{"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/train-and-validate","entry":"train_and_validate","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":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":6,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"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":"2412.01108","paper":"/paper/multi-scale-representation-learning-for","title":"Multi-Scale Representation Learning for Protein Fitness Prediction","date":"2024-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepgraphlearning/s3f","path":"script/pretrain.py","file_url":"https://github.com/deepgraphlearning/s3f/blob/HEAD/script/pretrain.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4437a10c67d7e3b8","mcp_get_code":{"code_sha256":"4437a10c67d7e3b8"}},{"arxiv_id":"2303.06275","paper":"/paper/enhancing-protein-language-models-with","title":"A Systematic Study of Joint Representation Learning on Protein Sequences and Structures","date":"2023-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"30bb4a1a37663d9c","mcp_get_code":{"code_sha256":"30bb4a1a37663d9c"}},{"arxiv_id":"2303.06275","paper":"/paper/enhancing-protein-language-models-with","title":"A Systematic Study of Joint Representation Learning on Protein Sequences and Structures","date":"2023-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"81beaaea01060569","mcp_get_code":{"code_sha256":"81beaaea01060569"}},{"arxiv_id":"2301.12040","paper":"/paper/protst-multi-modality-learning-of-protein","title":"ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts","date":"2023-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeepGraphLearning/ProtST","path":"script/run_downstream.py","file_url":"https://github.com/DeepGraphLearning/ProtST/blob/HEAD/script/run_downstream.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"849819576c34835d","mcp_get_code":{"code_sha256":"849819576c34835d"}},{"arxiv_id":"2206.02096","paper":"/paper/peer-a-comprehensive-and-multi-task-benchmark","title":"PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding","date":"2022-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeepGraphLearning/PEER_Benchmark","path":"script/run_single.py","file_url":"https://github.com/DeepGraphLearning/PEER_Benchmark/blob/HEAD/script/run_single.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":"6cdd2cf240cdfce8","mcp_get_code":{"code_sha256":"6cdd2cf240cdfce8"}},{"arxiv_id":"2206.02096","paper":"/paper/peer-a-comprehensive-and-multi-task-benchmark","title":"PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding","date":"2022-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeepGraphLearning/PEER_Benchmark","path":"script/run_multi.py","file_url":"https://github.com/DeepGraphLearning/PEER_Benchmark/blob/HEAD/script/run_multi.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":"2cb1b6e4c11814b4","mcp_get_code":{"code_sha256":"2cb1b6e4c11814b4"}},{"arxiv_id":"2203.06125","paper":"/paper/protein-structure-representation-learning-by","title":"Protein Representation Learning by Geometric Structure Pretraining","date":"2022-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepgraphlearning/esm-gearnet","path":"script/downstream.py","file_url":"https://github.com/deepgraphlearning/esm-gearnet/blob/HEAD/script/downstream.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"30bb4a1a37663d9c","mcp_get_code":{"code_sha256":"30bb4a1a37663d9c"}},{"arxiv_id":"2203.06125","paper":"/paper/protein-structure-representation-learning-by","title":"Protein Representation Learning by Geometric Structure Pretraining","date":"2022-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepgraphlearning/gearnet","path":"script/downstream.py","file_url":"https://github.com/deepgraphlearning/gearnet/blob/HEAD/script/downstream.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"81beaaea01060569","mcp_get_code":{"code_sha256":"81beaaea01060569"}}]}