fuzzywuzzy
thefuzz
fuzzywuzzy | thefuzz | |
---|---|---|
20 | 10 | |
9,067 | 2,479 | |
0.0% | 2.7% | |
0.0 | 6.2 | |
about 1 year ago | 2 months ago | |
Python | Python | |
GNU General Public License v2.0 only | MIT License |
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fuzzywuzzy
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Need help solving a subtitles problem. The logic seems complex
Do fuzzy matching (something like fuzzywuzzy maybe) to see if the the words line up (allowing for wrong words). You'll need to work out how to use scoring to work out how well aligned the two lists are.
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Thanks to this sub, we now have an Anki deck for Persona 5 Royal. Spreadsheet with Jp and Eng side by side too.
Convert the original lines to full furigana and do a fuzzy match. (For reference, the original line is 貴方がこれまでに得てきた力、存分に発揮してくださいね。) You can do a regional search using the initial scene data (E60) first, and if the confidence is low, go for a slower full search.
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Fuzzy search
It's now known as "thefuzz", see https://github.com/seatgeek/fuzzywuzzy
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import fuzzywuzzy
fuzzywuzzy is actually just called the thefuzz now.
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I made a bot that stops muck chains, here are the phrases that he looks for to flag the comment as a muck comment. Are there any muck forms I forgot about?
You can have a look at this library to use fuzzy search instead of looking for plaintext muck: https://github.com/seatgeek/fuzzywuzzy
- Test if two strings are similar?
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How would you approach this
To deal with comparing the string, I found FuzzyWuzzy ratio function that is returning a score of how much the strings are similar from 0-100.
- [D] Matching Records that "don't Exactly Match"
- Text Detection
- FuzzyWuzzy: Fuzzy String Matching in Python
thefuzz
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File Path Issue
probbaly can use https://github.com/seatgeek/thefuzz
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[Flask] Best / Modern approaches for fuzzy name searching?
Check out https://github.com/seatgeek/thefuzz. It basically provides different methods that take two strings and return a score between 0 and 100 indicating how similar they are. For instance,
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How to identify duplicate crawl data?
Consider something like Levenshtein distance and one of it's implementations like thefuzz.
- Find best match between a reference string and a list of strings
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NLP: How to rebuild a name from letters
The problem you are solving is most commonly called “fuzzy string matching”. There are a bunch of algorithms for it (some of which are described in this thread) depending on your specific requirements. I’d start with an existing fuzzy string matching library (e.g. thefuzz, for python) and calculate matches between your input letter cases and your list of names. This sounds pretty reasonable to do fast since fuzzy string matching is commonly used in text editors to make it easier to find files. If you start with a fuzzy string matching library, I wouldn’t worry about asymptomatic complexity until you actually see a performance problem.
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Is there a Python library that lets me search through a list like searching with a search engine?
You probably want a package that can do fuzzy matching. The first search result for me turned up this: https://github.com/seatgeek/thefuzz
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How good is my summary?
Having said that, you can use the Levenshtein distance to compute how many "edits" (substitutions, deletions, insertions) the generated summary is away from the original abstract. The package TheFuzz implements this concept in Python. For example fuzz.ratio(text1, text2) will give you a similarity score.
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import fuzzywuzzy
fuzzywuzzy is actually just called the thefuzz now.
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Bad word filter?
It sounds like what you're looking for is "fuzzy string matching," which is not just checking if a string matches another exactly, but defining a way to measure "how close" a string is to another. Luckily, it looks like there's a good Python library for that already: https://github.com/seatgeek/thefuzz
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Extracting information from scanned PDF docs, is it possible?
Finally, even though Tesseract's output is usually very nice, it can sometime make a mistake. Again, this is case-specific, and if you're extracting for example numbers, it will be very hard to check for errors, but since I'm extracting names, I'm capable of fuzzy comparing the names detected by Slavic NER to a database of names that I have. I do this fuzzy matching with thefuzz library, and in cases I find a very high match with one of the names in my database, I simply fix the error by taking the name from there.
What are some alternatives?
jellyfish - 🪼 a python library for doing approximate and phonetic matching of strings.
RapidFuzz - Rapid fuzzy string matching in Python using various string metrics
Levenshtein - The Levenshtein Python C extension module contains functions for fast computation of Levenshtein distance and string similarity
Slavic-BERT-NER - Shared BERT model for 4 languages of Bulgarian, Czech, Polish and Russian. Slavic NER model.
ftfy - Fixes mojibake and other glitches in Unicode text, after the fact.
xonsh - :shell: Python-powered, cross-platform, Unix-gazing shell.
TextDistance - 📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.
google-research - Google Research
chardet - Python character encoding detector
fzf - :cherry_blossom: A command-line fuzzy finder
pyfiglet - An implementation of figlet written in Python