Dec 5, 2023 · OpenSearch 2.11 introduced neural sparse search—a new efficient method of semantic retrieval. In this blog post, you’ll learn about using sparse encoders for semantic search. You’ll find that neural sparse search reduces costs, …

Jul 8, 2021 · SPLADE is a first-stage ranker trained to predict sparse keyword representations, which are later used in a traditional inverted index, that works by adding new terms or removing existing ones in documents/queries, as well as by estimating the importance of each word.

Feb 26, 2025 · Granite-Embedding-30m-Sparse Model Summary: Granite-Embedding-30m-Sparse is a 30M parameter sparse biencoder embedding model from the Granite Experimental suite that can be used to generate high quality text embeddings.

Aug 21, 2024 · For neural sparse search, the sparse encoding model is critical because it influences document scoring and ranking, directly impacting search relevance. The model’s inference speed also affects ingestion throughput and …

Jul 9, 2023 · Recently, Elasticsearch introduced a new machine learning model called ELSER (Elastic Learned Sparse EncodeR). This model provides the semantic search experience when using an inverted index....

Tackling the trade-of, we propose a novel uni-encoder ranking model, Sparse retriever using a Dual document Encoder (SpaDE), learning document representation via the dual encoder. Each encoder plays a central role in (i) adjusting the importance of terms to improve lexical matching and (ii) expanding additional terms to support semantic matching.

Jun 11, 2024 · Neural sparse search has two modes: doc-only and bi-encoder: For the doc-only mode, we use a tokenizer and a predefined lookup table to build the sparse vector. The tokenization takes less than 1 millisecond.

Nov 30, 2023 · The bi-encoder architecture provides a framework for understanding machine-learned retrieval models based on dense and sparse vector representations. Although these representations capture...

neural_ir.rerank, neural_ir.rank_dense, neural_ir.rank_sparse: These modules handle different modes of inference, including re-ranking run files with a cross-encoder, vector indexing and searching with a dense bi-encoder, and inverted indexing and searching with a sparse bi-encoder.

These notes describe the sparse autoencoder learning algorithm, which is one approach to automatically learn features from unlabeled data.