hojichar 0.5.3

Text preprocessing management system.

HojiChar

Official docs: https://hojichar.github.io/HojiChar/hojichar.html

Background and what is for HojiChar

Text preprocessing is far from a one-size-fits-all process. Depending on the data source and the specific task at hand, various steps including normalization, noise removal, and filtering may be necessary. Not all texts require the same level of preprocessing. For instance, relatively clean texts may only need minimal filtering, while "dirtier" sources like Common Crawl data often require more thorough processing. As a result, the preprocessing profile has to be tailored to each specific domain.

Many preprocessing operations can be viewed as filters, taking string as input, applying a transformation, and outputting the processed string. Even though these operations might seem straightforward individually, managing them in a multi-layered, efficient manner can be challenging.

Inspired by torchvision.transforms and iver56/audiomentations, HojiChar addresses these challenges. It enables users to define each text processing step as a class inheriting from hojichar.Filter and use hojichar.Compose to chain them together into a single filter. By writing out the Compose recipe as a profile, the preprocessing process for a specific domain's text can be made portable. Moreover, Compose automatically logs various metrics for each filter, such as byte changes, processing time, and number of rejected texts. This allows users to assess the validity of each operation and consider trade-offs between computation time and performance.

While there are other text normalization tools available, most are designed to perform a specific set of operations. Text preprocessing, despite its importance, is often considered a mundane task compared to machine learning or artificial intelligence tasks. As a result, many existing solutions can be ad hoc, poorly maintained, or inadequately tested. Recognizing these issues, we developed HojiChar as a robust tool for configuring text preprocessing.

Install

pip install hojichar

Defining a Compose Object

The Compose class in HojiChar allows you to create a sequence of text processing filters.

from hojichar import Compose, document_filters

cleaner = Compose([
    document_filters.JSONLoader(key="text"),
    document_filters.AcceptJapanese(),
    document_filters.DocumentLengthFilter(min_doc_len=0,max_doc_len=1000),
    document_filters.ExampleHojiChar(),
    document_filters.JSONDumper()
])

When a Compose object is called, it accepts a string and returns the processed string.

>>> cleaner('{"text": "こんにちは、"}')
{"text": "こんにちは、<hojichar>"}

The filter pipeline above accomplishes the following steps:

1. Extracts the value from the 'text' key in the JSON object.
2. Discards the string if it's not in Japanese.
3. Rejects any text shorter than 0 characters or longer than 1000 characters.
4. Appends <hojichar> to the string.
5. Outputs the processed string as JSON with the key "text".

The filters used in the pipeline are predefined filters found in hojichar.filters.

While HojiChar provides some fundamental text processing filters and plans to add more in the future, users can also define their custom filters.

User-defined Filters

A filter composing a Compose object is a class that inherits the Filter class and implements the text processing within the apply function.

from hojichar.core.filter_interface import Filter

class YourFilter(Filter):
    def apply(self, document):
        text = document.text
        """
        Write your text transformation...
        """
        document.text = text
        return document

The apply method accepts a hojichar.Document type as an argument and returns it after the transformations. The Document is a class that encapsulates a string.

Reject documents

• The hojichar.Document has an is_rejected attribute. If a filter sets this flag to True, Compose will discard the document during processing.

Definition of __init__ for custom filter

When creating a user-defined class and applying a custom constructor, make sure to initialize the parent class.

class YourFilter(Filter):
    def __init__(self, your_param, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)
        self.your_param = your_param

    def apply(self, document):
        text = document.text
        text = process(text, self.your_param)
        document.text = text
        return document

This is because The Filter class implicitly has several arguments, one of which is p.

cleaner = Compose([
    document_filters.JSONLoader(key="text"),
    document_filters.AcceptJapanese(p=0.5),
    document_filters.JSONDumper()
])

The p argument passed to the document_filters.AcceptJapanese constructor determines the probability of applying the filter; with a probability of 1-p, it acts as an identity function. This behavior is defined in the parent class hojichar.Filter.

Additional Notes on Compose

• Even though the behavior of a Compose object when called is a text-in, text-out function, Compose itself also inherits from the Filter class. Therefore, applying the apply method to a Compose object results in hojihcar.Document class being used as input and output.
• You can access various statistics regarding the processing performed by Compose through Compose.statistics, which returns a dictionary.

It might be helpful to add examples demonstrating the use of `Compose

CLI tool and preprocessing profile

• HojiChar provides CLI tools for text preprocess pipeline.

• User defines a series of preprocessing into a python file as profile.

• Example:

  cat <your_text.jsonl> | hojichar -p your_preprocessing_profile.py -o your_text_preprocessed.jsonl
  

• hojichar --help

  usage: hojichar [-h] --profile <your_filter.py> [--output OUTPUT] [--dump-stats <path to stats.json>] [--exit-on-error] [--args ARGS [ARGS ...]]
  
  options:
    -h, --help            show this help message and exit
    --profile <your_filter.py>, -p <your_filter.py>
                          Path to a Python file that implements your custom filter. hojichar.Compose must be defined as FILTER variable in the file.
    --output OUTPUT, -o OUTPUT
                          Output file path. If not given, stdout is used.
    --dump-stats <path to stats.json>
                          Dump statistics to a file.
    --exit-on-error       Exit if an exception occurs during filtering. Useful for debugging custom filters.
    --args ARGS [ARGS ...]
                          Argument for the profile which receives arguments.
  

Definition of Profile

• HojiChar CLI receives a series of preprocessing as a profile.
• The preprocessing profile is provided as a Python file. Two patterns of the file are allowed.
• hojichar.utils.load_compose.load_compose() loads these profile.

FILTER profile

• hojichar.Compose must be defined as FILTER variable.

• Example.

  import json
  
  from hojichar import Compose, Filter
  from hojichar.filters.document_filters import ExampleHojiChar, JSONLoader
  
  
  class JSONDumper(Filter):
      def apply(self, document):
          text = document.text
          document.text = json.dumps({"text": text}, ensure_ascii=False)
          return document
  
  # FILTER must define Compose object.
  FILTER = Compose(
      [
          JSONLoader(),
          ExampleHojiChar(),
          JSONDumper(),
      ]
  )
  

  • Pass the texts to the filter you have defined using a pipe as follows.

    cat <your_file> | hojichar -p example_profile.py
    

• hojichar.utils.load_compose.load_filter_from_file() loads this type of profile.

FACTORY profile

• A callable function that returns hojichar.Compose must be defined as FACTORY variable.

• The callable can receive arguments. In this way, parameters can be passed to the profile.

  • Some kinds of value are not preferred to static. For example, random seeds and some flags modify the behavior of a filter, etc
  • FACTORY provides a mechanism to pass those values as arguments to the preprocessing.

• Example.

  import json
  
  from hojichar import Compose, Filter
  from hojichar.filters.document_filters import JSONLoader
  
  
  class AddSomething(Filter): #  Concat some value after every document.
      def __init__(self, something: str, *args, **kwargs) -> None:
          self.something = something
  
      def apply(self, document):
          text = document.text + self.something
          document.text = text
          return document
  
  class JSONDumper(Filter):
      def apply(self, document):
          text = document.text
          document.text = json.dumps({"text": text}, ensure_ascii=False)
          return document
  
  
  def callback(something):
      return Compose(
          [
              JSONLoader(),
              AddSomething(something),
              JSONDumper(),
          ]
      )
  
  # FACTORY must be callable which returns Compose object.
  FACTORY = callback
  

  • Using FACTORY profile with arguments in CLI.

    cat <your_file> | hojichar -p example_profile.py --args arg1 arg2
    

• hojichar.utils.load_compose.load_parametrized_filter_from_file() or load_factory_from_file loads this type of profile.

For Developers

Local Installation with Poetry

Requirements: python >= 3.8, poetry >= 1.2

To install the package, run the following commands:

git clone https://github.com/HojiChar/HojiChar.git
cd HojiChar
poetry install

For installing development-related packages, you can run:

poetry install --extras "dev lint test"

Testing

You can run the tests with:

pytest --doctest-modules .

This will execute both mypy and pytest tests.

Linting can be done using:

poetry run task lint

And for formatting:

poetry run task format