{"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/create-data-loader","entry":"create_data_loader","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":8,"n_papers_ran":2,"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":8,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":2,"ran":0,"unverified":6},"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":"2404.09027","paper":"/paper/ming-moe-enhancing-medical-multi-task","title":"MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts","date":"2024-04-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mediabrain-sjtu/medicalgpt-zh","path":"ming/eval/model_diverse_gen.py","file_url":"https://github.com/mediabrain-sjtu/medicalgpt-zh/blob/HEAD/ming/eval/model_diverse_gen.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":"8948016310b7f832","mcp_get_code":{"code_sha256":"8948016310b7f832"}},{"arxiv_id":"2305.07759","paper":"/paper/tinystories-how-small-can-language-models-be","title":"TinyStories: How Small Can Language Models Be and Still Speak Coherent English?","date":"2023-05-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"danbraunai/simple_stories_train","path":"simple_stories_train/dataloaders.py","file_url":"https://github.com/danbraunai/simple_stories_train/blob/HEAD/simple_stories_train/dataloaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2425cc683fe447c3","mcp_get_code":{"code_sha256":"2425cc683fe447c3"}},{"arxiv_id":"2206.03230","paper":"/paper/shedding-a-pac-bayesian-light-on-adaptive","title":"Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances","date":"2022-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rubenohana/pac-bayesian_sliced-wasserstein","path":"swd_pac.py","file_url":"https://github.com/rubenohana/pac-bayesian_sliced-wasserstein/blob/HEAD/swd_pac.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"469cf34b179724c4","mcp_get_code":{"code_sha256":"469cf34b179724c4"}},{"arxiv_id":"2202.07304","paper":"/paper/xai-for-transformers-better-explanations","title":"XAI for Transformers: Better Explanations through Conservative Propagation","date":"2022-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ameenali/xai_transformers","path":"IMDB/imdb.py","file_url":"https://github.com/ameenali/xai_transformers/blob/HEAD/IMDB/imdb.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e68f6ccbc2173bdb","mcp_get_code":{"code_sha256":"e68f6ccbc2173bdb"}},{"arxiv_id":"2110.13266","paper":"/paper/image-quality-assessment-using-contrastive","title":"Image Quality Assessment using Contrastive Learning","date":"2021-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pavancm/conviqt","path":"demo_score.py","file_url":"https://github.com/pavancm/conviqt/blob/HEAD/demo_score.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e26510bc9e75a961","mcp_get_code":{"code_sha256":"e26510bc9e75a961"}},{"arxiv_id":"2109.13398","paper":"/paper/unrolling-sgd-understanding-factors","title":"Unrolling SGD: Understanding Factors Influencing Machine Unlearning","date":"2021-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cleverhans-lab/unrolling-sgd","path":"BERT/bert_helpers.py","file_url":"https://github.com/cleverhans-lab/unrolling-sgd/blob/HEAD/BERT/bert_helpers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"63ae7db4794f4b09","mcp_get_code":{"code_sha256":"63ae7db4794f4b09"}},{"arxiv_id":"1511.06434","paper":"/paper/unsupervised-representation-learning-with-1","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","date":"2015-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shivamswarnkar/Image-Generator","path":"utils/data.py","file_url":"https://github.com/shivamswarnkar/Image-Generator/blob/HEAD/utils/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0f467eb93c999baa","mcp_get_code":{"code_sha256":"0f467eb93c999baa"}},{"arxiv_id":"2023.findings-acl.805","paper":null,"title":"arXiv:2023.findings-acl.805","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"iesl/Softmax-CPR","path":"src/LM/utils.py","file_url":"https://github.com/iesl/Softmax-CPR/blob/HEAD/src/LM/utils.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":"6609e39289bdad39","mcp_get_code":{"code_sha256":"6609e39289bdad39"}}]}