Semantic String Similarity CLI

simil is a CLI interface to spacy's string similarity engine. It uses the en_vectors_web_lg dataset to compare strings for their English semantic similarity. Given two words, phrases, or sentences, simil will tell you how similar their meanings are.

Installation

First install simil itself:

$ pip3 install --user -U simil

Now install one of spacy's web_vector models:

$ python3 -m spacy download en_vectors_web_lg

You can choose between en_vectors_web_lg, en_core_web_lg, and en_core_web_md, (en_core_web_sm don't include word vectors at all, and can't be used with simil.) simil will use the largest model that you have installed, with preference for the vectors model over a core model.

I suggest using the large vectors model (en_vectors_web_lg), but you might want to use a smaller model in order to save on disk space or memory usage.

Usage:

$ sim first_file.txt second_file.txt # compare two files
$ sim -s "first string" "second string" # compare two strings

The output is a number between 0 and 1, representing how similar the two strings are.

Details:

simil uses Spacy's word vector models trained with GLoVe, such as en_vectors_web_lg.

This can be a large dataset, which makes for long startup times. So simil spins off a process in the background to hold the model, and works under a client-server model with it. This means that if you run simil a number of times in a row, only the first run is slow.

This background process does take up a fair bit of memory, typically around 2GB (for the en_vectors_web_lg model). After 10 minutes of inactivity it will automatically be killed, in order not to take up memory indefinitely. You can change the length of this timeout with the --timeout flag.