Based on our record, Hugging Face seems to be a lot more popular than FuzzyWuzzy. While we know about 252 links to Hugging Face, we've tracked only 11 mentions of FuzzyWuzzy. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
HuggingFaceEmbeddings is a function that we use for converting our documents to vector which is called embedding, you can use any embedding model from huggingface, it will load the model on your local computer and create embeddings(you can use external api/service to create embeddings), then we just pass this to context and create index and store them into folder so we can reuse them and don't need to recalculate it. - Source: dev.to / 12 days ago
The only requirement for this tutorial is to have an Hugging Face account. In order to get it:. - Source: dev.to / 17 days ago
Finally, you'll need to download a compatible language model and copy it to the ~/llama.cpp/models directory. Head over to Hugging Face and search for a GGUF-formatted model that fits within your device's available RAM. I'd recommend starting with TinyLlama-1.1B. - Source: dev.to / 24 days ago
At this point, probably everyone has heard about OpenAI, GPT-4, Claude or any of the popular Large Language Models (LLMs). However, using these LLMs in a production environment can be expensive or nondeterministic regarding its results. I guess that is the downside of being good at everything; you could be better at performing one specific task. This is where HuggingFace can utilized. HuggingFace provides... - Source: dev.to / 24 days ago
New models can be added by downloading GGUF format models to the models sub-directory from https://huggingface.co/. - Source: dev.to / about 1 month 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: almost 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: over 2 years ago
Replika - Your Ai friend
Amazon Comprehend - Discover insights and relationships in text
LangChain - Framework for building applications with LLMs through composability
spaCy - spaCy is a library for advanced natural language processing in Python and Cython.
Mitsuku - Browser-based, AI chat bot.
Microsoft Bing Spell Check API - Enhance your apps with the Bing Spell Check API from Microsoft Azure. The spell check API corrects spelling mistakes as users are typing.