Thilina Rajapakse
Postdoctoral Researcher at Eindhoven University of Technology
Netherlands
Summary
👤
Senior🎓
Top SchoolThilina Rajapakse is a postdoctoral researcher at Eindhoven University of Technology with a decade of experience focused on NLP and information retrieval, particularly LLMs, retrieval-augmented generation, and dense retrieval. He blends rigorous academic training (PhD-level research at University of Amsterdam) with industry stints at Google DeepMind and Spotify, and practical consulting experience delivering production-impacting solutions. Creator of the widely used Simple Transformers library (4k+ GitHub stars), he bridges open-source tooling and cutting-edge research to accelerate adoption. Based in the Netherlands, he pairs an experimental research mindset with product-aware engineering, and—less obviously—maintains a playful intellectual curiosity as an avid reader and genre fiction fan that fuels creative problem framing.
10 years of coding experience
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at Postgraduate Institute of Science (PGIS), University of PeradeniyaBachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at University of PeradeniyaHigh School, Physics, Maths and Chemistry, High School, Physics, Maths and Chemistry at Dharmaraja College Kandy
English, Sinhalese
Stackoverflow
66reputation
3kreached
1answer
1question
Github Skills (39)
transformers10
pytorch10
named-entity-recognition10
classification10
distilbert10
machine-learning10
text-classification10
korean-nlp10
deep-learning10
question-answering10
natural-language-processing10
bert10
conversational-ai10
transformer10
nlp10
Programming languages (3)
Github contributions (5)
ThilinaRajapakse/BERT_binary_text_classification
Jun 2019 - Oct 2019
Contributions:20 commits, 19 pushes, 1 branch in 4 months
ThilinaRajapakse/pytorch-transformers-classification
Sep 2019 - May 2020
Based on the Pytorch-Transformers library by HuggingFace. To be used as a starting point for employing Transformer models in text classification tasks. Contains code to easily train BERT, XLNet, RoBERTa, and XLM models for text classification.
Contributions:46 commits, 1 PR, 43 pushes in 8 months
distilbertxlnetbertstarting-pointnatural-language-processing
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