SPACY v3: Design concepts explained (behind the scenes)

spaCy is a popular open-source library for industrial-strength Natural Language Processing in Python. spaCy v3.0 features new transformer-based pipelines that get spaCy’s accuracy right up to the current state-of-the-art, and a new training config and workflow system to help you take projects from prototype to production. In this video, I’ll show you some of the new design concepts and explain what’s going on under the hood, how we’ve implemented them and most importantly, why. I’ll also share some lessons we’ve learned about developer experience along the way. STEP BY STEP 00:00 – Intro and "Let Them Write Code" philosophy 01:32 – spaCy's declarative config system 06:44 – "Bottom-up" vs. "top-down" configuration 08:35 – Function registries 12:55 – Type hints an type-based valiation 13:53 – Data validation with Pydantic 18:36 – Static analysis for model definitions 22:27 – Summary and conclusion SPACY ● Website & documentation: https://spacy.io ● GitHub: https://github.com/explosion/spaCy ● Free online course: https://course.spacy.io ● Thinc: https://thinc.ai THIS VIDEO ● "Let Them Write Code" Slides: https://speakerdeck.com/inesmontani/let-them-write-code-keynote-pycon-india-2019 ● "Let Them Write Code" Video: https://www.youtube.com/watch?v=Ivb4AAuj5JY ● What's new in spaCy v3.0: https://spacy.io/usage/v3 ● Catalogue: https://github.com/explosion/catalogue ● Pydantic: https://github.com/samuelcolvin/pydantic FOLLOW US ● Ines Montani: https://twitter.com/_inesmontani ● spaCy: https://twitter.com/spacy_io ● Explosion: https://twitter.com/explosion_ai CREDITS ● PyCon India photos: https://photos.app.goo.gl/RbMp67jSLjr5SrzVA ● Icons: https://twemoji.twitter.com