### Install pkuseg via pip

Source: https://github.com/lancopku/pkuseg-python/blob/master/README.md

Commands to install or update the pkuseg package using pip, including options for using mirror sources.

```bash
pip3 install pkuseg
pip3 install -U pkuseg
pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple pkuseg
```

--------------------------------

### Perform Chinese Word Segmentation

Source: https://github.com/lancopku/pkuseg-python/blob/master/README.md

Examples of using pkuseg to perform word segmentation with default settings, specific domain models, and part-of-speech tagging.

```python
import pkuseg

# Default configuration
seg = pkuseg.pkuseg()
print(seg.cut('我爱北京天安门'))

# Specific domain model
seg_med = pkuseg.pkuseg(model_name='medicine')
print(seg_med.cut('我爱北京天安门'))

# With part-of-speech tagging
seg_pos = pkuseg.pkuseg(postag=True)
print(seg_pos.cut('我爱北京天安门'))
```

--------------------------------

### Load and Use Custom or Pre-trained Models

Source: https://context7.com/lancopku/pkuseg-python/llms.txt

Shows how to initialize the pkuseg segmenter with custom models, pre-trained release models, and how to integrate user dictionaries with part-of-speech tagging enabled.

```python
import pkuseg

# Load local model
seg = pkuseg.pkuseg(model_name='./my_trained_model')
print(seg.cut('使用自己训练的模型进行分词'))

# Load pre-trained model
seg_ctb8 = pkuseg.pkuseg(model_name='./ctb8')
print(seg_ctb8.cut('我爱北京天安门'))

# Use with user dictionary and POS tagging
seg = pkuseg.pkuseg(model_name='./domain_model', user_dict='./domain_dict.txt', postag=True)
print(seg.cut('领域专业术语测试'))
```

--------------------------------

### Train and Fine-tune pkuseg Models

Source: https://context7.com/lancopku/pkuseg-python/llms.txt

Demonstrates how to train a new model from scratch, adjust training iterations, and perform fine-tuning on a pre-trained model using the pkuseg.train method.

```python
import pkuseg

# Train new model
pkuseg.train(trainFile='msr_training.utf8', testFile='msr_test_gold.utf8', savedir='./my_models')

# Train with custom iterations
pkuseg.train(trainFile='training.utf8', testFile='test.utf8', savedir='./models', train_iter=30)

# Fine-tune from pre-trained model
pkuseg.train(trainFile='domain_train.txt', testFile='domain_test.txt', savedir='./fine_tuned_model', train_iter=10, init_model='./pretrained')
```

--------------------------------

### pkuseg.pkuseg Initialization

Source: https://github.com/lancopku/pkuseg-python/blob/master/README.md

Initializes the word segmentation model with specific configurations such as domain selection and part-of-speech tagging.

```APIDOC
## POST /pkuseg/pkuseg

### Description
Initializes the segmentation model. Users can specify the domain model, custom user dictionaries, and whether to enable part-of-speech tagging.

### Parameters
#### Request Body
- **model_name** (string) - Optional - Path or name of the model (default: "default", options: "news", "web", "medicine", "tourism")
- **user_dict** (string) - Optional - Path to custom dictionary or "default"
- **postag** (boolean) - Optional - Whether to perform part-of-speech tagging (default: false)

### Request Example
{
  "model_name": "medicine",
  "postag": true
}

### Response
#### Success Response (200)
- **seg_object** (object) - The initialized segmentation instance.
```

--------------------------------

### Train and Test pkuseg Model

Source: https://github.com/lancopku/pkuseg-python/blob/master/readme/environment.md

Demonstrates how to train a pkuseg model using a training file and a gold standard test file, and how to perform inference on raw text. The training function saves the model to a specified directory, while the test function outputs segmentation results to a file.

```python
import pkuseg

# Train the model
pkuseg.train('msr_training.utf8', 'msr_test_gold.utf8', './models')

# Test the model
pkuseg.test('msr_test.raw', 'output.txt', user_dict=None)
```

--------------------------------

### Basic Chinese Word Segmentation

Source: https://context7.com/lancopku/pkuseg-python/llms.txt

Demonstrates how to initialize the default pkuseg segmenter and process text strings or lists of sentences into segmented word lists.

```python
import pkuseg

# 创建默认分词器（使用混合领域模型）
seg = pkuseg.pkuseg()

# 对句子进行分词
text = seg.cut('我爱北京天安门')
print(text)

# 分词多个句子
sentences = ['北京大学是中国著名高等学府', '今天天气真不错']
for sentence in sentences:
    result = seg.cut(sentence)
    print(f'{sentence} -> {result}')
```

--------------------------------

### Perform Part-of-Speech Tagging

Source: https://context7.com/lancopku/pkuseg-python/llms.txt

Illustrates how to enable part-of-speech tagging in pkuseg and map the resulting tags to human-readable labels using the provided POS_TAGS dictionary.

```python
import pkuseg

POS_TAGS = {'n': '名词', 't': '时间词', 's': '处所词', 'v': '动词', 'a': '形容词', 'nr': '人名', 'ns': '地名', 'nt': '机构名称'}

seg = pkuseg.pkuseg(postag=True)
result = seg.cut('2024年北京大学举办了人工智能研讨会')
for word, pos in result:
    pos_name = POS_TAGS.get(pos, pos)
    print(f'{word}: {pos} ({pos_name})')
```

--------------------------------

### Implementing Multiprocessing in pkuseg

Source: https://github.com/lancopku/pkuseg-python/blob/master/readme/multiprocess.md

Demonstrates the correct way to wrap pkuseg test and train functions within a multiprocessing-safe block. This ensures that the script executes correctly when using the nthread parameter.

```python
import pkuseg

if __name__ == '__main__':
    pkuseg.test('input.txt', 'output.txt', nthread=20)
    pkuseg.train('msr_training.utf8', 'msr_test_gold.utf8', './models', nthread=20)
```

--------------------------------

### Pkuseg: Fine-tuning a Pre-trained Model

Source: https://github.com/lancopku/pkuseg-python/blob/master/readme/interface.md

Fine-tunes an existing pre-trained model using new training and testing data. This allows for adapting a general model to a specific domain or dataset. It supports specifying the number of training iterations and the directory of the pre-trained model.

```python
import pkuseg

pkuseg.train('train.txt', 'test.txt', './models', train_iter=10, init_model='./pretrained')
```

--------------------------------

### Multi-processing Best Practices

Source: https://context7.com/lancopku/pkuseg-python/llms.txt

Illustrates the required use of if __name__ == '__main__' block when using multi-processing features in Python scripts.

```python
import pkuseg

if __name__ == '__main__':
    # 多进程文件分词
    pkuseg.test('input.txt', 'output.txt', nthread=20)

    # 多进程模型训练
    pkuseg.train('training.utf8', 'test.utf8', './models', nthread=20)
```

--------------------------------

### Pkuseg: Word Segmentation with Part-of-Speech Tagging

Source: https://github.com/lancopku/pkuseg-python/blob/master/readme/interface.md

Performs word segmentation and part-of-speech tagging simultaneously. The POS tags can be detailed by referring to the 'tags.txt' file. It takes a string as input and returns segmented words with their corresponding POS tags.

```python
import pkuseg

seg = pkuseg.pkuseg(postag=True) 
text = seg.cut('我爱北京天安门') 
print(text)
```

--------------------------------

### Pkuseg: Training a New Model

Source: https://github.com/lancopku/pkuseg-python/blob/master/readme/interface.md

Trains a new word segmentation model from scratch using specified training and testing files. The trained model is saved to a designated directory. This function supports UTF-8 encoding and requires words to be space-separated.

```python
import pkuseg

pkuseg.train('msr_training.utf8', 'msr_test_gold.utf8', './models')
```

--------------------------------

### Pkuseg: Fine-grained Domain Word Segmentation

Source: https://github.com/lancopku/pkuseg-python/blob/master/readme/interface.md

Performs word segmentation using a specific domain model (e.g., 'medicine'). The library automatically downloads the required model. This is recommended when the domain is known. It takes a string as input and returns a list of segmented words.

```python
import pkuseg

seg = pkuseg.pkuseg(model_name='medicine') 
text = seg.cut('我爱北京天安门') 
print(text)
```

--------------------------------

### Batch Process Files for Segmentation

Source: https://github.com/lancopku/pkuseg-python/blob/master/README.md

Use the test method to process an input file and save the segmented output to a file using multiple threads.

```python
import pkuseg

# Process input.txt and save to output.txt using 20 threads
pkuseg.test('input.txt', 'output.txt', nthread=20)
```

--------------------------------

### Domain-Specific Word Segmentation

Source: https://context7.com/lancopku/pkuseg-python/llms.txt

Shows how to load specialized pre-trained models (medicine, news, web, tourism) to improve accuracy for specific text domains.

```python
import pkuseg

# 使用医药领域模型
seg_medicine = pkuseg.pkuseg(model_name='medicine')
text = seg_medicine.cut('甲状腺功能减退症简称甲减，是甲状腺制造的甲状腺激素过少而引发的疾病')
print(text)

# 使用新闻领域模型
seg_news = pkuseg.pkuseg(model_name='news')
text = seg_news.cut('乌克兰政府正式通过最新宪法修正案')
print(text)
```

--------------------------------

### Pkuseg: Word Segmentation with Custom Dictionary

Source: https://github.com/lancopku/pkuseg-python/blob/master/readme/interface.md

Performs word segmentation while incorporating words from a user-defined dictionary file. This allows for custom vocabulary inclusion. It takes a string as input and returns a list of segmented words.

```python
import pkuseg

seg = pkuseg.pkuseg(user_dict='my_dict.txt') 
text = seg.cut('我爱北京天安门') 
print(text)
```

--------------------------------

### Pkuseg: Word Segmentation with Self-Trained Model

Source: https://github.com/lancopku/pkuseg-python/blob/master/readme/interface.md

Performs word segmentation using a self-trained model located in a specified directory (e.g., './ctb8'). This requires pre-training a model using the `pkuseg.train` function or obtaining one externally.

```python
import pkuseg

seg = pkuseg.pkuseg(model_name='./ctb8') 
text = seg.cut('我爱北京天安门') 
print(text)
```

--------------------------------

### Pkuseg: Default Word Segmentation

Source: https://github.com/lancopku/pkuseg-python/blob/master/readme/interface.md

Performs word segmentation using the default model configuration. This is recommended when the specific domain of the text is unknown. It takes a string as input and returns a list of segmented words.

```python
import pkuseg

seg = pkuseg.pkuseg() 
text = seg.cut('我爱北京天安门') 
print(text)
```

--------------------------------

### pkuseg.train Model Training

Source: https://github.com/lancopku/pkuseg-python/blob/master/README.md

Trains a new segmentation model using provided training and testing datasets.

```APIDOC
## POST /pkuseg/train

### Description
Trains a custom model based on provided training and testing files.

### Parameters
#### Request Body
- **trainFile** (string) - Required - Path to training data
- **testFile** (string) - Required - Path to testing data
- **savedir** (string) - Required - Directory to save the trained model
- **train_iter** (integer) - Optional - Number of training iterations (default: 20)

### Request Example
{
  "trainFile": "train.txt",
  "testFile": "test.txt",
  "savedir": "./my_model/"
}

### Response
#### Success Response (200)
- **status** (string) - Success message indicating the model has been saved.
```

--------------------------------

### Cite PKUSEG in BibTeX

Source: https://github.com/lancopku/pkuseg-python/blob/master/README.md

This BibTeX entry provides the standard citation format for the PKUSEG toolkit. It should be used in academic papers that utilize this library for multi-domain Chinese word segmentation.

```BibTeX
@article{pkuseg,
  author = {Luo, Ruixuan and Xu, Jingjing and Zhang, Yi and Zhang, Zhiyuan and Ren, Xuancheng and Sun, Xu},
  journal = {CoRR},
  title = {PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation.},
  url = {https://arxiv.org/abs/1906.11455},
  volume = {abs/1906.11455},
  year = 2019
}
```

--------------------------------

### Part-of-Speech Tagging

Source: https://context7.com/lancopku/pkuseg-python/llms.txt

Enables part-of-speech tagging during segmentation, returning tuples of (word, tag) to provide grammatical context.

```python
import pkuseg

# 开启词性标注功能
seg = pkuseg.pkuseg(postag=True)

# 分词并标注词性
text = seg.cut('我爱北京天安门')
print(text)
# 输出: [('我', 'r'), ('爱', 'v'), ('北京', 'ns'), ('天安门', 'ns')]
```

--------------------------------

### Custom User Dictionaries

Source: https://context7.com/lancopku/pkuseg-python/llms.txt

Configures the segmenter to use custom word lists or files to ensure specific terminology is segmented correctly.

```python
import pkuseg

# 使用用户自定义词典文件
seg = pkuseg.pkuseg(user_dict='my_dict.txt')

# 也可以传入词语列表
user_words = ['北京天安门', '机器学习', '自然语言处理']
seg = pkuseg.pkuseg(user_dict=user_words)
text = seg.cut('自然语言处理是机器学习的重要应用领域')
print(text)
```

--------------------------------

### pkuseg.test File Processing

Source: https://github.com/lancopku/pkuseg-python/blob/master/README.md

Performs word segmentation on an input file and saves the result to an output file using multi-processing.

```APIDOC
## POST /pkuseg/test

### Description
Processes a text file for word segmentation and optional part-of-speech tagging, outputting the result to a specified file.

### Parameters
#### Request Body
- **readFile** (string) - Required - Path to the input text file
- **outputFile** (string) - Required - Path to save the output
- **nthread** (integer) - Optional - Number of processes to use (default: 10)

### Request Example
{
  "readFile": "input.txt",
  "outputFile": "output.txt",
  "nthread": 20
}

### Response
#### Success Response (200)
- **status** (string) - Success message indicating file processing completion.
```

--------------------------------

### Batch File Processing

Source: https://context7.com/lancopku/pkuseg-python/llms.txt

Performs segmentation on large input files using multi-processing for improved performance.

```python
import pkuseg

# 基本文件分词（使用10个进程）
pkuseg.test('input.txt', 'output.txt', nthread=10)

# 文件分词同时进行词性标注
pkuseg.test('input.txt', 'output_with_pos.txt', postag=True, nthread=10)
```

--------------------------------

### Pkuseg: File Word Segmentation

Source: https://github.com/lancopku/pkuseg-python/blob/master/readme/interface.md

Segments words from an input text file and writes the output to another file. This function supports parallel processing using multiple threads (nthread).

```python
import pkuseg

pkuseg.test('input.txt', 'output.txt', nthread=20)
```

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