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Sentence Transformers and Embedding Evaluation - Nils Reimers - Talking Language AI Ep#3

Sentence Transformers (https://www.sbert.net/) is one of the most popular Language AI/NLP tools. Tens of thousands of users rely on it to build systems for text classification, neural/semantic search, text clustering, and other language AI tasks. In this conversation, Nils Reimers, the creator of Sentence BERT talks about, - An introduction to the package and the Large Language Models provided in it - Lessons learned from the open-source development of such a popular package - His research collaborations on how to evaluate embeddings through works like MTEB: Massive Text Embedding Benchmark and BEIR Bio: Nils Reimers is currently the Director and Principal Scientist of Machine Learning at Cohere. Previously, he authored several well-known research papers, including Sentence-BERT and the popular sentence-transformers library. He also worked as a Research Scientist at HuggingFace, (co-)founded several web companies and worked as an AI consultant in the area of investment banking, media, and IoT. Join the Cohere Discord:   / discord   Discussion thread for this episode (feel free to ask questions):   / discord   === Contents Introduction (0:00) Nils Intro (2:19) Neural search (2:55) Dense Bi-encoders (6:26) Contrastive training (8:16) Why we need embedding benchmarks (10:07) The predictive power of benchmarks declines over time (14:28) Benchmarking Information Retrieval with BEIR (19:58) Massive text embeddings benchmark (29:07) SetFit (34:05) Multilingual search and embeddings (40:52) Cross-lingual search benefits and drawbacks (46:27) Lessons from developing open source software (50:18) The benefits and challenges of maintaining a popular open source library (54:21) === Resources: Bonjour. مرحبا. Guten tag. Hola. Cohere's Multilingual Text Understanding Model is Now Available: https://txt.cohere.ai/multilingual/ Sentence Transformers: https://www.sbert.net/ SBERT Paper: https://arxiv.org/abs/1908.10084 MTEB: Massive Text Embedding Benchmark: https://arxiv.org/abs/2210.07316 BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models: https://openreview.net/forum?id=wCu6T... SetFit - Efficient Few-shot Learning with Sentence Transformers https://github.com/huggingface/setfit