diff --git a/website/docs/usage/layers-architectures.md b/website/docs/usage/layers-architectures.md index ea0427903..32319ca07 100644 --- a/website/docs/usage/layers-architectures.md +++ b/website/docs/usage/layers-architectures.md @@ -70,12 +70,11 @@ classes = 16 > return model > ``` -​ The Thinc `Model` class is a **generic type** that can specify its input and +The Thinc `Model` class is a **generic type** that can specify its input and output types. Python uses a square-bracket notation for this, so the type ~~Model[List, Dict]~~ says that each batch of inputs to the model will be a -list, and the outputs will be a dictionary. Both `typing.List` and `typing.Dict` -are also generics, allowing you to be more specific about the data. For -instance, you can write ~~Model[List[Doc], Dict[str, float]]~~ to specify that +list, and the outputs will be a dictionary. You can be even more specific and +write for instance~~Model[List[Doc], Dict[str, float]]~~ to specify that the model expects a list of [`Doc`](/api/doc) objects as input, and returns a dictionary mapping of strings to floats. Some of the most common types you'll see are: ​ @@ -103,8 +102,8 @@ interchangeably. There are many other ways they could be incompatible. However, if the types don't match, they almost surely _won't_ be compatible. This little bit of validation goes a long way, especially if you [configure your editor](https://thinc.ai/docs/usage-type-checking) or other -tools to highlight these errors early. Thinc will also verify that your types -match correctly when your config file is processed at the beginning of training. +tools to highlight these errors early. The config file is also validated +at the beginning of training, to verify that all the types match correctly.