Johann Petrak
Researcher at Austrian Research Institute for Artificial Intelligence
Vienna, Austria
Summary
👤
Senior🎓
Top SchoolJohann Petrak is a seasoned researcher and software developer with over a decade of experience in machine learning, deep learning and NLP, currently working at the Austrian Research Institute for Artificial Intelligence and affiliated with the University of Sheffield's NLP group. He is the main architect of the Python NLP framework GateNLP, a long-time contributor to the Java GATE ecosystem, and an active open-source developer who has improved transfer-learning tooling in prominent projects like deepset-ai/FARM. His work bridges practical engineering and semantic knowledge—specializing in ML for NLP, data linking, and integrating knowledge bases into language technology. Comfortable across Python, Java, Scala and R, he combines tool-building, plugin development and academic research to turn complex linguistic models into usable systems. An early adopter of reproducible text-analysis tooling, he blends decades of systems experience with contemporary deep-learning practices.
12 years of coding experience
31 years of employment as a software developer
Dipl.Ing., Computer Science, Artificial Intelligence, Dipl.Ing., Computer Science, Artificial Intelligence at Vienna University of Technology
English, German, Spanish
Stackoverflow
66reputation
4kreached
2answers
0questions
Github Skills (14)
pytorch10
deeplearning-ai10
nlp10
deep-learning10
language-model10
python10
bert10
question-answering9
cross-validation8
early-stopping8
machine-learning7
api6
documentation6
gate6
Programming languages (22)
Github contributions (5)
deepset-ai/FARM
Nov 2019 - Jul 2021
:house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
Role in this project:
ML Engineer
ML EngineerContributions:13 commits, 13 PRs, 73 comments in 1 year 8 months
Contributions summary:Johann made several contributions related to the FARM framework's core functionality, specifically within the context of transfer learning for NLP and question answering. They addressed bugs in example scripts, added features for early stopping and custom metrics to the training process. They also implemented cross-validation support and contributed to the multi-task learning implementation.
bertxlnet-pytorchnatural-language-processingtransformersharvesting
johann-petrak/gatetool-runpipeline
Feb 2017 - Jan 2020
Contributions:3 releases, 22 pushes, 3 branches in 2 years 11 months
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