{"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/xgboost-a-scalable-tree-boosting-system","title":"XGBoost: A Scalable Tree Boosting System","arxiv_id":"1603.02754","date":"2016-03-09","proceeding":null,"authors":["Tianqi Chen","Carlos Guestrin"],"abstract":"Tree boosting is a highly effective and widely used machine learning method.\nIn this paper, we describe a scalable end-to-end tree boosting system called\nXGBoost, which is used widely by data scientists to achieve state-of-the-art\nresults on many machine learning challenges. We propose a novel sparsity-aware\nalgorithm for sparse data and weighted quantile sketch for approximate tree\nlearning. More importantly, we provide insights on cache access patterns, data\ncompression and sharding to build a scalable tree boosting system. By combining\nthese insights, XGBoost scales beyond billions of examples using far fewer\nresources than existing systems.","url_abs":"http://arxiv.org/abs/1603.02754v3","url_pdf":"http://arxiv.org/pdf/1603.02754v3.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":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/dmlc/xgboost","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/1082-datascience/finalproject-finalproject-1082ds_group3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/360jinrong/GBST","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/Automunge/AutoMunge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/Fickle519/stockforecastingmodelbasedon_XGboost","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/Hem7513/Decision-Trees-and-XGBoost-Algorithm-Documentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/Javihaus/Auto-ML-app-with-dash","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/KPIxLILU/Machine-Learning-Workshop","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/MrCat9/Sklearn_Note","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/agnesdeng/mixgb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/bw2color/bw2color","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/heartyguy/AI-AngryBird-Eagle-Wing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/jiangzhongkai/ifly-algorithm_challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/jlanday/Language-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/jlanday/Model-Selection-for-Pion-Photoproduction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/jlanday/Towards-the-Minimal-Spectrum-of-Excited-Baryons","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/jlanday/X-ray-Object-Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/joezengcbs/mini_xgboost_from_scratch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/kwantommy/breast-cancer-diagnosis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/lucasmfaria/Machine-Learning-Capstone","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/mtorabirad/PricePrediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/nsubbaian/FrequentistML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/omglu93/energy_consumption_eda_prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/osofr/xgboost","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/pierobeat/Hoax-News-Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/poyushen/classifaction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/tqchen/xgboost","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"xgboost-a-scalable-tree-boosting-system","repo_url":"https://github.com/xiadanqing/Binary","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"data-compression","task_name":"Data Compression"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"humor-detection","task_name":"Humor Detection"},{"task_slug":"video-super-resolution","task_name":"Video Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/humor-detection-on-200k-short-texts-for-humor-1","task":"Humor Detection","dataset":"200k Short Texts for Humor Detection","model":"XGBoost","rank_in_archive_order":5,"of":6,"metrics":{"F1-score":"0.813"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.02754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1603.02754"}},"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/dmlc/xgboost","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Javihaus/Auto-ML-app-with-dash","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/agnesdeng/mixgb","reach":{"status":"ok","spdx":"GPL-3.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/kwantommy/breast-cancer-diagnosis","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jlanday/Towards-the-Minimal-Spectrum-of-Excited-Baryons","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/poyushen/classifaction","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jiangzhongkai/ifly-algorithm_challenge","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jlanday/Model-Selection-for-Pion-Photoproduction","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/1082-datascience/finalproject-finalproject-1082ds_group3","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Automunge/AutoMunge","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/pierobeat/Hoax-News-Classification","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jlanday/Language-Detection","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/KPIxLILU/Machine-Learning-Workshop","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/lucasmfaria/Machine-Learning-Capstone","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/xiadanqing/Binary","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/MrCat9/Sklearn_Note","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/joezengcbs/mini_xgboost_from_scratch","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Hem7513/Decision-Trees-and-XGBoost-Algorithm-Documentation","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mtorabirad/PricePrediction","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/omglu93/energy_consumption_eda_prediction","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tqchen/xgboost","reach":{"status":"ok","spdx":"NOASSERTION"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/bw2color/bw2color","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jlanday/X-ray-Object-Classification","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Fickle519/stockforecastingmodelbasedon_XGboost","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/heartyguy/AI-AngryBird-Eagle-Wing","reach":{"status":"ok","spdx":"NOASSERTION"}}],"summary":{"unverified":17},"by_repo_kind":{"listed":{"samples":17,"ran":0,"repositories":2}},"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":"56d023d699f86812","entry":"allreduce","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/rabit.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/rabit.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"56d023d699f86812"}},{"code_sha256_prefix":"cb6b3b44c71c8b4d","entry":"broadcast","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/rabit.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/rabit.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"cb6b3b44c71c8b4d"}},{"code_sha256_prefix":"dcc420b0c0e4736f","entry":"ctypes2numpy","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/core.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/core.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"dcc420b0c0e4736f"}},{"code_sha256_prefix":"93ea111e4faa8eb4","entry":"evalauc","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/metrics.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/metrics.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"93ea111e4faa8eb4"}},{"code_sha256_prefix":"f3c98203a82d21f3","entry":"from_cstr_to_pystr","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/core.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/core.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"f3c98203a82d21f3"}},{"code_sha256_prefix":"b97f869f1e1f317a","entry":"from_pystr_to_cstr","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/core.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/core.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"b97f869f1e1f317a"}},{"code_sha256_prefix":"2283c0485da99f5e","entry":"groups_to_rows","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/training.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/training.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"2283c0485da99f5e"}},{"code_sha256_prefix":"a1073934e27565bc","entry":"loadfmap","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_src/demo/binary_classification/mapfeat.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_src/demo/binary_classification/mapfeat.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a1073934e27565bc"}},{"code_sha256_prefix":"17365dbebfb45226","entry":"loss_with_metrics","repo":"bw2color/bw2color","repo_kind":"listed","path":"colorization/training_utils.py","file_url":"https://github.com/bw2color/bw2color/blob/HEAD/colorization/training_utils.py","link_basis":"harvester_set","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":"17365dbebfb45226"}},{"code_sha256_prefix":"ec4a12f8317bbdd8","entry":"mkgroupfold","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/training.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/training.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"ec4a12f8317bbdd8"}},{"code_sha256_prefix":"02e322ab0e7455f6","entry":"plot_importance","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/plotting.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/plotting.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"02e322ab0e7455f6"}},{"code_sha256_prefix":"9b4357ad647895da","entry":"plot_tree","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/plotting.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/plotting.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"9b4357ad647895da"}},{"code_sha256_prefix":"2ede4efe489775eb","entry":"print_evaluation","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/callback.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/callback.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"2ede4efe489775eb"}},{"code_sha256_prefix":"da2df451a1ec6956","entry":"record_evaluation","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/callback.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/callback.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"da2df451a1ec6956"}},{"code_sha256_prefix":"c42441cb9c13a0b8","entry":"reset_learning_rate","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/callback.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/callback.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c42441cb9c13a0b8"}},{"code_sha256_prefix":"1495b6370c8c3330","entry":"to_graphviz","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/plotting.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/plotting.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"1495b6370c8c3330"}},{"code_sha256_prefix":"0deb98c24eb53d79","entry":"train","repo":"360jinrong/GBST","repo_kind":"listed","path":"gbst_package/gbst/training.py","file_url":"https://github.com/360jinrong/GBST/blob/HEAD/gbst_package/gbst/training.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"0deb98c24eb53d79"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}