Overall, this work contributes to the ongoing progress in bridging the gap between simulation and reality in the field of robotics, offering insights into the potential of Deep Reinforcement Learning for synthesizing sophisticated and adaptable robotic behaviors. - Source: dev.to / 2 months ago

That’s why I launched AIModels.fyi — a search engine for AI models. The site offers the ability to sort and pick the best-suited model for your project. Plus, it features a tag system for filtering models based on tasks (such as Text-to-Image or Image-to-Video) for a more focused search. - Source: dev.to / 3 months ago

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. Source: over 1 year ago

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. Source: over 1 year ago

It's now known as "thefuzz", see https://github.com/seatgeek/fuzzywuzzy. Source: about 2 years ago

You can have a look at this library to use fuzzy search instead of looking for plaintext muck: https://github.com/seatgeek/fuzzywuzzy. Source: over 2 years ago

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. Source: almost 3 years ago