{"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/count-features","entry":"count_features","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":7,"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":2,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":1,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":1},"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":"2608.06589","paper":"/paper/arxiv-2608-06589","title":"Beyond \"AI Language\": The case for the idiolectal nature of LLM output","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"fsu-nlp/ai-idiolects","path":"src/aiidiolects/discourse_features.py","file_url":"https://github.com/fsu-nlp/ai-idiolects/blob/HEAD/src/aiidiolects/discourse_features.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"16602dd75d605ace","mcp_get_code":{"code_sha256":"16602dd75d605ace"}},{"arxiv_id":"2003.13027","paper":"/paper/abstractive-text-summarization-based-on","title":"Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling","date":"2020-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"axenov/BERT-Summ-OpenNMT","path":"preprocess.py","file_url":"https://github.com/axenov/BERT-Summ-OpenNMT/blob/HEAD/preprocess.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c8cfd6442c243f1","mcp_get_code":{"code_sha256":"0c8cfd6442c243f1"}},{"arxiv_id":"1907.08854","paper":"/paper/incremental-transformer-with-deliberation","title":"Incremental Transformer with Deliberation Decoder for Document Grounded Conversations","date":"2019-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lizekang/ITDD","path":"preprocess.py","file_url":"https://github.com/lizekang/ITDD/blob/HEAD/preprocess.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c8cfd6442c243f1","mcp_get_code":{"code_sha256":"0c8cfd6442c243f1"}},{"arxiv_id":"1906.07651","paper":"/paper/scheduled-sampling-for-transformers","title":"Scheduled Sampling for Transformers","date":"2019-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deep-spin/scheduled-sampling-transformers","path":"preprocess.py","file_url":"https://github.com/deep-spin/scheduled-sampling-transformers/blob/HEAD/preprocess.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c8cfd6442c243f1","mcp_get_code":{"code_sha256":"0c8cfd6442c243f1"}},{"arxiv_id":"1906.06253","paper":"/paper/a-simple-and-effective-approach-to-automatic","title":"A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning","date":"2019-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deep-spin/OpenNMT-APE","path":"preprocess.py","file_url":"https://github.com/deep-spin/OpenNMT-APE/blob/HEAD/preprocess.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c8cfd6442c243f1","mcp_get_code":{"code_sha256":"0c8cfd6442c243f1"}},{"arxiv_id":"1809.07358","paper":"/paper/a-dataset-for-document-grounded-conversations","title":"A Dataset for Document Grounded Conversations","date":"2018-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"0c8cfd6442c243f1","mcp_get_code":{"code_sha256":"0c8cfd6442c243f1"}},{"arxiv_id":"1805.11462","paper":"/paper/opennmt-neural-machine-translation-toolkit","title":"OpenNMT: Neural Machine Translation Toolkit","date":"2018-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Unbabel/OpenNMT-py","path":"preprocess.py","file_url":"https://github.com/Unbabel/OpenNMT-py/blob/HEAD/preprocess.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c8cfd6442c243f1","mcp_get_code":{"code_sha256":"0c8cfd6442c243f1"}},{"arxiv_id":"2020.acl-main.640","paper":null,"title":"arXiv:2020.acl-main.640","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"QAQ-v/HetGT","path":"preprocess.py","file_url":"https://github.com/QAQ-v/HetGT/blob/HEAD/preprocess.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c8cfd6442c243f1","mcp_get_code":{"code_sha256":"0c8cfd6442c243f1"}}]}