prose VS go-i18n

Apart from Gonum[1] numerical libraries, I haven't found specific data science related Go libraries in my search for it for some hobby projects when compared to Python ecosystem.

Interestingly Prose[2] A Go library for text processing yielded better results for named-entity extraction when compared to NLTK in my tests in terms of accuracy and obviously performance.

Perhaps Go is not being applied enough in the Data Science/ML and for fields where it's applied (Network) Math in the standard library seems to be sufficient.

[1] https://github.com/gonum/gonum

[2] https://github.com/jdkato/prose

Thanks for sharing, from a quick read this looks easier to maintain than nicksnyder/go-i18n.

I am using Nick Snyder's go-i18n for translations. I am also building a go library intented to be imported by either by other libraries or cli executables. All of the documentation I have seen about supporting i18n assume this is being used in an app, but don't cover the scenario where this is being implemented in a library.

On a different note you may consider exploring nicksnyder/go-i18n as well, it simplifies things a bit.

I am trying to get to grips with i18n in Go but finding that most articles on the subject are either inadequate or geared towards web applications. This query is specifically for cli applications and libraries. The kind of articles I've looked at so for are i18n managing translations and internationalization i18n go. I have also investigated using Nick Snyder's go-i18n, but the more I learn, the more I'm coming to the conclusion that it's not necessary for my needs.

What you are looking for is I18N like https://github.com/nicksnyder/go-i18n

This article will show you how to prepare your Go project for localization using a very popular go-i18n package.

I've looked at many packages for this operation (including the one built into the Go core), but nicksnyder/go-i18n was the only one I enjoyed working with in my projects. We will create our demo application using this particular package.