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.
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
The code from the whylogs workshop in DataTalks.Club on 29 March 2022

whylogs Workshop The code from the whylogs workshop in DataTalks.Club on 29 March 2022 whylogs - The open source standard for data logging (Don't forg

DataTalksClub 12 Sep 05, 2022
A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-t

Facebook Research 5.1k Dec 26, 2022
Code for ACL 2022 main conference paper "STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation".

STEMM: Self-learning with Speech-Text Manifold Mixup for Speech Translation This is a PyTorch implementation for the ACL 2022 main conference paper ST

ICTNLP 29 Oct 16, 2022
Resources for "Natural Language Processing" Coursera course.

Natural Language Processing course resources This github contains practical assignments for Natural Language Processing course by Higher School of Eco

Advanced Machine Learning specialisation by HSE 1.1k Jan 01, 2023
Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation

Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation Official Code Repository for the paper "Unsupervised Documen

NLP*CL Laboratory 2 Oct 26, 2021
End-to-End Speech Processing Toolkit

ESPnet: end-to-end speech processing toolkit system/pytorch ver. 1.0.1 1.1.0 1.2.0 1.3.1 1.4.0 1.5.1 1.6.0 1.7.1 1.8.1 ubuntu18/python3.8/pip ubuntu18

ESPnet 5.9k Jan 03, 2023
Beyond the Imitation Game collaborative benchmark for enormous language models

BIG-bench 🪑 The Beyond the Imitation Game Benchmark (BIG-bench) will be a collaborative benchmark intended to probe large language models, and extrap

Google 1.3k Jan 01, 2023
Sorce code and datasets for "K-BERT: Enabling Language Representation with Knowledge Graph",

K-BERT Sorce code and datasets for "K-BERT: Enabling Language Representation with Knowledge Graph", which is implemented based on the UER framework. R

Weijie Liu 834 Jan 09, 2023
Python utility library for compositing PDF documents with reportlab.

pdfdoc-py Python utility library for compositing PDF documents with reportlab. Installation The pdfdoc-py package can be installed directly from the s

Michael Gale 1 Jan 06, 2022
An open-source NLP library: fast text cleaning and preprocessing.

An open-source NLP library: fast text cleaning and preprocessing

Iaroslav 21 Mar 18, 2022
Pre-training BERT masked language models with custom vocabulary

Pre-training BERT Masked Language Models (MLM) This repository contains the method to pre-train a BERT model using custom vocabulary. It was used to p

Stella Douka 14 Nov 02, 2022
TalkNet: Audio-visual active speaker detection Model

Is someone talking? TalkNet: Audio-visual active speaker detection Model This repository contains the code for our ACM MM 2021 paper, TalkNet, an acti

142 Dec 14, 2022
Kinky furry assitant based on GPT2

KinkyFurs-V0 Kinky furry assistant based on GPT2 How to run python3 V0.py then, open web browser and go to localhost:8080 Requirements: Flask trans

Sparki 1 Jun 11, 2022
Simple Python library, distributed via binary wheels with few direct dependencies, for easily using wav2vec 2.0 models for speech recognition

Wav2Vec2 STT Python Beta Software Simple Python library, distributed via binary wheels with few direct dependencies, for easily using wav2vec 2.0 mode

David Zurow 22 Dec 29, 2022
NLP-Project - Used an API to scrape 2000 reddit posts, then used NLP analysis and created a classification model to mixed succcess

Project 3: Web APIs & NLP Problem Statement How do r/Libertarian and r/Neoliberal differ on Biden post-inaguration? The goal of the project is to see

Adam Muhammad Klesc 2 Mar 29, 2022
Code for using and evaluating SpanBERT.

SpanBERT This repository contains code and models for the paper: SpanBERT: Improving Pre-training by Representing and Predicting Spans. If you prefer

Meta Research 798 Dec 30, 2022
open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

中文开放信息抽取系统, open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

7 Nov 02, 2022
Mapping a variable-length sentence to a fixed-length vector using BERT model

Are you looking for X-as-service? Try the Cloud-Native Neural Search Framework for Any Kind of Data bert-as-service Using BERT model as a sentence enc

Han Xiao 11.1k Jan 01, 2023
Model for recasing and repunctuating ASR transcripts

Recasing and punctuation model based on Bert Benoit Favre 2021 This system converts a sequence of lowercase tokens without punctuation to a sequence o

Benoit Favre 88 Dec 29, 2022