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Released:
Functions to preprocess and normalize text.
Project description
User-generated content on the Web and in social media is often dirty. Preprocess your scraped data with clean-text to create a normalized text representation. For instance, turn this corrupted input:
into this clean output:
clean-text uses ftfy, unidecode and numerous hand-crafted rules, i.e., RegEx.
Installation
To install the GPL-licensed package unidecode alongside:
You may want to abstain from GPL:
NB: This package is named clean-text and not cleantext.
If unidecode is not available, clean-text will resort to Python's unicodedata.normalize for transliteration.Transliteration to closest ASCII symbols involes manually mappings, i.e., ê to e.unidecode's mapping is superiour but unicodedata's are sufficent.However, you may want to disable this feature altogether depending on your data and use case.
To make it clear: There are inconsistencies between processing text with or without unidecode.
Usage
Carefully choose the arguments that fit your task. The default parameters are listed above.
You may also only use specific functions for cleaning. For this, take a look at the source code.
So far, only English and German are fully supported. It should work for the majority of western languages. If you need some special handling for your language, feel free to contribute. 🙃
Development
Install and use poetry.
Contributing
If you have a question, found a bug or want to propose a new feature, have a look at the issues page.
Pull requests are especially welcomed when they fix bugs or improve the code quality.
If you don't like the output of clean-text, consider adding a test with your specific input and desired output.
Related Work
Acknowledgements
Built upon the work by Burton DeWilde for Textacy.
License
Apache
Sponsoring
This work was created as part of a project that was funded by the German Federal Ministry of Education and Research.
Release historyRelease notifications | RSS feed
0.3.0
0.2.1
0.2.0
0.1.1
Released:
Functions to preprocess and normalize text.
Project description
User-generated content on the Web and in social media is often dirty. Preprocess your scraped data with clean-text to create a normalized text representation. For instance, turn this corrupted input:
into this clean output:
clean-text uses ftfy, unidecode and numerous hand-crafted rules, i.e., RegEx.
Installation
To install the GPL-licensed package unidecode alongside:
You may want to abstain from GPL:
NB: This package is named clean-text and not cleantext.
If unidecode is not available, clean-text will resort to Python's unicodedata.normalize for transliteration.Transliteration to closest ASCII symbols involes manually mappings, i.e., ê to e.unidecode's mapping is superiour but unicodedata's are sufficent.However, you may want to disable this feature altogether depending on your data and use case.
To make it clear: There are inconsistencies between processing text with or without unidecode.
Usage
Carefully choose the arguments that fit your task. The default parameters are listed above.
You may also only use specific functions for cleaning. For this, take a look at the source code.
So far, only English and German are fully supported. It should work for the majority of western languages. If you need some special handling for your language, feel free to contribute. 🙃
Development
Install and use poetry.
Contributing
If you have a question, found a bug or want to propose a new feature, have a look at the issues page.
Pull requests are especially welcomed when they fix bugs or improve the code quality.
If you don't like the output of clean-text, consider adding a test with your specific input and desired output.
Related Work
Acknowledgements
Built upon the work by Burton DeWilde for Textacy.
License
Apache
Sponsoring
This work was created as part of a project that was funded by the German Federal Ministry of Education and Research.
Release historyRelease notifications | RSS feed
0.3.0
0.2.1
0.2.0
0.1.1
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
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|---|---|---|---|---|
| Filename, size clean_text-0.3.0-py3-none-any.whl (9.6 kB) | File type Wheel | Python version py3 | Upload date | Hashes |
| Filename, size clean-text-0.3.0.tar.gz (9.3 kB) | File type Source | Python version None | Upload date | Hashes |
Hashes for clean_text-0.3.0-py3-none-any.whl
Clean Text 7 9 Pro Max
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| BLAKE2-256 | 78307013e9bf37e00ad81406c771e8f5b071c624b8ab27a7984cd9b8434bed4f |
Hashes for clean-text-0.3.0.tar.gz
| Algorithm | Hash digest |
|---|---|
| SHA256 | 648de7c65d474c65c36ec7d1f19e815c942d67bde2db4894d3930afb75da769e |
| MD5 | 54b02f17a3db438ddd5b8680b3a5b06a |
| BLAKE2-256 | 67eab180c5f799d5a9a954aa9832333d472fc70a8f61454cc7ac92c27fbb32ca |
