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...In other words, it has "zero dependencies", and we are committed to keeping it that way as much as possible. Spago uses a multi-module workspace to ensure that additional dependencies are downloaded only when specific features (e.g. persistent embeddings) are used. A good place to start is by looking at the implementation of built-in neural models, such as the LSTM. Except for a few linear algebra operations written in assembly for optimal performance (a bit of copying from Gonum), it's straightforward Go code, so you don't have to worry.

Go package for text annotation. There are two parts to anno, the first is a series of Finder functions that look for interesting articles (which it calls `Notes`) inside the text, returning a slice of Note structs. The second is the Expander, which replaces the text in each Note with something else, like the HTML for a link or something. It tells you the bytes that it found, the `Start` index and a string describing the kind of `Note`. The kind is useful for when you run pass `Finder`...