thefuzz VS google-research

probbaly can use https://github.com/seatgeek/thefuzz

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,

Consider something like Levenshtein distance and one of it's implementations like thefuzz.

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.

You probably want a package that can do fuzzy matching. The first search result for me turned up this: https://github.com/seatgeek/thefuzz

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.

fuzzywuzzy is actually just called the thefuzz now.

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

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.

People on here will be happy to say that I do a similar thing, however my sequence length is dynamic because I also use a 2nd data structure - I'll use pretentious academic speak: I use a simple bigram LM (2-gram) for single next-word likeliness and separately a trie that models all words and phrases (so, n-gram). Not sure how many total nodes because sentence lengths vary in training data, but there are about 200,000 entry points (keys) so probably about 2-10 million total nodes in the default setup.

"Constructing 7-gram LM": They likely started with bigrams (what I use) which only tells you the next word based on 1 word given, and thought to increase accuracy by modeling out more words in a sequence, and eventually let the user (developer) pass in any amount they want to model (https://github.com/google-research/google-research/blob/5c87...). I thought of this too at first, but I actually got more accuracy (and speed) out of just keeping them as bigrams and making a totally separate structure that models out an n-gram of all phrases (e.g. could be a 24-token long sequence or 100+ tokens etc. I model it all) and if that phrase is found, then I just get the bigram assumption of the last token of the phrase. This works better when the training data is more diverse (for a very generic model), but theirs would probably outperform mine on accuracy when the training data has a lot of nearly identical sentences that only change wildly toward the end - I don't find this pattern in typical data though, maybe for certain coding and other tasks there are those patterns though. But because it's not dynamic and they make you provide that number, even a low number (any phrase longer than 2 words) - theirs will always have to do more lookup work than with simple bigrams and they're also limited by that fixed number as far as accuracy. I wonder how scalable that is - if I need to train on occasional ~100-word long sentences but also (and mostly) just ~3-word long sentences, I guess I set this to 100 and have a mostly "undefined" trie.

I also thought of the name "LMJS", theirs is "jslm" :) but I went with simply "next-token-prediction" because that's what it ultimately does as a library. I don't know what theirs is really designed for other than proving a concept. Most of their code files are actually comments and hypothetical scenarios.

I recently added a browser example showing simple autocomplete using my library: https://github.com/bennyschmidt/next-token-prediction/tree/m... (video)

And next I'm implementing 8-dimensional embeddings that are converted to normalized vectors between 0-1 to see if doing math on them does anything useful beyond similarity, right now they look like this:

  [nextFrequency, prevalence, specificity, length, firstLetter, lastLetter, firstVowel, lastVowel]

The change was literally just made: https://github.com/google-research/google-research/commit/4a...

It appears this was in response to Hacker News comments.

There are options such as Google's ScaNN that may let you go farther before needing to consider specialized databases.

https://github.com/google-research/google-research/blob/mast...

I feel it was unnecesary to create this because https://research.google/ already exists? It just seems like they want to take another URL with a "pure" domain name instead of psubdirectories, etc parts.

https://github.com/google-research/google-research/blob/mast...

You could also try some dedicated translation models like https://huggingface.co/facebook/nllb-moe-54b (or https://github.com/google-research/google-research/tree/master/madlad_400 for something smaller) and see how they do.

Google released T5X checkpoints for MADLAD-400 a couple of months ago, but nobody could figure out how to run them. Turns out the vocabulary was wrong, but they uploaded the correct one last week.