{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001226523,0.00005981282,0.00008264808,0.00002358812,0.00006050506,0.00007939426,0.0001295814,0.00003465,0.00002800592],"category_scores_gemma":[0.00002058745,0.00005352227,0.00003633648,0.00009105678,0.00001218223,0.0001998977,0.00003464469,0.00003819614,0.000001607524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007893111,"about_ca_system_score_gemma":0.00004999265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004190443,"about_ca_topic_score_gemma":0.00001802816,"domain_scores_codex":[0.9993906,0.000009475436,0.0001051436,0.0002492287,0.00009141859,0.0001541144],"domain_scores_gemma":[0.9996932,0.00006554239,0.0000109417,0.0001369424,0.00004150891,0.00005182038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005609987,0.00002629039,0.0001534814,0.00004823695,0.00002558426,0.000006922374,0.002320086,0.000035812,0.004686542,0.3070015,0.000700419,0.6849895],"study_design_scores_gemma":[0.001798322,0.00008402803,0.0004290275,0.00001692081,0.0000144787,0.000053728,0.0002478558,0.9106173,0.02441437,0.03359456,0.02839273,0.0003366589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006757188,0.0001209869,0.9676146,0.004176356,0.0001333839,0.0001147442,8.359981e-7,0.00006110306,0.02102082],"genre_scores_gemma":[0.6949899,0.00001187332,0.3011563,0.001186919,0.00003439172,0.00001273628,0.000001524875,0.000004926882,0.002601472],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9105815,"threshold_uncertainty_score":0.2182575,"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."}}