Metadata-Version: 2.1
Name: toiro
Version: 0.0.3
Summary: A comparison tool of Japanese tokenizers
Home-page: https://github.com/taishi-i/toiro
Author: Taishi Ikeda
Author-email: taishi.ikeda.0323@gmail.com
License: UNKNOWN
Download-URL: https://github.com/taishi-i/toiro/archive/0.0.3.tar.gz
Description: <p align="center"><img width="50%" src="https://github.com/taishi-i/toiro/blob/master/toiro/datadownloader/data/toiro.png" /></p>
        
        toiro
        -----
        
        [![Build Status](https://travis-ci.org/taishi-i/toiro.svg?branch=master)](https://travis-ci.org/taishi-i/toiro)
        [![PyPI](https://img.shields.io/pypi/v/toiro)](https://pypi.python.org/pypi/toiro)
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/toiro)
        
        
        Toiro is a comparison tool of Japanese tokenizers.
        - Compare the processing speed of tokenizers
        - Compare the words segmented in tokenizers
        - Compare the performance of tokenizers by benchmarking application tasks (e.g., text classification)
        
        It also provides useful functions for natural language processing in Japanese.
        - Data downloader for Japanese text corpora
        - Preprocessor of these corpora
        - Text classifier for Japanese text (e.g., SVM, BERT)
        
        <p align="center"><img width="90%" src="https://github.com/taishi-i/toiro/blob/master/toiro/datadownloader/data/toiro.gif" /></p>
        
        
        Installation
        ------------
        
        Python 3.6+ is required. You can install toiro with the following command.
        [Janome](https://github.com/mocobeta/janome) is included in the default installation.
        ```bash
        pip install toiro
        ```
        
        Adding a tokenizer to toiro
        ------------------------
        
        If you want to add a tokenizer to toiro, please install it individually.
        This is an example of adding [SudachiPy](https://github.com/WorksApplications/SudachiPy) and [nagisa](https://github.com/taishi-i/nagisa) to toiro.
        
        ```bash
        pip install sudachipy sudachidict_core
        pip install nagisa
        ```
        
        If you want to install all the tokonizers at once, please use the following command.
        ```bash
        pip install toiro[all_tokenizers]
        ```
        
        Getting started
        ---------------
        
        You can check the available tokonizers in your Python environment.
        ```python
        from toiro import tokenizers
        
        available_tokenizers = tokenizers.available_tokenizers()
        print(available_tokenizers)
        ```
        
        Toiro supports 9 different Japanese tokonizers. This is an example of adding SudachiPy and nagisa.
        ```python
        {'nagisa': {'is_available': True, 'version': '0.2.7'},
         'janome': {'is_available': True, 'version': '0.3.10'},
         'mecab-python3': {'is_available': False, 'version': False},
         'sudachipy': {'is_available': True, 'version': '0.4.9'},
         'spacy': {'is_available': False, 'version': False},
         'ginza': {'is_available': False, 'version': False},
         'kytea': {'is_available': False, 'version': False},
         'jumanpp': {'is_available': False, 'version': False},
         'sentencepiece': {'is_available': False, 'version': False}}
        ```
        
        Download the livedoor news corpus and compare the processing speed of tokenizers.
        ```python
        from toiro import tokenizers
        from toiro import datadownloader
        
        # A list of avaliable corpora in toiro
        corpora = datadownloader.available_corpus()
        print(corpora)
        #=> ['livedoor_news_corpus', 'yahoo_movie_reviews', 'amazon_reviews']
        
        # Download the livedoor news corpus and load it as pandas.DataFrame
        corpus = corpora[0]
        datadownloader.download_corpus(corpus)
        train_df, dev_df, test_df = datadownloader.load_corpus(corpus)
        texts = train_df[1]
        
        # Compare the processing speed of tokenizers
        report = tokenizers.compare(texts)
        #=> [1/3] Tokenizer: janome
        #=> 100%|███████████████████| 5900/5900 [00:07<00:00, 746.21it/s]
        #=> [2/3] Tokenizer: nagisa
        #=> 100%|███████████████████| 5900/5900 [00:15<00:00, 370.83it/s]
        #=> [3/3] Tokenizer: sudachipy
        #=> 100%|███████████████████| 5900/5900 [00:08<00:00, 696.68it/s]
        print(report)
        {'execution_environment': {'python_version': '3.7.8.final.0 (64 bit)',
          'arch': 'X86_64',
          'brand_raw': 'Intel(R) Core(TM) i7-7700K CPU @ 4.20GHz',
          'count': 8},
         'data': {'number_of_sentences': 5900, 'average_length': 37.69593220338983},
         'janome': {'elapsed_time': 9.114670515060425},
         'nagisa': {'elapsed_time': 15.873093605041504},
         'sudachipy': {'elapsed_time': 9.05256724357605}}
        
        # Compare the words segmented in tokenizers
        text = "都庁所在地は新宿区。"
        tokenizers.print_words(text, delimiter="|")
        #=>        janome: 都庁|所在地|は|新宿|区|。
        #=>        nagisa: 都庁|所在|地|は|新宿|区|。
        #=>     sudachipy: 都庁|所在地|は|新宿区|。
        ```
        
        Run toiro in Docker
        -------------------
        
        You can use all tokenizers by building a docker container from Docker Hub.
        
        ```bash
        docker run --rm -it taishii/toiro /bin/bash
        ```
        
        <details>
        <summary> How to run the Python interpreter in the Docker container </summary>
        <p>
        
        Run the Python interpreter.
        ```
        root@cdd2ad2d7092:/workspace# python3
        ```
        
        Compare the words segmented in tokenizers
        ```python
        >>> from toiro import tokenizers
        >>> text = "都庁所在地は新宿区。"
        >>> tokenizers.print_words(text, delimiter="|")
        mecab-python3: 都庁|所在地|は|新宿|区|。
               janome: 都庁|所在地|は|新宿|区|。
               nagisa: 都庁|所在|地|は|新宿|区|。
            sudachipy: 都庁|所在地|は|新宿区|。
                spacy: 都庁|所在|地|は|新宿|区|。
                ginza: 都庁|所在地|は|新宿区|。
                kytea: 都庁|所在|地|は|新宿|区|。
              jumanpp: 都庁|所在|地|は|新宿|区|。
        sentencepiece: ▁|都|庁|所在地|は|新宿|区|。
        ```
        
        </p>
        </details>
        
        Get more information about toiro
        --------------------------------
        
        Tutorials
        - [01_getting_started.ipynb](https://github.com/taishi-i/toiro/blob/master/examples/01_getting_started.ipynb)
        - [02_tutorial_tokenizers.ipynb](https://github.com/taishi-i/toiro/blob/master/examples/02_tutorial_tokenizers.ipynb)
        - [03_compare_tokenizers_with_downsteam_tasks.ipynb](https://github.com/taishi-i/toiro/blob/master/examples/03_compare_tokenizers_with_downsteam_tasks.ipynb)
        - [04_tutorial_datadownloader.ipynb](https://github.com/taishi-i/toiro/blob/master/examples/04_tutorial_datadownloader.ipynb)
        - [05_svm_vs_bert_benchmarking_application_tasks.ipynb](https://github.com/taishi-i/toiro/blob/master/examples/05_svm_vs_bert_benchmarking_application_tasks.ipynb)
        
Keywords: Japanese NLP
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: Japanese
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: Unix
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
Provides-Extra: all_tokenizers
Provides-Extra: all_classifiers
Provides-Extra: all
