Скачать или смотреть Ten Proven Techniques to Improve Your NLP Model Training - Darek Kłeczek | Munich NLP Hands-on 011

Darek Kłeczek is a Machine Learning Engineer at Weights & Biases where he is leading the W&B education program. Previously, he applied machine learning across supply chain, manufacturing, legal and commercial use cases. He also worked on operationalizing machine learning at P&G. Darek has contributed the first Polish versions of BERT and GPT language models and has been a leader in the Polish NLP community.

In this talk, Darek explores the latest and most effective techniques for training natural language processing models. We will cover methods that have been proven to work in Kaggle competitions, such as AWP, SWA, and pseudo-labeling, as well as best practices learned from Weights & Biases power users. With these techniques, you'll be able to train your models faster and achieve better performance. Whether you're a seasoned machine learning engineer or just getting started, you'll come away with valuable insights and practical tips for boosting your NLP model training.

You can find Darek, Weights & Biases, and the resources mentioned in the talk at:
Personal Homepage: https://skok.ai/
GitHub: https://github.com/kldarek

DEBERTA Paper: https://arxiv.org/abs/2006.03654
Weights & Biases courses: https://www.wandb.courses/pages/w-b-c...
Munich Datageeks: munich-datageeks.de


About Munich NLP:

Munich🥨NLP is a community newly founded in May 2022 by LMU and TUM students focusing on NLP topics. Within just six months, the community has already grown to over 900 members consisting not only of current students, but also including PhD students, professors, and industry practitioners. We host weekly workshops and/or paper-reading events, both to learn from guests and to gather inspiration for our own (research) projects, as well as to establish and keep going an active student NLP community in the Munich area. The goal is to promote NLP-related exchange between students, researchers, and practitioners inside and outside the university and to showcase paths and possibilities during and after university.

https://munich-nlp.github.io/
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