上 | 同上 |
> **注：括号内为线程数量**
> **注2：Jieba的词表是在人民日报数据集上统计的**
### 流水线
除了分词以外，我们也测试了 LTP 三个任务（分词、词性标注、命名实体识别）流水线的速度：
| Algorithm | Speed(KB/s) |
| ---------------------------------------------------------------------------- | ----------: |
| [LTP 3(pyltp)](https://github.com/HIT-SCIR/pyltp) | 153.10 |
| [LTP legacy(1)](https://github.com/HIT-SCIR/ltp/tree/main/python/extension) | 508.74 |
| [LTP legacy(2)](https://github.com/HIT-SCIR/ltp/tree/main/python/extension) | 899.25 |
| [LTP legacy(4)](https://github.com/HIT-SCIR/ltp/tree/main/python/extension) | 1598.03 |
| [LTP legacy(8)](https://github.com/HIT-SCIR/ltp/tree/main/python/extension) | 2267.48 |
| [LTP legacy(16)](https://github.com/HIT-SCIR/ltp/tree/main/python/extension) | 2452.34 |
> **注：括号内为线程数量**
> **注2：速度数据在人民日报命名实体测试数据上获得，速度计算方式均为所有任务顺序执行的结果。**
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"description": "| Language | version |\n| ------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| [Python](python/interface/README.md) | [](https://pypi.org/project/ltp) [](https://pypi.org/project/ltp-core) [](https://pypi.org/project/ltp-extension) |\n| [Rust](rust/ltp/README.md) | [](https://crates.io/crates/ltp) |\n\n# LTP extension For Python\n\nLTP for Rust \u5bf9 Python \u7684\u7ed1\u5b9a\uff0c\u7528\u4e8e\u63d0\u5347 LTP \u7684\u901f\u5ea6\uff0c\u4ee5\u53ca\u52a0\u5165\u4f20\u7edf\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5b9e\u73b0\u7684\u4e2d\u6587\u4fe1\u606f\u5904\u7406\u5de5\u5177\u3002\n\n| method | ltp 3.0(c++) | ap(1) | ap(8) | pa | pa-i(0.5) | pa-ii(0.5) |\n| ------ | ------------ | ----- | ----- | ----- | --------- | ---------- |\n| cws | 97.83 | 97.93 | 97.67 | 97.90 | 97.90 | 97.93 |\n| pos | 98.35 | 98.41 | 98.30 | 98.39 | 98.39 | 98.38 |\n| ner | 94.17 | 94.28 | 93.42 | 94.02 | 94.06 | 93.95 |\n\n## \u81ea\u884c\u7f16\u8bd1\u5b89\u88c5\n\n```bash\nmaturin build --release -m python/extension/Cargo.toml --out dist --no-default-features --features=\"malloc\"\n# or \u9488\u5bf9cpu\u4f18\u5316\nmaturin build --release -m python/extension/Cargo.toml --out dist --no-default-features --features=\"malloc\" -- -C target-cpu=native\n```\n\n## features\n\n- [x] \u5206\u53e5\n- [x] \u4efb\u52a1\n - [x] \u4e2d\u6587\u5206\u8bcd(cws)\n - [ ] \u5bf9\u6570\u5b57\u3001\u82f1\u6587\u3001\u7f51\u5740\u3001\u90ae\u4ef6\u7684\u5904\u7406\n - [x] \u652f\u6301\u81ea\u5b9a\u4e49\u8bcd\u5178\n - [x] \u8bcd\u6027\u6807\u6ce8(pos)\n - [ ] \u652f\u6301\u81ea\u5b9a\u4e49\u8bcd\u5178\n - [x] \u547d\u540d\u5b9e\u4f53\u8bc6\u522b(ner)\n- [x] \u7b97\u6cd5\n - [x] \u5e73\u5747\u611f\u77e5\u673a(ap)\n - [x] \u5355\u7ebf\u7a0b\u5e73\u5747\u611f\u77e5\u673a\n - [x] \u591a\u7ebf\u7a0b\u5e73\u5747\u611f\u77e5\u673a\n - [x] \u88ab\u52a8\u653b\u51fb\u7b97\u6cd5(pa)\n- [ ] \u6a21\u578b\u91cf\u5316\n- [ ] \u5728\u7ebf\u5b66\u4e60\n- [ ] \u589e\u91cf\u5b66\u4e60\n\n## \u6027\u80fd\u6d4b\u8bd5\n\n### \u8bc4\u6d4b\u73af\u5883\n\n- Python 3.10\n- MacBook Pro (16-inch, 2019)\n- \u5904\u7406\u5668: 2.6 GHz \u516d\u6838Intel Core i7\n- \u5185\u5b58: 16 GB 2667 MHz DDR4\n\n> \u6ce8: \u901f\u5ea6\u6d4b\u8bd5\u6587\u4ef6\u5927\u5c0f\u4e3a 33.85 MB / 305041 \u884c\n\n### \u5206\u8bcd\n\n\u6211\u4eec\u9009\u62e9Jieba\u3001Pkuseg\u3001Thulac\u7b49\u56fd\u5185\u4ee3\u8868\u5206\u8bcd\u8f6f\u4ef6\u4e0e LTP \u505a\u6027\u80fd\u6bd4\u8f83\uff0c\u6839\u636e\u7b2c\u4e8c\u5c4a\u56fd\u9645\u6c49\u8bed\u5206\u8bcd\u6d4b\u8bc4\u53d1\u5e03\u7684\u56fd\u9645\u4e2d\u6587\u5206\u8bcd\u6d4b\u8bc4\u6807\u51c6\uff0c\u5bf9\u4e0d\u540c\u8f6f\u4ef6\u8fdb\u884c\u4e86\u901f\u5ea6\u548c\u51c6\u786e\u7387\u6d4b\u8bd5\u3002\n\n\u5728\u7b2c\u4e8c\u5c4a\u56fd\u9645\u6c49\u8bed\u5206\u8bcd\u6d4b\u8bc4\u4e2d\uff0c\u5171\u6709\u56db\u5bb6\u5355\u4f4d\u63d0\u4f9b\u7684\u6d4b\u8bd5\u8bed\u6599\uff08Academia Sinica\u3001 City University \u3001Peking University(PKU)\n\u3001Microsoft Research(MSR)\uff09, \u5728\u8bc4\u6d4b\u63d0\u4f9b\u7684\u8d44\u6e90[icwb2-data](http://sighan.cs.uchicago.edu/bakeoff2005/)\n\u4e2d\u5305\u542b\u4e86\u6765\u81ea\u8fd9\u56db\u5bb6\u5355\u4f4d\u7684\u8bad\u7ec3\u96c6\uff08icwb2-data/training\uff09\u3001\u6d4b\u8bd5\u96c6\uff08icwb2-data/testing\uff09,\n\u4ee5\u53ca\u6839\u636e\u5404\u81ea\u5206\u8bcd\u6807\u51c6\u800c\u63d0\u4f9b\u7684\u76f8\u5e94\u6d4b\u8bd5\u96c6\u7684\u6807\u51c6\u7b54\u6848\uff08icwb2-data/gold\uff09\uff0e\u5728icwb2-data/scripts\u76ee\u5f55\u4e0b\u542b\u6709\u5bf9\u5206\u8bcd\u8fdb\u884c\u81ea\u52a8\u8bc4\u5206\u7684perl\u811a\u672cscore\u3002\n\n\u6211\u4eec\u5728\u7edf\u4e00\u6d4b\u8bd5\u73af\u5883\u4e0b\uff0c\u5bf9\u82e5\u5e72\u6d41\u884c\u5206\u8bcd\u8f6f\u4ef6\u548c LTP \u8fdb\u884c\u4e86\u6d4b\u8bd5\uff0c\u4f7f\u7528\u7684\u6a21\u578b\u4e3a\u5404\u5206\u8bcd\u8f6f\u4ef6\u81ea\u5e26\u6a21\u578b\u3002\u5728PKU\u548cMSR\u6d4b\u8bd5\u96c6\u8bc4\u6d4b\u7ed3\u679c\u5982\u4e0b\uff1a\n\n| Algorithm | Speed(KB/s) | PKU(F1) | MSR(F1) 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