{"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":"/code/build-embedding-matrix","entry":"build_embedding_matrix","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":5,"n_papers_ran":0,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":5,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":5},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2410.24105","paper":"/paper/matchmaker-self-improving-large-language","title":"Matchmaker: Self-Improving Large Language Model Programs for Schema Matching","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JZCS2018/SMAT","path":"data_utils.py","file_url":"https://github.com/JZCS2018/SMAT/blob/HEAD/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5aed3763dd886c9a","mcp_get_code":{"code_sha256":"5aed3763dd886c9a"}},{"arxiv_id":"2109.07177","paper":"/paper/adversarial-mixing-policy-for-relaxing","title":"Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup","date":"2021-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"songyouwei/ABSA-PyTorch","path":"data_utils.py","file_url":"https://github.com/songyouwei/ABSA-PyTorch/blob/HEAD/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbd3d55c5e5a0fc7","mcp_get_code":{"code_sha256":"dbd3d55c5e5a0fc7"}},{"arxiv_id":"2002.10710","paper":"/paper/end-to-end-emotion-cause-pair-extraction-via","title":"End-to-end Emotion-Cause Pair Extraction via Learning to Link","date":"2020-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shl5133/E2EECPE","path":"data_utils.py","file_url":"https://github.com/shl5133/E2EECPE/blob/HEAD/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f1a136b31b010ebc","mcp_get_code":{"code_sha256":"f1a136b31b010ebc"}},{"arxiv_id":"1512.01100","paper":"/paper/effective-lstms-for-target-dependent","title":"Effective LSTMs for Target-Dependent Sentiment Classification","date":"2015-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hiyouga/PBAN-PyTorch","path":"data_utils.py","file_url":"https://github.com/hiyouga/PBAN-PyTorch/blob/HEAD/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3a29afb1afccd2d0","mcp_get_code":{"code_sha256":"3a29afb1afccd2d0"}},{"arxiv_id":"aaai_6517","paper":null,"title":"arXiv:aaai_6517","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"hiyouga/RepWalk","path":"data_utils.py","file_url":"https://github.com/hiyouga/RepWalk/blob/HEAD/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"910bb0ee01e68890","mcp_get_code":{"code_sha256":"910bb0ee01e68890"}}]}