{"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/eclare-extreme-classification-with-label","title":"ECLARE: Extreme Classification with Label Graph Correlations","arxiv_id":"2108.00261","date":"2021-07-31","proceeding":null,"authors":["Anshul Mittal","Noveen Sachdeva","Sheshansh Agrawal","Sumeet Agarwal","Purushottam Kar","Manik Varma"],"abstract":"Deep extreme classification (XC) seeks to train deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. The core utility of XC comes from predicting labels that are rarely seen during training. Such rare labels hold the key to personalized recommendations that can delight and surprise a user. However, the large number of rare labels and small amount of training data per rare label offer significant statistical and computational challenges. State-of-the-art deep XC methods attempt to remedy this by incorporating textual descriptions of labels but do not adequately address the problem. This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds. Core contributions of ECLARE include a frugal architecture and scalable techniques to train deep models along with label correlation graphs at the scale of millions of labels. In particular, ECLARE offers predictions that are 2 to 14% more accurate on both publicly available benchmark datasets as well as proprietary datasets for a related products recommendation task sourced from the Bing search engine. Code for ECLARE is available at https://github.com/Extreme-classification/ECLARE.","url_abs":"https://arxiv.org/abs/2108.00261v1","url_pdf":"https://arxiv.org/pdf/2108.00261v1.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":"eclare-extreme-classification-with-label","repo_url":"https://github.com/Extreme-classification/ECLARE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"extreme-multi-label-classification","task_name":"Extreme Multi-Label Classification"},{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"multi-label-learning","task_name":"Multi-Label Learning"},{"task_slug":"multi-label-text-classification","task_name":"Multi-Label Text Classification"},{"task_slug":"product-recommendation","task_name":"Product Recommendation"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"short-text-clustering","task_name":"Short Text Clustering"},{"task_slug":"tag","task_name":"TAG"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-label-text-classification-on-lf-1","task":"Multi-Label Text Classification","dataset":"LF-AmazonTitles-131K","model":"DECAF","rank_in_archive_order":1,"of":1,"metrics":{"Precision@1":"38.4"},"uses_additional_data":false},{"leaderboard":"/sota/multi-label-text-classification-on-lf","task":"Multi-Label Text Classification","dataset":"LF-AmzonTitles-131K","model":"ECLARE","rank_in_archive_order":1,"of":1,"metrics":{"Precision@1":"40.74"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2108.00261","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00261"}},"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/Extreme-classification/ECLARE","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"official":{"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":"f26f4fc74ede5aa7","entry":"load_overlap","repo":"Extreme-classification/ECLARE","repo_kind":"official","path":"ECLARE/tools/evaluate.py","file_url":"https://github.com/Extreme-classification/ECLARE/blob/HEAD/ECLARE/tools/evaluate.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f26f4fc74ede5aa7"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}