thefuzz
Fuzzy String Matching in Python (by seatgeek)
Slavic-BERT-NER
Shared BERT model for 4 languages of Bulgarian, Czech, Polish and Russian. Slavic NER model. (by deeppavlov)
thefuzz | Slavic-BERT-NER | |
---|---|---|
10 | 1 | |
2,479 | 72 | |
3.5% | - | |
6.2 | 0.0 | |
2 months ago | over 2 years ago | |
Python | Python | |
MIT License | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
thefuzz
Posts with mentions or reviews of thefuzz.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-11.
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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.
Slavic-BERT-NER
Posts with mentions or reviews of Slavic-BERT-NER.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-10-06.
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Extracting information from scanned PDF docs, is it possible?
Extracting the required data from the string. This is very specific for each use case and most likely my use case won't intersect with yours, but in case it does, I'm trying to detect names of people and companies from the text, for which I'm using the Slavic NER model (note that my PDFs are not in english).
What are some alternatives?
When comparing thefuzz and Slavic-BERT-NER you can also consider the following projects:
fuzzywuzzy - Fuzzy String Matching in Python
RapidFuzz - Rapid fuzzy string matching in Python using various string metrics
xonsh - :shell: Python-powered, cross-platform, Unix-gazing shell.
google-research - Google Research
fzf - :cherry_blossom: A command-line fuzzy finder