{"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/multilabel-categorical-crossentropy","entry":"multilabel_categorical_crossentropy","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":6,"n_papers_ran":1,"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":4,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":3},"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":"2311.05419","paper":"/paper/mirror-a-universal-framework-for-various","title":"Mirror: A Universal Framework for Various Information Extraction Tasks","date":"2023-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Spico197/Mirror","path":"src/model.py","file_url":"https://github.com/Spico197/Mirror/blob/HEAD/src/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d612fab4384dbec0","mcp_get_code":{"code_sha256":"d612fab4384dbec0"}},{"arxiv_id":"2304.09048","paper":"/paper/codekgc-code-language-model-for-generative","title":"CodeKGC: Code Language Model for Generative Knowledge Graph Construction","date":"2023-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjunlp/DeepKE","path":"src/deepke/relation_extraction/document/losses.py","file_url":"https://github.com/zjunlp/DeepKE/blob/HEAD/src/deepke/relation_extraction/document/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9f47bb60899af064","mcp_get_code":{"code_sha256":"9f47bb60899af064"}},{"arxiv_id":"2210.10678","paper":"/paper/towards-realistic-low-resource-relation","title":"Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study","date":"2022-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjunlp/KnowPrompt","path":"lit_models/transformer.py","file_url":"https://github.com/zjunlp/KnowPrompt/blob/HEAD/lit_models/transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d235c954a3acaf08","mcp_get_code":{"code_sha256":"d235c954a3acaf08"}},{"arxiv_id":"2210.00312","paper":"/paper/multimodal-analogical-reasoning-over","title":"Multimodal Analogical Reasoning over Knowledge Graphs","date":"2022-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjunlp/MKGformer","path":"MKG/lit_models/transformer.py","file_url":"https://github.com/zjunlp/MKGformer/blob/HEAD/MKG/lit_models/transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d235c954a3acaf08","mcp_get_code":{"code_sha256":"d235c954a3acaf08"}},{"arxiv_id":"2204.13413","paper":"/paper/hpt-hierarchy-aware-prompt-tuning-for","title":"HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification","date":"2022-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wzh9969/HPT","path":"models/prompt.py","file_url":"https://github.com/wzh9969/HPT/blob/HEAD/models/prompt.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bb58bb20e888b915","mcp_get_code":{"code_sha256":"bb58bb20e888b915"}},{"arxiv_id":"2023.findings-emnlp.594","paper":null,"title":"arXiv:2023.findings-emnlp.594","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"simonucl/HJCL","path":"model/model.py","file_url":"https://github.com/simonucl/HJCL/blob/HEAD/model/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"bb58bb20e888b915","mcp_get_code":{"code_sha256":"bb58bb20e888b915"}}]}