Skip to content

Fix README.md h2 titles rendering #890

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
perkfly wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Fix README.md h2 titles rendering #890

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
30 changes: 15 additions & 15 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@ jieba

主要功能
=======
1. 分词
1 分词
--------
* `jieba.cut` 方法接受四个输入参数: 需要分词的字符串；cut_all 参数用来控制是否采用全模式；HMM 参数用来控制是否使用 HMM 模型；use_paddle 参数用来控制是否使用paddle模式下的分词模式，paddle模式采用延迟加载方式，通过enable_paddle接口安装paddlepaddle-tiny，并且import相关代码；
* `jieba.cut_for_search` 方法接受两个参数：需要分词的字符串；是否使用 HMM 模型。该方法适合用于搜索引擎构建倒排索引的分词，粒度比较细
Expand Down Expand Up @@ -81,7 +81,7 @@ print(", ".join(seg_list))

【搜索引擎模式】： 小明, 硕士, 毕业, 于, 中国, 科学, 学院, 科学院, 中国科学院, 计算, 计算所, 后, 在, 日本, 京都, 大学, 日本京都大学, 深造

2. 添加自定义词典
2 添加自定义词典
----------------

### 载入词典
Expand Down Expand Up @@ -139,7 +139,7 @@ print(", ".join(seg_list))

* "通过用户自定义词典来增强歧义纠错能力" --- https://github.com/fxsjy/jieba/issues/14

3. 关键词提取
3 关键词提取
-------------
### 基于 TF-IDF 算法的关键词抽取

Expand Down Expand Up @@ -189,7 +189,7 @@ https://github.com/fxsjy/jieba/blob/master/test/extract_tags.py

见 [test/demo.py](https://github.com/fxsjy/jieba/blob/master/test/demo.py)

4. 词性标注
4 词性标注
-----------
* `jieba.posseg.POSTokenizer(tokenizer=None)` 新建自定义分词器，`tokenizer` 参数可指定内部使用的 `jieba.Tokenizer` 分词器。`jieba.posseg.dt` 为默认词性标注分词器。
* 标注句子分词后每个词的词性，采用和 ictclas 兼容的标记法。
Expand Down Expand Up @@ -226,7 +226,7 @@ paddle模式词性和专名类别标签集合如下表，其中词性标签 24
| PER | 人名 | LOC | 地名 | ORG | 机构名 | TIME | 时间 |


5. 并行分词
5 并行分词
-----------
* 原理：将目标文本按行分隔后，把各行文本分配到多个 Python 进程并行分词，然后归并结果，从而获得分词速度的可观提升
* 基于 python 自带的 multiprocessing 模块，目前暂不支持 Windows
Expand All @@ -240,7 +240,7 @@ paddle模式词性和专名类别标签集合如下表，其中词性标签 24

* **注意**：并行分词仅支持默认分词器 `jieba.dt` 和 `jieba.posseg.dt`。

6. Tokenize：返回词语在原文的起止位置
6 Tokenize：返回词语在原文的起止位置
----------------------------------
* 注意，输入参数只接受 unicode
* 默认模式
Expand Down Expand Up @@ -277,12 +277,12 @@ word 有限公司 start: 6 end:10
```


7. ChineseAnalyzer for Whoosh 搜索引擎
7 ChineseAnalyzer for Whoosh 搜索引擎
--------------------------------------------
* 引用： `from jieba.analyse import ChineseAnalyzer`
* 用法示例：https://github.com/fxsjy/jieba/blob/master/test/test_whoosh.py

8. 命令行分词
8 命令行分词
-------------------

使用示例：`python -m jieba news.txt > cut_result.txt`
Expand Down Expand Up @@ -518,7 +518,7 @@ Algorithm
Main Functions
==============

1. Cut
1 Cut
--------
* The `jieba.cut` function accepts three input parameters: the first parameter is the string to be cut; the second parameter is `cut_all`, controlling the cut mode; the third parameter is to control whether to use the Hidden Markov Model.
* `jieba.cut_for_search` accepts two parameter: the string to be cut; whether to use the Hidden Markov Model. This will cut the sentence into short words suitable for search engines.
Expand Down Expand Up @@ -558,7 +558,7 @@ Output:
[Search Engine Mode]： 小明, 硕士, 毕业, 于, 中国, 科学, 学院, 科学院, 中国科学院, 计算, 计算所, 后, 在, 日本, 京都, 大学, 日本京都大学, 深造


2. Add a custom dictionary
2 Add a custom dictionary
----------------------------

### Load dictionary
Expand Down Expand Up @@ -615,7 +615,7 @@ Example:
「/台中/」/正确/应该/不会/被/切开
```

3. Keyword Extraction
3 Keyword Extraction
-----------------------
`import jieba.analyse`

Expand Down Expand Up @@ -650,7 +650,7 @@ Note that it filters POS by default.

`jieba.analyse.TextRank()` creates a new TextRank instance.

4. Part of Speech Tagging
4 Part of Speech Tagging
-------------------------
* `jieba.posseg.POSTokenizer(tokenizer=None)` creates a new customized Tokenizer. `tokenizer` specifies the jieba.Tokenizer to internally use. `jieba.posseg.dt` is the default POSTokenizer.
* Tags the POS of each word after segmentation, using labels compatible with ictclas.
Expand Down Expand Up @@ -683,7 +683,7 @@ Note that it filters POS by default.

* **Note** that parallel processing supports only default tokenizers, `jieba.dt` and `jieba.posseg.dt`.

6. Tokenize: return words with position
6 Tokenize: return words with position
----------------------------------------
* The input must be unicode
* Default mode
Expand Down Expand Up @@ -720,12 +720,12 @@ word 有限公司 start: 6 end:10
```


7. ChineseAnalyzer for Whoosh
7 ChineseAnalyzer for Whoosh
-------------------------------
* `from jieba.analyse import ChineseAnalyzer`
* Example: https://github.com/fxsjy/jieba/blob/master/test/test_whoosh.py

8. Command Line Interface
8 Command Line Interface
--------------------------------

$> python -m jieba --help
Expand Down