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<!--- Provide a general summary of your changes in the title. --> ## Description The new website is implemented using [Gatsby](https://www.gatsbyjs.org) with [Remark](https://github.com/remarkjs/remark) and [MDX](https://mdxjs.com/). This allows authoring content in **straightforward Markdown** without the usual limitations. Standard elements can be overwritten with powerful [React](http://reactjs.org/) components and wherever Markdown syntax isn't enough, JSX components can be used. Hopefully, this update will also make it much easier to contribute to the docs. Once this PR is merged, I'll implement auto-deployment via [Netlify](https://netlify.com) on a specific branch (to avoid building the website on every PR). There's a bunch of other cool stuff that the new setup will allow us to do – including writing front-end tests, service workers, offline support, implementing a search and so on. This PR also includes various new docs pages and content. Resolves #3270. Resolves #3222. Resolves #2947. Resolves #2837. ### Types of change enhancement ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
39 lines
2.4 KiB
Markdown
39 lines
2.4 KiB
Markdown
spaCy's models are **statistical** and every "decision" they make – for example,
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which part-of-speech tag to assign, or whether a word is a named entity – is a
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**prediction**. This prediction is based on the examples the model has seen
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during **training**. To train a model, you first need training data – examples
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of text, and the labels you want the model to predict. This could be a
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part-of-speech tag, a named entity or any other information.
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The model is then shown the unlabelled text and will make a prediction. Because
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we know the correct answer, we can give the model feedback on its prediction in
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the form of an **error gradient** of the **loss function** that calculates the
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difference between the training example and the expected output. The greater the
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difference, the more significant the gradient and the updates to our model.
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> - **Training data:** Examples and their annotations.
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> - **Text:** The input text the model should predict a label for.
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> - **Label:** The label the model should predict.
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> - **Gradient:** Gradient of the loss function calculating the difference
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> between input and expected output.
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When training a model, we don't just want it to memorize our examples – we want
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it to come up with theory that can be **generalized across other examples**.
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After all, we don't just want the model to learn that this one instance of
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"Amazon" right here is a company – we want it to learn that "Amazon", in
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contexts _like this_, is most likely a company. That's why the training data
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should always be representative of the data we want to process. A model trained
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on Wikipedia, where sentences in the first person are extremely rare, will
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likely perform badly on Twitter. Similarly, a model trained on romantic novels
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will likely perform badly on legal text.
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This also means that in order to know how the model is performing, and whether
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it's learning the right things, you don't only need **training data** – you'll
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also need **evaluation data**. If you only test the model with the data it was
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trained on, you'll have no idea how well it's generalizing. If you want to train
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a model from scratch, you usually need at least a few hundred examples for both
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training and evaluation. To update an existing model, you can already achieve
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decent results with very few examples – as long as they're representative.
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