Tatoeba is a website to crowdsource sentences translated in several languages, a resource that is very useful to language learners or people interested in NLP.

I am a contributor and an user of Tatoeba, where I mostly translate sentences to Italian. In 2020 Tatoeba organized an event called Kodoeba to which I participated with an automated cloze deletion tool.

In this article I’m going to analyze the Tatoeba dataset and build some charts from it.

Note: The whole code for this article is freely available on GitHub

A few weeks ago I saw a talk about Skorch, a library that wraps a PyTorch neural network to use it as a Scikit-learn model.

That is amazing: I can take an existing product based on, say, a random forest, and replace only the model without refactoring anything else: the fit and predict functions have the usual interface. On the other hand, I can use the powerful tools offered by Scikit-learn, like the grid search for hyperparameters and make_pipeline to apply encoders.

Note: this is an old article and while the software is still available and I think the idea is pretty you probably want to give a look to this. The software here described is probably more flexible, but harder to use.

Some months ago, I stumbled across this amazing article about transforming an arbitrary English text in a patent application. The underlying pattern library allows, among other nice things, to find patterns like “The [an adjective] [a noun] and the [a noun]” easily, look for hypernyms (“is a” relationships between expressed concepts, for example “animal” is an hypernym of “cat”) in WordNet, and conjugate verbs in various languages comprehending Italian.