{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pruning-the-index-contents-for-memory","title":"Pruning the Index Contents for Memory Efficient Open-Domain QA","arxiv_id":"2102.10697","date":"2021-02-21","proceeding":null,"authors":["Martin Fajcik","Martin Docekal","Karel Ondrej","Pavel Smrz"],"abstract":"This work presents a novel pipeline that demonstrates what is achievable with a combined effort of state-of-the-art approaches. Specifically, it proposes the novel R2-D2 (Rank twice, reaD twice) pipeline composed of retriever, passage reranker, extractive reader, generative reader and a simple way to combine them. Furthermore, previous work often comes with a massive index of external documents that scales in the order of tens of GiB. This work presents a simple approach for pruning the contents of a massive index such that the open-domain QA system altogether with index, OS, and library components fits into 6GiB docker image while retaining only 8% of original index contents and losing only 3% EM accuracy.","url_abs":"https://arxiv.org/abs/2102.10697v2","url_pdf":"https://arxiv.org/pdf/2102.10697v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pruning-the-index-contents-for-memory","repo_url":"https://github.com/KNOT-FIT-BUT/R2-D2","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"pruning-the-index-contents-for-memory","repo_url":"https://github.com/KNOT-FIT-BUT/scalingQA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"open-domain-question-answering","task_name":"Open-Domain Question Answering"}],"methods":[{"method_slug":"pruning","method_name":"Pruning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2102.10697","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.10697"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/KNOT-FIT-BUT/R2-D2","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/KNOT-FIT-BUT/scalingQA","reach":null}],"summary":{"ran":1,"ran_violates":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1},"listed":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"00edbd165e6fc7b2","entry":"get_dataloader_for_baseline_reranker","repo":"KNOT-FIT-BUT/scalingQA","repo_kind":"listed","path":"scalingqa/reranker/training/train_reranker.py","file_url":"https://github.com/KNOT-FIT-BUT/scalingQA/blob/HEAD/scalingqa/reranker/training/train_reranker.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"00edbd165e6fc7b2"}},{"code_sha256_prefix":"1bf63fc479343728","entry":"is_component_active","repo":"KNOT-FIT-BUT/R2-D2","repo_kind":"official","path":"prediction.py","file_url":"https://github.com/KNOT-FIT-BUT/R2-D2/blob/HEAD/prediction.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1bf63fc479343728"}},{"code_sha256_prefix":"06ea952a8b6b8f09","entry":"is_tuning_needed","repo":"KNOT-FIT-BUT/R2-D2","repo_kind":"official","path":"prediction.py","file_url":"https://github.com/KNOT-FIT-BUT/R2-D2/blob/HEAD/prediction.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"06ea952a8b6b8f09"}},{"code_sha256_prefix":"e0353304494afc8b","entry":"create_filename","repo":"KNOT-FIT-BUT/R2-D2","repo_kind":"official","path":"prediction.py","file_url":"https://github.com/KNOT-FIT-BUT/R2-D2/blob/HEAD/prediction.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e0353304494afc8b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}