{"id":"W3042667808","doi":"10.18653/v1/2020.emnlp-demos.7","title":"AdapterHub: A Framework for Adapting Transformers","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Samsung; DeepMind; Samsung Advanced Institute of Technology","keywords":"Computer science; Adapter (computing); Bottleneck; Upload; Scalability; Scripting language; Transformer; Artificial intelligence; Distributed computing; Software engineering; World Wide Web; Embedded system; Programming language; Operating system","routes":{"ca_aff":true,"ca_fund":false,"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":[0.007512625,0.002338494,0.001637686,0.002920664,0.001394292,0.004624739,0.006171761,0.002387168,0.03238381],"category_scores_gemma":[0.02326425,0.003050901,0.003655462,0.002813965,0.001905843,0.01613277,0.0110675,0.004446851,0.01562268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001358367,"about_ca_system_score_gemma":0.002510024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004758017,"about_ca_topic_score_gemma":0.00669023,"domain_scores_codex":[0.9955078,0.0013488,0.0005719995,0.0009280678,0.001224003,0.0004193391],"domain_scores_gemma":[0.9887715,0.004400083,0.0002984584,0.005252697,0.0008828141,0.0003943959],"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":[0.001634377,0.0004175601,0.003830108,0.001780665,0.0004284564,0.0008396397,0.001249038,0.0153295,0.01315468,0.2216464,0.2633615,0.476328],"study_design_scores_gemma":[0.0004600918,0.0001378761,0.0006221,0.0004442146,0.0002558038,0.0007094814,0.0003399603,0.1302946,0.02562593,0.3118512,0.5290464,0.0002125255],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002070819,0.0004584135,0.7911213,0.0003571594,0.0002452906,0.000348457,0.002502779,0.1965931,0.006302663],"genre_scores_gemma":[0.07467129,0.001814115,0.8151979,0.001209568,0.0002884409,0.001457918,0.01724576,0.07352491,0.01459013],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.03238381,"threshold_uncertainty_score":0.1083347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0962412776325239,"score_gpt":0.3059694317501878,"score_spread":0.2097281541176639,"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."}}