Fixes mojibake and other glitches in Unicode text, after the fact.

Overview

ftfy: fixes text for you

Travis PyPI package Docs

>>> print(fix_encoding("(ง'⌣')ง"))
(ง'⌣')ง

Full documentation: https://ftfy.readthedocs.org

Testimonials

  • “My life is livable again!” — @planarrowspace
  • “A handy piece of magic” — @simonw
  • “Saved me a large amount of frustrating dev work” — @iancal
  • “ftfy did the right thing right away, with no faffing about. Excellent work, solving a very tricky real-world (whole-world!) problem.” — Brennan Young
  • “Hat mir die Tage geholfen. Im Übrigen bin ich der Meinung, dass wir keine komplexen Maschinen mit Computern bauen sollten solange wir nicht einmal Umlaute sicher verarbeiten können. :D” — Bruno Ranieri
  • “I have no idea when I’m gonna need this, but I’m definitely bookmarking it.” — /u/ocrow
  • “9.2/10” — pylint

Developed at Luminoso

Luminoso makes groundbreaking software for text analytics that really understands what words mean, in many languages. Our software is used by enterprise customers such as Sony, Intel, Mars, and Scotts, and it's built on Python and open-source technologies.

We use ftfy every day at Luminoso, because the first step in understanding text is making sure it has the correct characters in it!

Luminoso is growing fast and hiring. If you're interested in joining us, take a look at our careers page.

What it does

ftfy fixes Unicode that's broken in various ways.

The goal of ftfy is to take in bad Unicode and output good Unicode, for use in your Unicode-aware code. This is different from taking in non-Unicode and outputting Unicode, which is not a goal of ftfy. It also isn't designed to protect you from having to write Unicode-aware code. ftfy helps those who help themselves.

Of course you're better off if your input is decoded properly and has no glitches. But you often don't have any control over your input; it's someone else's mistake, but it's your problem now.

ftfy will do everything it can to fix the problem.

Mojibake

The most interesting kind of brokenness that ftfy will fix is when someone has encoded Unicode with one standard and decoded it with a different one. This often shows up as characters that turn into nonsense sequences (called "mojibake"):

  • The word schön might appear as schön.
  • An em dash () might appear as —.
  • Text that was meant to be enclosed in quotation marks might end up instead enclosed in “ and â€<9d>, where <9d> represents an unprintable character.

ftfy uses heuristics to detect and undo this kind of mojibake, with a very low rate of false positives.

This part of ftfy now has an unofficial Web implementation by simonw: https://ftfy.now.sh/

Examples

fix_text is the main function of ftfy. This section is meant to give you a taste of the things it can do. fix_encoding is the more specific function that only fixes mojibake.

Please read the documentation for more information on what ftfy does, and how to configure it for your needs.

>>> print(fix_text('This text should be in “quotesâ€\x9d.'))
This text should be in "quotes".

>>> print(fix_text('ünicode'))
ünicode

>>> print(fix_text('Broken text&hellip; it&#x2019;s flubberific!',
...                normalization='NFKC'))
Broken text... it's flubberific!

>>> print(fix_text('HTML entities &lt;3'))
HTML entities <3

>>> print(fix_text('<em>HTML entities in HTML &lt;3</em>'))
<em>HTML entities in HTML &lt;3</em>

>>> print(fix_text('\001\033[36;44mI&#x92;m blue, da ba dee da ba '
...               'doo&#133;\033[0m', normalization='NFKC'))
I'm blue, da ba dee da ba doo...

>>> print(fix_text('LOUD NOISES'))
LOUD NOISES

>>> print(fix_text('LOUD NOISES', fix_character_width=False))
LOUD NOISES

Installing

ftfy is a Python 3 package that can be installed using pip:

pip install ftfy

(Or use pip3 install ftfy on systems where Python 2 and 3 are both globally installed and pip refers to Python 2.)

If you're on Python 2.7, you can install an older version:

pip install 'ftfy<5'

You can also clone this Git repository and install it with python setup.py install.

Who maintains ftfy?

I'm Robyn Speer ([email protected]). I develop this tool as part of my text-understanding company, Luminoso, where it has proven essential.

Luminoso provides ftfy as free, open source software under the extremely permissive MIT license.

You can report bugs regarding ftfy on GitHub and we'll handle them.

Citing ftfy

ftfy has been used as a crucial data processing step in major NLP research.

It's important to give credit appropriately to everyone whose work you build on in research. This includes software, not just high-status contributions such as mathematical models. All I ask when you use ftfy for research is that you cite it.

ftfy has a citable record on Zenodo. A citation of ftfy may look like this:

Robyn Speer. (2019). ftfy (Version 5.5). Zenodo.
http://doi.org/10.5281/zenodo.2591652

In BibTeX format, the citation is::

@misc{speer-2019-ftfy,
  author       = {Robyn Speer},
  title        = {ftfy},
  note         = {Version 5.5},
  year         = 2019,
  howpublished = {Zenodo},
  doi          = {10.5281/zenodo.2591652},
  url          = {https://doi.org/10.5281/zenodo.2591652}
}
Comments
  • Bump certifi from 2021.10.8 to 2022.12.7

    Bump certifi from 2021.10.8 to 2022.12.7

    Bumps certifi from 2021.10.8 to 2022.12.7.

    Commits

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    You can disable automated security fix PRs for this repo from the Security Alerts page.

    dependencies 
    opened by dependabot[bot] 0
  • Performance improvements using google-re2. 2 times faster to run fix_text()

    Performance improvements using google-re2. 2 times faster to run fix_text()

    Hi, thanks for the great lib!

    In our real time inference server, we are using ftfy to clean inputs coming from users. We noticed that processing time can be huge with a lot of text. So I run this little experiment to usegoogle-re2 which is a regex engine optimized for performance. On my test file of 10000 lines, I was able to clean the text, 2 times faster. On a run of 10, I'm getting 16.15 seconds with vanilla ftfy and 8.71 seconds with the optimizations made in this PR.

    As is, this PR is not mergable, its implies a big change for the lib. I think it should be better to have a way of choosing regex engine. If you are interested in merging it, I can make the necessary changes. I'm publishing it just for you and the community to know it's possible and what the expected outcomes can be. Of course, I made sure than all the tests are green.

    Anyone can test it by installing this branch pip install git+https://@github.com/ablanchard/[email protected]

    Notes on the PR :

    • re.VERBOSE is not supported by google-re2. To keep comments and line returns, I process it by "hand" using a regex. Bit of a hack but it works.
    • lookahead and lookbehind arenot supported by google-re2 so I splited the UTF8 detector and the a grave regex in 2 separate regexes in order to keep the same behavior. Meaning that UTF8_DETECTOR_RE.search() doesn't return the same results as before so you have to call the method utf8_detector(). The same idea goes for the sub method.
    • By default google-re2 uses utf8 for encoding regexes so to use binary string you have to pass options=LATIN_OPTIONS
    • I didn't migrate the surrogates for utf-16. In my understanding,it's not supported by google-re2. So I left it as it was.

    PS: Code used for the benchmark:

    import time
    import ftfy
    import pandas as pd
    import sys
    
    df = pd.read_csv(sys.argv[1])
    texts = df['input_text'].tolist()
    start_time = time.time()
    res = [ftfy.fix_text(text) for text in texts]
    print(time.time() - start_time)
    
    opened by ablanchard 0
  • Restore Python 36 support

    Restore Python 36 support

    Hi! There is not much that prohibits to still support Python 3.6 which is still widely supported on Linux distros. This PE re-enables Python 3.6 support I also removed some upper bounds on deps to avoid some issues, as highlighted in https://iscinumpy.dev/post/bound-version-constraints/ Thanks for your kind consideration!

    opened by pombredanne 0
  • İ and Ä« not detected as mojibake

    İ and ī not detected as mojibake

    Hi @rspeer. Many thanks for creating and maintaining FTFY! We're using it at Sectigo to help prevent mojibake from finding its way into string fields in the digital certificates that we issue. We've noticed a couple of mojibake sequences that FTFY doesn't currently detect and fix:

    Desired behaviour:

    $ echo "İstanbul" | iconv -t WINDOWS-1252
    İstanbul
    $ echo "Rīga" | iconv -t WINDOWS-1252
    Rīga
    

    Current FTFY behaviour:

    $ echo "İstanbul" | ftfy
    İstanbul
    $ echo "Rīga" | ftfy
    Rīga
    

    Would it be possible to make FTFY handle these cases?

    opened by robstradling 0
  • On the wish list:

    On the wish list: "Pyreneeu00ebn" being explained as "Pyreneeën 71"

    A while ago I blogged about "Pyreneeën 71" on a web-site being incorrectly represented as "Pyreneeu00ebn".

    Basically the Unicode code point U+00EB : LATIN SMALL LETTER E WITH DIAERESIS is being represented as u00eb.

    Is this something that ftfy could potentially recognise?

    Right now It does not:

    >>> ftfy.fix_and_explain("Pyreneeu00ebn")
    ExplainedText(text='Pyreneeu00ebn', explanation=[])
    
    opened by jpluimers 2
  • Any idea which encoding failure could cause

    Any idea which encoding failure could cause "beëindiging" to be printed in a letter as "beᅵindiging"?

    opened by jpluimers 0
Releases(v6.0.3)
  • v6.0.3(Aug 23, 2021)

    Updates in 6.0.x:

    • New function: ftfy.fix_and_explain() can describe all the transformations that happen when fixing a string. This is similar to what ftfy.fixes.fix_encoding_and_explain() did in previous versions, but it can fix more than the encoding.
    • fix_and_explain() and fix_encoding_and_explain() are now in the top-level ftfy module.
    • Changed the heuristic entirely. ftfy no longer needs to categorize every Unicode character, but only characters that are expected to appear in mojibake.
    • Because of the new heuristic, ftfy will no longer have to release a new version for every new version of Unicode. It should also run faster and use less RAM when imported.
    • The heuristic ftfy.badness.is_bad(text) can be used to determine whether there appears to be mojibake in a string. Some users were already using the old function sequence_weirdness() for that, but this one is actually designed for that purpose.
    • Instead of a pile of named keyword arguments, ftfy functions now take in a TextFixerConfig object. The keyword arguments still work, and become settings that override the defaults in TextFixerConfig.
    • Added support for UTF-8 mixups with Windows-1253 and Windows-1254.
    • Overhauled the documentation: https://ftfy.readthedocs.org
    • Requires Python 3.6 or later.
    Source code(tar.gz)
    Source code(zip)
  • v5.5.1(Mar 12, 2019)

Owner
Luminoso Technologies, Inc.
Luminoso Technologies, Inc.
本插件是pcrjjc插件的重置版,可以独立于后端api运行

pcrjjc2 本插件是pcrjjc重置版,不需要使用其他后端api,但是需要自行配置客户端 本项目基于AGPL v3协议开源,由于项目特殊性,禁止基于本项目的任何商业行为 配置方法 环境需求:.net framework 4.5及以上 jre8 别忘了装jre8 别忘了装jre8 别忘了装jre8

132 Dec 26, 2022
Datasets of Automatic Keyphrase Extraction

This repository contains 20 annotated datasets of Automatic Keyphrase Extraction made available by the research community. Following are the datasets and the original papers that proposed them. If yo

LIAAD - Laboratory of Artificial Intelligence and Decision Support 163 Dec 23, 2022
Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing

Trankit: A Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing Trankit is a light-weight Transformer-based Pyth

652 Jan 06, 2023
Share constant definitions between programming languages and make your constants constant again

Introduction Reconstant lets you share constant and enum definitions between programming languages. Constants are defined in a yaml file and converted

Natan Yellin 47 Sep 10, 2022
A very simple framework for state-of-the-art Natural Language Processing (NLP)

A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends. Flair is: A powerful NLP library. Flair allo

flair 12.3k Jan 02, 2023
Control the classic General Instrument SP0256-AL2 speech chip and AY-3-8910 sound generator with a Raspberry Pi and this Python library.

GI-Pi Control the classic General Instrument SP0256-AL2 speech chip and AY-3-8910 sound generator with a Raspberry Pi and this Python library. The SP0

Nick Bild 8 Dec 15, 2021
This is the main repository of open-sourced speech technology by Huawei Noah's Ark Lab.

Speech-Backbones This is the main repository of open-sourced speech technology by Huawei Noah's Ark Lab. Grad-TTS Official implementation of the Grad-

HUAWEI Noah's Ark Lab 295 Jan 07, 2023
Creating a Feed of MISP Events from ThreatFox (by abuse.ch)

ThreatFox2Misp Creating a Feed of MISP Events from ThreatFox (by abuse.ch) What will it do? This will fetch IOCs from ThreatFox by Abuse.ch, convert t

17 Nov 22, 2022
PyKaldi is a Python scripting layer for the Kaldi speech recognition toolkit.

PyKaldi is a Python scripting layer for the Kaldi speech recognition toolkit. It provides easy-to-use, low-overhead, first-class Python wrappers for t

922 Dec 31, 2022
Higher quality textures for the Metal Gear Solid series.

Metal Gear Solid: HD Textures Higher quality textures for the Metal Gear Solid series. The goal is to maximize the quality of assets that the engine w

Samantha 6 Dec 06, 2022
Simple text to phones converter for multiple languages

Phonemizer -- foʊnmaɪzɚ The phonemizer allows simple phonemization of words and texts in many languages. Provides both the phonemize command-line tool

CoML 762 Dec 29, 2022
ChatterBot is a machine learning, conversational dialog engine for creating chat bots

ChatterBot ChatterBot is a machine-learning based conversational dialog engine build in Python which makes it possible to generate responses based on

Gunther Cox 12.8k Jan 03, 2023
Code for PED: DETR For (Crowd) Pedestrian Detection

Code for PED: DETR For (Crowd) Pedestrian Detection

36 Sep 13, 2022
문장단위로 분절된 나무위키 데이터셋. Releases에서 다운로드 받거나, tfds-korean을 통해 다운로드 받으세요.

Namuwiki corpus 문장단위로 미리 분절된 나무위키 코퍼스. 목적이 LM등에서 사용하기 위한 데이터셋이라, 링크/이미지/테이블 등등이 잘려있습니다. 문장 단위 분절은 kss를 활용하였습니다. 라이선스는 나무위키에 명시된 바와 같이 CC BY-NC-SA 2.0

Jeong Ukjae 16 Apr 02, 2022
A CSRankings-like index for speech researchers

Speech Rankings This project mimics CSRankings to generate an ordered list of researchers in speech/spoken language processing along with their possib

Mutian He 19 Nov 26, 2022
A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).

Splitter ⠀⠀ A PyTorch implementation of Splitter: Learning Node Representations that Capture Multiple Social Contexts (WWW 2019). Abstract Recent inte

Benedek Rozemberczki 201 Nov 09, 2022
Th2En & Th2Zh: The large-scale datasets for Thai text cross-lingual summarization

Th2En & Th2Zh: The large-scale datasets for Thai text cross-lingual summarization 📥 Download Datasets 📥 Download Trained Models INTRODUCTION TH2ZH (

Nakhun Chumpolsathien 5 Jan 03, 2022
Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.

TextBlob: Simplified Text Processing Homepage: https://textblob.readthedocs.io/ TextBlob is a Python (2 and 3) library for processing textual data. It

Steven Loria 8.4k Dec 26, 2022
基于“Seq2Seq+前缀树”的知识图谱问答

KgCLUE-bert4keras 基于“Seq2Seq+前缀树”的知识图谱问答 简介 博客:https://kexue.fm/archives/8802 环境 软件:bert4keras=0.10.8 硬件:目前的结果是用一张Titan RTX(24G)跑出来的。 运行 第一次运行的时候,会给知

苏剑林(Jianlin Su) 65 Dec 12, 2022
C.J. Hutto 3.8k Dec 30, 2022