Neural-Cherche is a library designed to fine-tune neural search models such as Splade, ColBERT, and SparseEmbed on a specific dataset. Neural-Cherche also provide classes to run efficient inference on a fine-tuned retriever or ranker. Neural-Cherche aims to offer a straightforward and effective method for fine-tuning and utilizing neural search models in both offline and online settings. It also enables users to save all computed embeddings to prevent redundant computations.
Your training dataset must be made out of triples (anchor, positive, negative) where anchor is a query, positive is a document that is directly linked to the anchor and negative is a document that is not relevant for the anchor.
SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking authored by Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant, SIGIR 2021.
SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval authored by Thibault Formal, Carlos Lassance, Benjamin Piwowarski, Stéphane Clinchant, SIGIR 2022.