{"id":"W3208821253","doi":"10.2200/s01123ed1v01y202108hlt053","title":"Pretrained Transformers for Text Ranking: BERT and Beyond","year":2021,"lang":"en","type":"article","venue":"Synthesis lectures on human language technologies","topic":"Topic Modeling","field":"Computer Science","cited_by":124,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Compute Canada","keywords":"Transformer; Computer science; Artificial intelligence; Natural language processing; Encoder; Ranking (information retrieval); Question answering; Information retrieval; Artificial neural network; 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":[0.003520818,0.001929322,0.002056973,0.002689804,0.0008967787,0.003632459,0.002754181,0.001885712,0.01947907],"category_scores_gemma":[0.01792308,0.000881967,0.001224731,0.002659205,0.0009429376,0.01002723,0.002441725,0.003959512,0.01288898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001315157,"about_ca_system_score_gemma":0.002592386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005368648,"about_ca_topic_score_gemma":0.01022841,"domain_scores_codex":[0.9973662,0.000870541,0.0001983626,0.000641576,0.0006601646,0.0002632854],"domain_scores_gemma":[0.9911221,0.00438601,0.0002233536,0.002583305,0.001388183,0.0002970955],"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":[0.0007045155,0.0002198158,0.0009694223,0.0003401917,0.00008778182,0.00007280132,0.0001013136,0.02466143,0.006132819,0.04528267,0.06565246,0.8557748],"study_design_scores_gemma":[0.0001191884,0.0002195175,0.0007094122,0.0001040731,0.00008015703,0.0001704192,0.0001091113,0.7815019,0.01184237,0.1822278,0.02286509,0.00005099403],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01190236,0.002534807,0.9588102,0.001192462,0.0005844251,0.0001313437,0.002285267,0.01680174,0.00575736],"genre_scores_gemma":[0.3954889,0.003056148,0.549694,0.0009845981,0.001948993,0.0003865282,0.01704538,0.003756399,0.02763904],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01947907,"threshold_uncertainty_score":0.06516397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02076188500124745,"score_gpt":0.2760993430802039,"score_spread":0.2553374580789565,"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."}}