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We have Josh Phillips presenting this week, so you don’t want to miss this!
Join us as we explore the crucial task of selecting optimal embedding models to enhance AI performance across a variety of applications. This meetup will delve into the Multilingual Transferable Embedding Benchmark (MTEB), a pivotal resource providing a comprehensive framework to evaluate embedding models over diverse task categories and numerous languages. The selection of the right embedding model is vital, yet challenging due to the myriad of options and their inherent trade-offs.
This presentation will not only introduce you to MTEB’s holistic approach across eight core NLP tasks but will also guide you through the practical steps of identifying, shortlisting, and benchmarking models to find the best fit for your specific needs.
Agenda:
- Introduction to Embedding Models – Gain insights into why choosing the right model is critical for AI tasks.
- Overview of MTEB – Understand the framework of the Multilingual Transferable Embedding Benchmark and its application across 100+ languages.
- Deep Dive into MTEB Tasks – Explore the eight fundamental tasks within MTEB, including bitext mining, classification, clustering, and more.
- Case Studies – Walk through real-world use cases, demonstrating how to apply MTEB in selecting models for tasks such as walking path recommendations, form filling automation, and building a documentation assistant.
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