{"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/bertselfoutput","entry":"BertSelfOutput","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":14,"n_papers_ran":10,"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":17,"n_samples_ran":11,"n_samples_fingerprinted":1,"n_places":17,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":11,"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":"2603.05969","paper":"/paper/arxiv-2603-05969","title":"Imagine How To Change: Explicit Procedure Modeling for Change Captioning","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"BlueberryOreo/ProCap","path":"src/rtransformer/model.py","file_url":"https://github.com/BlueberryOreo/ProCap/blob/HEAD/src/rtransformer/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bdf10bafd164be1e","mcp_get_code":{"code_sha256":"bdf10bafd164be1e"}},{"arxiv_id":"2511.21416","paper":"/paper/arxiv-2511-21416","title":"Odin: Oriented Dual-module Integration for Text-rich Network Representation Learning","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"hongkaifeng/Odin","path":"src/OpenLP/models/Graphformer ME.py","file_url":"https://github.com/hongkaifeng/Odin/blob/HEAD/src/OpenLP/models/Graphformer%20ME.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d2a01c9ffae7873c","mcp_get_code":{"code_sha256":"d2a01c9ffae7873c"}},{"arxiv_id":"2405.00390","paper":"/paper/cofipara-a-coarse-to-fine-paradigm-for","title":"CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models","date":"2024-05-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wjq-learning/MSTI","path":"model.py","file_url":"https://github.com/wjq-learning/MSTI/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8412d3cacea13366","mcp_get_code":{"code_sha256":"8412d3cacea13366"}},{"arxiv_id":"2402.13040","paper":"/paper/text-guided-molecule-generation-with","title":"Text-Guided Molecule Generation with Diffusion Language Model","date":"2024-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Deno-V/tgm-dlm","path":"improved-diffusion/improved_diffusion/transformer_model.py","file_url":"https://github.com/Deno-V/tgm-dlm/blob/HEAD/improved-diffusion/improved_diffusion/transformer_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b628485b4544176e","mcp_get_code":{"code_sha256":"b628485b4544176e"}},{"arxiv_id":"2308.12587","paper":"/paper/grounded-entity-landmark-adaptive-pre","title":"Grounded Entity-Landmark Adaptive Pre-training for Vision-and-Language Navigation","date":"2023-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csir1996/vln-gela","path":"ada_pretrain_src/model/pretrain_cmt.py","file_url":"https://github.com/csir1996/vln-gela/blob/HEAD/ada_pretrain_src/model/pretrain_cmt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d50e815eff85ec2b","mcp_get_code":{"code_sha256":"d50e815eff85ec2b"}},{"arxiv_id":"2305.12268","paper":"/paper/patton-language-model-pretraining-on-text","title":"Patton: Language Model Pretraining on Text-Rich Networks","date":"2023-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PeterGriffinJin/Patton","path":"src/OpenLP/models/Graphformer.py","file_url":"https://github.com/PeterGriffinJin/Patton/blob/HEAD/src/OpenLP/models/Graphformer.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":"e7ff4d48905784eb","mcp_get_code":{"code_sha256":"e7ff4d48905784eb"}},{"arxiv_id":"2210.08714","paper":"/paper/selective-query-guided-debiasing-network-for","title":"Selective Query-guided Debiasing for Video Corpus Moment Retrieval","date":"2022-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dbstjswo505/SQuiDNet","path":"model/squidnet.py","file_url":"https://github.com/dbstjswo505/SQuiDNet/blob/HEAD/model/squidnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cd8f5862f19a2eb6","mcp_get_code":{"code_sha256":"cd8f5862f19a2eb6"}},{"arxiv_id":"2110.13309","paper":"/paper/history-aware-multimodal-transformer-for","title":"History Aware Multimodal Transformer for Vision-and-Language Navigation","date":"2021-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cshizhe/vln-hamt","path":"finetune_src/models/vilmodel_cmt.py","file_url":"https://github.com/cshizhe/vln-hamt/blob/HEAD/finetune_src/models/vilmodel_cmt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2e4a0b2327855df3","mcp_get_code":{"code_sha256":"2e4a0b2327855df3"}},{"arxiv_id":"2108.00061","paper":"/paper/mtvr-multilingual-moment-retrieval-in-videos","title":"MTVR: Multilingual Moment Retrieval in Videos","date":"2021-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jayleicn/mTVRetrieval","path":"baselines/mxml/model_xml_shared.py","file_url":"https://github.com/jayleicn/mTVRetrieval/blob/HEAD/baselines/mxml/model_xml_shared.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a35f293effa83702","mcp_get_code":{"code_sha256":"a35f293effa83702"}},{"arxiv_id":"2106.14019","paper":"/paper/umic-an-unreferenced-metric-for-image","title":"UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning","date":"2021-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hwanheelee1993/UMIC","path":"model/ce.py","file_url":"https://github.com/hwanheelee1993/UMIC/blob/HEAD/model/ce.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"10c57c28582d7a4d","mcp_get_code":{"code_sha256":"10c57c28582d7a4d"}},{"arxiv_id":"2010.08210","paper":"/paper/coarse-to-fine-pre-training-for-named-entity","title":"Coarse-to-Fine Pre-training for Named Entity Recognition","date":"2020-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"strawberryx/CoFEE","path":"model/bert_mrc_ner_cluster.py","file_url":"https://github.com/strawberryx/CoFEE/blob/HEAD/model/bert_mrc_ner_cluster.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c774d5dca62d41fb","mcp_get_code":{"code_sha256":"c774d5dca62d41fb"}},{"arxiv_id":"2004.13278","paper":"/paper/vd-bert-a-unified-vision-and-dialog","title":"VD-BERT: A Unified Vision and Dialog Transformer with BERT","date":"2020-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salesforce/VD-BERT","path":"pytorch_pretrained_bert/modeling.py","file_url":"https://github.com/salesforce/VD-BERT/blob/HEAD/pytorch_pretrained_bert/modeling.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3b0ae0e2b34fdf2e","mcp_get_code":{"code_sha256":"3b0ae0e2b34fdf2e"}},{"arxiv_id":"1907.11692","paper":"/paper/roberta-a-robustly-optimized-bert-pretraining","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","date":"2019-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data","path":"run_hbm.py","file_url":"https://github.com/GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data/blob/HEAD/run_hbm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fcf3cd282eb03efe","mcp_get_code":{"code_sha256":"fcf3cd282eb03efe"}},{"arxiv_id":"1810.04805","paper":"/paper/bert-pre-training-of-deep-bidirectional","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","date":"2018-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cybertronai/megatron-lm","path":"model/modeling.py","file_url":"https://github.com/cybertronai/megatron-lm/blob/HEAD/model/modeling.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5d2d7e3a1b6801df","mcp_get_code":{"code_sha256":"5d2d7e3a1b6801df"}},{"arxiv_id":"1810.04805","paper":"/paper/bert-pre-training-of-deep-bidirectional","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","date":"2018-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maknotavailable/pytorch-pretrained-bert","path":"pytorch_pretrained_bert/modeling.py","file_url":"https://github.com/maknotavailable/pytorch-pretrained-bert/blob/HEAD/pytorch_pretrained_bert/modeling.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5c90e4d2568bfcd1","mcp_get_code":{"code_sha256":"5c90e4d2568bfcd1"}},{"arxiv_id":"1810.04805","paper":"/paper/bert-pre-training-of-deep-bidirectional","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","date":"2018-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cedrickchee/pytorch-pretrained-BERT","path":"pytorch_pretrained_bert/modeling.py","file_url":"https://github.com/cedrickchee/pytorch-pretrained-BERT/blob/HEAD/pytorch_pretrained_bert/modeling.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":"5a3875e5bed5964b","mcp_get_code":{"code_sha256":"5a3875e5bed5964b"}},{"arxiv_id":"1810.04805","paper":"/paper/bert-pre-training-of-deep-bidirectional","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","date":"2018-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"derronxu/sparsebert","path":"SparseBERT/main_functions/transformer/modeling.py","file_url":"https://github.com/derronxu/sparsebert/blob/HEAD/SparseBERT/main_functions/transformer/modeling.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"178a7c8f563f0beb","mcp_get_code":{"code_sha256":"178a7c8f563f0beb"}}]}