Capreolus is a toolkit for conducting end-to-end ad hoc retrieval experiments, which consist of a first stage ranking method (e.g., BM25 or RM3) followed by a neural re-ranking method.
Capreolus is organized around the idea of interchangeable and configurable modules, such as a neural
reranker or a first stage
searcher. Researchers can implement new module classes, such as a new neural
reranker, to experiment with a new module while controlling for all other variables in the pipeline (e.g., the first stage ranking method and its parameters, folds used for cross-validation, tokenization and embeddings if applicable used with the reranker, neural training options like the number of iterations, batch size, and loss function, etc).
Looking for the code? Find Capreolus on GitHub.