{"id":"W3154971029","doi":"10.18653/v1/2021.eacl-main.8","title":"BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Waterloo","funders":"Vector Institute; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Inference; Fine-tuning; Extension (predicate logic); Acceleration; Code (set theory); Quality (philosophy); Look-ahead; Regression; Machine learning; Artificial intelligence; Algorithm; Programming language","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":[0.004264983,0.001727358,0.001831586,0.0009538902,0.0006713748,0.001624962,0.002953221,0.001928654,0.006320339],"category_scores_gemma":[0.01164935,0.0008799411,0.001137567,0.0006403887,0.0009001087,0.00323504,0.002790555,0.005101318,0.002577407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008215127,"about_ca_system_score_gemma":0.001663142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004804944,"about_ca_topic_score_gemma":0.006544614,"domain_scores_codex":[0.9986821,0.0003994937,0.0000748996,0.0003682754,0.0002649553,0.0002102171],"domain_scores_gemma":[0.9965736,0.00179968,0.0002148091,0.0007148983,0.0004542665,0.0002427474],"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":[0.001056024,0.0005903226,0.004185958,0.0003034437,0.0001695827,0.000327353,0.0003561091,0.4068226,0.01755379,0.02147705,0.0150199,0.5321378],"study_design_scores_gemma":[0.00003894439,0.00007792188,0.0002431285,0.00001490254,0.00001206732,0.00003969081,0.00001248877,0.9909025,0.00258918,0.004417153,0.001637692,0.00001423498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01692767,0.0003646236,0.9724154,0.0002654404,0.0001109891,0.00008662585,0.0001079685,0.008099161,0.001622069],"genre_scores_gemma":[0.3921251,0.0003182464,0.5914615,0.0007175255,0.000264752,0.0003651658,0.001232986,0.002445013,0.01106966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006320339,"threshold_uncertainty_score":0.02255565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0309562390184768,"score_gpt":0.2613005794683832,"score_spread":0.2303443404499064,"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."}}