Matthew Honnibal
Founder at Explosion AI
Berlin, Germany
Summary
🤩
Rockstar🎓
Top School
Top expert inNatural Language Processing and Machine Learning Technologies
Matthew Honnibal is a computational linguist, entrepreneur, and founder of Explosion AI, best known for creating the industrial-strength spaCy NLP library that makes state-of-the-art language technology accessible beyond academia. With a PhD in computational linguistics and over a decade of research and engineering experience, he has authored 20+ peer-reviewed publications including influential work on conversational parsing and named entity linking. He bridges deep research and pragmatic engineering—leading spaCy core development, spaCy-transformers integration, and performant tools like sense2vec and Prodigy recipes—while prioritizing concise, readable code and efficient training pipelines. Based in Berlin, he combines academic rigor with product-minded execution, having taken theoretical ideas to evaluated systems and industry deployments. An uncommon trait: he left a research trajectory deliberately to scale NLP impact through open-source and commercial products.
11 years of coding experience
2 years of employment as a software developer
The University of SydneyBachelor of Arts (B.A.) Linguistics, Bachelor of Arts (B.A.) Linguistics at Macquarie University
Github Skills (26)
transformers10
pytorch10
prodigy10
python10
machine-learning10
ml10
spacy10
natural-language-processing10
cython10
nlp10
data-augmentation10
word2vec9
command-line-interface9
unit-testing9
caching9
Programming languages (13)
Github contributions (5)
explosion/spacy-transformers
Jul 2019 - Mar 2021
🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:2 releases, 13 reviews, 963 commits in 1 year 8 months
Contributions summary:Matthew contributed to the core functionality of the spaCy-transformers project, by debugging and refactoring existing code, and implementing unit tests. The user's work focused on enhancing the model architecture, and introducing new functionalities for transformer models. The user also worked on alignment for the project.
natural-language-understandingxlnetbertgooglenatural-language-processing
explosion/spaCy
Jul 2014 - Jul 2022
💫 Industrial-strength Natural Language Processing (NLP) in Python
Role in this project:Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:9 releases, 202 reviews, 4014 commits in 8 years 1 month
Contributions summary:Matthew's commits primarily focused on improvements to the model training scripts and the implementation of data augmentation techniques. Their work involved modifications to the command-line interface, adjustments to the optimizer, and the introduction of a character-based pretraining objective. They demonstrated a good understanding of the project's goals by integrating new techniques that could improve performance, while also contributing to the efficiency of the training process through GPU optimization.
fairness-mlpythondata-preprocessinglanguage-processingtokenization
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