Summary
The state of the art in natural language processing is a constantly moving target. With the rise of deep learning, previously cutting edge techniques have given way to robust language models. Through it all the team at Explosion AI have built a strong presence with the trifecta of SpaCy, Thinc, and Prodigy to support fast and flexible data labeling to feed deep learning models and performant and scalable text processing. In this episode founder and open source author Matthew Honnibal shares his experience growing a business around cutting edge open source libraries for the machine learning developent process.
Announcements
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- Your host as usual is Tobias Macey and today I’m interviewing Matthew Honnibal about the Thinc and Prodigy tools and an update on SpaCy
Interview
- Introductions
- How did you get introduced to Python?
- Can you start by giving an overview of your mission at Explosion?
- We spoke previously about your work on SpaCy. What has changed in the past 3 1/2 years?
- How have recent innovations in language models such as BERT and GPT-2 influenced the direction or implementation of the project?
- When I last looked SpaCy only supported English and German, but you have added several new languages. What are the most challenging aspects of building the additional models?
- What would be required for supporting symbolic or right-to-left languages?
- How has the ecosystem for language processing in Python shifted or evolved since you first introduced SpaCy?
- Another project that you have released is Prodigy to support labelling of datasets. Can you talk through the motivation for creating it and describe the workflow for someone using it?
- What was lacking in the other annotation tools that you have worked with that you are trying to solve for in Prodigy?
- What are some of the most challenging or problematic aspects of labelling data sets for use in machine learning projects?
- What is a typical scale of data that can be reasonably handled by an individual or small team working with Prodigy?
- At what point do you find that it makes sense to use a labeling service rather than generating the labels yourself?
- What is a typical scale of data that can be reasonably handled by an individual or small team working with Prodigy?
- Your most recent project is Thinc for building and using deep learning models. What was the motivation for creating it and what problem does it solve in the ecosystem?
- How does its design and usage compare to other deep learning frameworks such as PyTorch and Tensorflow?
- How does it compare to projects such as Keras that abstract across those frameworks?
- How do the SpaCy, Prodigy, and Thinc libraries work together?
- What are some of the biggest challenges that you are facing in building open source tools to meet the needs of data scientists and machine learning engineers?
- What are some of the most interesting or impressive projects that you have seen built with the tools your team is creating?
- What do you have planned for the future of Explosion, SpaCy, Prodigy, and Thinc?
Keep In Touch
Picks
- Tobias
- Onward movie
- Matthew
Closing Announcements
- Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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Links
- Explosion AI
- SpaCy
- Thinc
- Prodigy
- Natural Language Processing
- Perl
- NLTK
- GPU == Graphics Processing Unit
- TPU == Tensor Processing Unit
- Transfer Learning
- Airflow
- Luigi
- Perceptron
- PyTorch
- Tensorflow
- Functional Programming
- MxNet
- Keras
- Cuda
- C Language
- Continuous Integration
- Blackstone
- Allen AI Institute
- SciSpaCy
- Holmes
- Sense2Vec
- FastAPI
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
from Planet Python
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