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blog.vespa.ai | ||
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unstructured.io
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| | | | | Navigate the Massive Text Embedding Benchmark (MTEB) leaderboard with confidence! Understand the difference between Bi-Encoders and Cross-Encoders, learn how text embedding models are pre-trained and benchmarked, and how to make the best choice for your specific use case. | |
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www.shaped.ai
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| | | | | This article explores how cross-encoders, long praised for their performance in neural ranking, may in fact be reimplementing classic information retrieval logic, specifically, a semantic variant of BM25. Through mechanistic interpretability techniques, the authors uncover circuits within MiniLM that correspond to term frequency, IDF, length normalization, and final relevance scoring. The findings bridge modern transformer-based relevance modeling with foundational IR principles, offering both theoretical insight and a roadmap for building more transparent and interpretable neural retrieval systems. | |
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zackproser.com
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| | | | | Embeddings models are the secret sauce that makes RAG work. How are THEY made? | |
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www.onlandscape.co.uk
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| | | [AI summary] The article discusses the use of cookies on the Winter Forest website, part of Landscape Media Limited, and outlines privacy and cookie policies. | ||
