Training and Finetuning Reranker Models with Sentence Transformers v4

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Sentence Transformers is a Python library for using and training embedding and reranker models for a wide range of applications, such as retrieval augmented generation, semantic search, semantic textual similarity, paraphrase mining, and more. In this blogpost, I'll show you how to use it to finetune a reranker model (also known as a cross-encoder) that beats all existing options on exactly your data. This method can also train extremely strong new reranker models from scratch. Finetuning reranker models involves several components: datasets, loss functions, training arguments, evaluators,…

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• John Zhang

related reading

• The Annotated Transformernlp.seas.harvard.edu
• Rerankers and Two-Stage Retrieval | Pineconepinecone.io
• On the Tradeoffs of SSMs and Transformers | Goomba Labgoombalab.github.io
• Sentence Embeddings. Introduction to Sentence Embeddings – hackerllamaosanseviero.github.io
• Recent Advances in Language Model Fine-tuningruder.io
• Semantic Search - Sentence Transformers documentationsbert.net
• Transformers Laid Outgoyalpramod.github.io
• Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformerai.googleblog.com
• 1810.04805arxiv.org
• Seq2seq and Attentionlena-voita.github.io
• Transfer Learninglena-voita.github.io
• [2011.04006] Long Range Arena: A Benchmark for Efficient Transformersarxiv.org