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* change class names to bm25 * Update Documentation & Code Style * Update Documentation & Code Style * Update Documentation & Code Style * Add back all_terms_must_match * fix syntax * Update Documentation & Code Style * Update Documentation & Code Style * Creating a wrapper for old ES retriever with deprecated wrapper * Update Documentation & Code Style * New method for deprecating old ESRetriever * New attempt for deprecating the ESRetriever * Reverting to the simplest solution - warning logged * Update Documentation & Code Style Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Sara Zan <sara.zanzottera@deepset.ai>
Benchmarks
Run the benchmarks with the following command:
python run.py [--reader] [--retriever_index] [--retriever_query] [--ci] [--update-json]
You can specify which components and processes to benchmark with the following flags.
--reader will trigger the speed and accuracy benchmarks for the reader. Here we simply use the SQuAD dev set.
--retriever_index will trigger indexing benchmarks
--retriever_query will trigger querying benchmarks (embeddings will be loaded from file instead of being computed on the fly)
--ci will cause the the benchmarks to run on a smaller slice of each dataset and a smaller subset of Retriever / Reader / DocStores.
--update-json will cause the script to update the json files in docs/_src/benchmarks so that the website benchmarks will be updated.