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Show HN: Semantic Grep – A Word2Vec-powered search tool

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Show HN: Semantic Grep – A Word2Vec-powered search tool

#1
Much improved new version. Search for words similar to the query. For example, "death" will find "death", "dying", "dead", "killing"... Incredibly useful for exploring large text datasets where exact matches are too restrictive.

Show HN: Semantic Grep – A Word2Vec-powered search tool
github.com

Re: Show HN: Semantic Grep – A Word2Vec-powered search tool

#6
I might have a work use case for which this would be perfect.

Having no experience with word2vec, some reference performance numbers would be great. If I have one million PDF pages, how long is that going to take to encode? How long will it take to search? Is it CPU only or will I get a huge performance benefit if I have a GPU?

Re: Show HN: Semantic Grep – A Word2Vec-powered search tool

#7
post #6

I might have a work use case for which this would be perfect. Having no experience with word2vec, some reference performance numbers would be great. If I have one million PDF pages, how long is that going to take to encode? How long will it take to search? Is it CPU only or will I get a huge performance benefit if I have a GPU?

As someone working extensively with word2vec: I would recommend to set up Elasticsearch. It has support for vector embeddings, so you can process your PDF documents once, write the word2vec embeddings and PDF metadata into an index, and search that in milliseconds later on. Doing live vectorisation is neat for exploring data, but using Elasticsearch will be much more convenient in actual products!

Re: Show HN: Semantic Grep – A Word2Vec-powered search tool

#9
post #6

I might have a work use case for which this would be perfect. Having no experience with word2vec, some reference performance numbers would be great. If I have one million PDF pages, how long is that going to take to encode? How long will it take to search? Is it CPU only or will I get a huge performance benefit if I have a GPU?

Vector stuff doesn’t take much. It’s essentially the same computational time as regular old text search.
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