{"id":"W3134665270","doi":"10.1145/3437963.3441667","title":"Pretrained Transformers for Text Ranking: BERT and Beyond","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":164,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Transformer; Computer science; Artificial intelligence; Ranking (information retrieval); Natural language processing; Question answering; Language model; Information retrieval; Machine learning; Natural language; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001568458,0.00109325,0.0009175154,0.0009416434,0.0003948601,0.001917512,0.001373353,0.0009995602,0.004829126],"category_scores_gemma":[0.007527633,0.0004887999,0.0005765911,0.001441108,0.00108127,0.006492107,0.001248245,0.003244196,0.002751776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000998322,"about_ca_system_score_gemma":0.001076614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00361806,"about_ca_topic_score_gemma":0.005166466,"domain_scores_codex":[0.9993948,0.0002260243,0.00003889062,0.0001206794,0.0001585257,0.00006106253],"domain_scores_gemma":[0.9977437,0.001298977,0.0001159178,0.0004203538,0.0003345404,0.00008641028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002479355,0.0001325035,0.0008684231,0.0003842599,0.00007573023,0.0001026982,0.0002581379,0.1044411,0.009080168,0.1432115,0.01980026,0.7213973],"study_design_scores_gemma":[0.00002040326,0.0001258974,0.0004172041,0.00007113482,0.0000314373,0.00009552448,0.0000657105,0.7988894,0.006053129,0.1804181,0.01378081,0.00003122498],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.008559169,0.003641817,0.9782532,0.00151344,0.0001743984,0.0000712698,0.000244224,0.00267221,0.004870428],"genre_scores_gemma":[0.4324582,0.01071468,0.5252085,0.001337258,0.001006131,0.0002998373,0.001908835,0.001276732,0.02578983],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004829126,"threshold_uncertainty_score":0.01615506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01803717118089927,"score_gpt":0.2431211939237196,"score_spread":0.2250840227428204,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}