{"id":"W4381683870","doi":"10.22214/ijraset.2023.53887","title":"Universal Language Model Fine-Tuning for Text Classification","year":2023,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Topic Modeling","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Language model; Transfer of learning; Categorization; Classifier (UML); Natural language processing; Task (project management); Fine-tuning; Deep learning; Process (computing); Natural language; Machine learning; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002476422,0.00005557792,0.00006760408,0.002176995,0.0001832785,0.0001908669,0.0014145,0.00005819332,3.197774e-7],"category_scores_gemma":[0.0004164313,0.00005523822,0.00001519923,0.001188209,0.0001166859,0.0002531391,0.0003324203,0.000286702,0.000002513639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002201318,"about_ca_system_score_gemma":0.0001872487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002366418,"about_ca_topic_score_gemma":0.000002098848,"domain_scores_codex":[0.9987254,0.000003575603,0.0001457937,0.0002583888,0.0004799136,0.000386968],"domain_scores_gemma":[0.999307,0.0001425223,0.00002842533,0.0001556391,0.0003077656,0.00005863532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008074262,0.000007688482,0.00002140593,0.000007523963,0.000004537571,0.000007936156,0.0003219411,0.03732209,0.170033,0.6965841,0.0001721608,0.09550954],"study_design_scores_gemma":[0.0002630101,0.00001950811,0.00004059594,0.00001913189,3.313259e-7,0.00001853505,0.000283284,0.9601672,0.001354373,0.03662727,0.001152327,0.00005446269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06654375,0.00001669726,0.9248298,0.007852394,0.0002634208,0.0001794081,0.000002067931,0.0001330701,0.0001794308],"genre_scores_gemma":[0.9106686,0.00002202922,0.08911319,0.00001598957,0.00005574345,0.00006517778,8.674227e-7,0.000005590137,0.00005284726],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9228451,"threshold_uncertainty_score":0.2628517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1047353107758972,"score_gpt":0.3965053319277147,"score_spread":0.2917700211518174,"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."}}