Awesome

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 contact@explosion.ai 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 contact@explosion.ai 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)

If present, ${solution} will be replaced with the string value of the submitted user code. In this case, we're inserting it twice: once as a string so we can check whether the submission includes something, and once as the code, so we can actually run it and check the objects it creates. ${test} is replaced by the contents of the test file. I'm also making wasabi's printer available as __msg__, so we can easily print pretty messages in the tests. Finally, the try/accept block checks if the test function raises an AssertionError and if so, displays the error message. This also hides the full error traceback (which can easily leak the correct answers).

A test file could then look like this:

def test():
    assert "spacy.load" in __solution__, "Are you calling spacy.load?"
    assert nlp.meta["lang"] == "en", "Are you loading the correct model?"
    assert nlp.meta["name"] == "core_web_sm", "Are you loading the correct model?"
    assert "nlp(text)" in __solution__, "Are you processing the text correctly?"
    assert "print(doc.text)" in __solution__, "Are you printing the Doc's text?"

    __msg__.good(
        "Well done! Now that you've practiced loading models, let's look at "
        "some of their predictions."
    )

With this approach, it's not always possible to validate the input perfectly – there are too many options and we want to avoid false positives.

Running automated tests

The automated tests make sure that the provided solution code is compatible with the test file that's used to validate submissions. The test suite is powered by the pytest framework and runnable test files are generated automatically in a directory __tests__ before the test session starts. See the conftest.py for implementation details.

# Install requirements
pip install -r binder/requirements.txt
# Run the tests (will generate the files automatically)
python -m pytest __tests__

If running with docker just run make build and then make pytest

Directory Structure

├── binder
|   └── requirements.txt  # Python dependency requirements for Binder
├── chapters              # chapters, grouped by language
|   ├── en                # English chapters, one Markdown file per language
|   |   └── slides        # English slides, one Markdown file per presentation
|   └── ...               # other languages
├── exercises             # code files, tests and assets for exercises
|   ├── en                # English exercises, solutions, tests and data
|   └── ...               # other languages
├── public                # compiled site
├── src                   # Gatsby/React source, independent from content
├── static                # static assets like images, available in slides/chapters
├── locale.json           # translations of meta and UI text
├── meta.json             # course metadata
└── theme.sass            # UI theme colors and settings

Setting up Binder

The requirements.txt in the repository defines the packages that are installed when building it with Binder. For this course, I'm using the source repo as the Binder repo, as it allows to keep everything in one place. It also lets the exercises reference and load other files (e.g. JSON), which will be copied over into the Python environment. I build the binder from a branch binder, though, which I only update if Binder-relevant files change. Otherwise, every update to master would trigger an image rebuild.

You can specify the binder settings like repo, branch and kernel type in the "juniper" section of the meta.json. I'd recommend running the very first build via the interface on the Binder website, as this gives you a detailed build log and feedback on whether everything worked as expected. Enter your repository URL, click "launch" and wait for it to install the dependencies and build the image.

File formats

Chapters

Chapters are placed in /chapters and are Markdown files consisting of <exercise> components. They'll be turned into pages, e.g. /chapter1. In their frontmatter block at the top of the file, they need to specify type: chapter, as well as the following meta:

---
title: The chapter title
description: The chapter description
prev: /chapter1 # exact path to previous chapter or null to not show a link
next: /chapter3 # exact path to next chapter or null to not show a link
id: 2 # unique identifier for chapter
type: chapter # important: this creates a standalone page from the chapter
---

Slides

Slides are placed in /slides and are markdown files consisting of slide content, separated by ---. They need to specify the following frontmatter block at the top of the file:

---
type: slides
---

The first and last slide use a special layout and will display the headline in the center of the slide. Speaker notes (in this case, the script) can be added at the end of a slide, prefixed by Notes:. They'll then be shown on the right next to the slides. Here's an example slides file:

---
type: slide
---

# Processing pipelines

Notes: This is a slide deck about processing pipelines.

---

# Next slide

- Some bullet points here
- And another bullet point

<img src="/image.jpg" alt="An image located in /static" />

Custom Elements

When using custom elements, make sure to place a newline between the opening/closing tags and the children. Otherwise, Markdown content may not render correctly.

<exercise>

Container of a single exercise.

<exercise id="1" title="Introduction to spaCy">

Content goes here...

</exercise>

<codeblock>

<codeblock id="02_03">

This is a hint!

</codeblock>

<slides>

Container to display slides interactively using Reveal.js and a Markdown file.

<slides source="chapter1_01_introduction-to-spacy">
</slides>

<choice>

Container for multiple-choice question.

<choice>

<opt text="Option one">You have selected option one! This is not good.</opt>
<opt text="Option two" correct="true">Yay! </opt>

</choice>

<opt>

A multiple-choice option.