Metadata-Version: 2.1
Name: simil
Version: 0.0.2
Summary: CLI for semantic string similarity
Home-page: https://github.com/foxbenjaminfox/string-similarity-cli
Author: Benjamin Fox
Author-email: foxbenjaminfox@gmail.com
License: UNKNOWN
Description: # Semantic String Similarity CLI
        
        `simil` is a CLI interface to [`spacy`](https://spacy.io)'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:
        ```bash
        $ 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:
        ```bash
        $ 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`](https://nlp.stanford.edu/projects/glove/), such as [`en_vectors_web_lg`](https://spacy.io/models/en#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.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Description-Content-Type: text/markdown
