spacy-course

Advanced NLP with spaCy: A free online course

This repo contains both an online course, as well as its modern open-source web framework. In the course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches. The front-end is powered by Gatsby, Reveal.js and Plyr, and the back-end code execution uses Binder It's all open-source and published under the MIT license (code and framework) and CC BY-NC (spaCy course materials).

This course is mostly intended for self-study. Yes, you can cheat – the solutions are all in this repo, there's no penalty for clicking "Show hints" or "Show solution", and you can mark an exercise as done when you think it's done.

Languages and Translations

If you spot a mistake, I always appreciate pull requests!

1. This is the language used for the text examples and resources used in the exercises. For example, the German version of the course also uses German text examples and models. It's not always possible to translate all code examples, so some translations may still use and analyze English text as part of the course.

Related resources

• Prefer notebooks? Check out the Jupyter notebook version of this course, put together by @cristianasp.

FAQ

Is this related to the spaCy course on DataCamp?

I originally developed the content for DataCamp, but I wanted to make a free version to make it available to more people, and so you don't have to sign up for their service. As a weekend project, I ended up putting together my own little app to present the exercises and content in a fun and interactive way.

Can I use this to build my own course?

Probably, yes! If you've been looking for a DIY way to publish your materials, I hope that my little framework can be useful. Because so many people expressed interest in this, I put together some starter repos that you can fork and adapt:

• Python: ines/course-starter-python
• R: ines/course-starter-r

Why the different licenses?

The source of the app, UI components and Gatsby framework for building interactive courses is licensed as MIT, like pretty much all of my open-source software. The course materials themselves (slides and chapters), are licensed under CC BY-NC. This means that you can use them freely – you just can't make money off them.

I want to help translate this course into my language. How can I contribute?

First, thanks so much, this is really cool and valuable to the community I've tried to set up the course structure so it's easy to add different languages: language-specific files are organized into directories in exercises and chapters, and other language specific texts are available in locale.json. If you want to contribute, there are two different ways to get involved:

1. Start a community translation project. This is the easiest, no-strings-attached way. You can fork the repo, copy-paste the English version, change the language code, start translating and invite others to contribute (if you like). If you're looking for contributors, feel free to open an issue here or tag @spacy_io on Twitter so we can help get the word out. We're also happy to answer your questions on the issue tracker.

2. Make us an offer. We're open to commissioning translations for different languages, so if you're interested, email us at [email protected] and include your offer, estimated time schedule and a bit about you and your background (and any technical writing or translation work you've done in the past, if available). It doesn't matter where you're based, but you should be able to issue invoices as a freelancer or similar, depending on your country.

I want to help create an audio/video tutorial for an existing translation. How can I get involved?

Again, thanks, this is super cool! While the English and German videos also include a video recording, it's not a requirement and we'd be happy to just provide an audio track alongside the slides. We'd take care of the postprocessing and video editing, so all we need is the audio recording. If you feel comfortable recording yourself reading out the slide notes in your language, email us at [email protected] and make us an offer and include a bit about you and similar work you've done in the past, if available.

Usage & API

Running the app

To start the local development server, install Gatsby and then all other dependencies, then use npm run dev to start the development server. Make sure you have at least Node 10.15 installed.

npm install -g gatsby-cli  # Install Gatsby globally
npm install                # Install dependencies
npm run dev                # Run the development server

If running with docker just run make build and then make gatsby-dev

How it works

When building the site, Gatsby will look for .py files and make their contents available to query via GraphQL. This lets us use the raw code within the app. Under the hood, the app uses Binder to serve up an image with the package dependencies, including the spaCy models. By calling into JupyterLab, we can then execute code using the active kernel. This lets you edit the code in the browser and see the live results. Also see my juniper repo for more details on the implementation.

To validate the code when the user hits "Submit", I'm currently using a slightly hacky trick. Since the Python code is sent back to the kernel as a string, we can manipulate it and add tests – for example, exercise exc_01_02_01.py will be validated using test_01_02_01.py (if available). The user code and test are combined using a string template. At the moment, the testTemplate in the meta.json looks like this:

from wasabi import msg
__msg__ = msg
__solution__ = """${solution}"""
${solution}

${test}
try:
    test()
except AssertionError as e:
    __msg__.fail(e