{ "results": { "assin2_rte": { "f1_macro,all": 0.8836486323653452, "acc,all": 0.8839869281045751, "alias": "assin2_rte" }, "assin2_sts": { "pearson,all": 0.6678266192299295, "mse,all": 0.6669526143790849, "alias": "assin2_sts" }, "bluex": { "acc,all": 0.47983310152990266, "acc,exam_id__USP_2021": 0.36538461538461536, "acc,exam_id__UNICAMP_2021_2": 0.37254901960784315, "acc,exam_id__UNICAMP_2023": 0.4418604651162791, "acc,exam_id__UNICAMP_2021_1": 0.45652173913043476, "acc,exam_id__USP_2024": 0.7073170731707317, "acc,exam_id__UNICAMP_2018": 0.42592592592592593, "acc,exam_id__USP_2022": 0.5510204081632653, "acc,exam_id__UNICAMP_2020": 0.4909090909090909, "acc,exam_id__USP_2018": 0.5, "acc,exam_id__USP_2020": 0.42857142857142855, "acc,exam_id__UNICAMP_2022": 0.46153846153846156, "acc,exam_id__USP_2019": 0.425, "acc,exam_id__USP_2023": 0.5909090909090909, "acc,exam_id__UNICAMP_2019": 0.56, "acc,exam_id__UNICAMP_2024": 0.4666666666666667, "alias": "bluex" }, "enem_challenge": { "alias": "enem", "acc,all": 0.5787263820853744, "acc,exam_id__2016_2": 0.5284552845528455, "acc,exam_id__2009": 0.591304347826087, "acc,exam_id__2011": 0.6666666666666666, "acc,exam_id__2012": 0.6206896551724138, "acc,exam_id__2013": 0.5925925925925926, "acc,exam_id__2016": 0.5537190082644629, "acc,exam_id__2022": 0.5037593984962406, "acc,exam_id__2023": 0.5777777777777777, "acc,exam_id__2010": 0.5555555555555556, "acc,exam_id__2014": 0.5963302752293578, "acc,exam_id__2015": 0.6218487394957983, "acc,exam_id__2017": 0.5517241379310345 }, "faquad_nli": { "f1_macro,all": 0.7017672651113582, "acc,all": 0.7384615384615385, "alias": "faquad_nli" }, "hatebr_offensive": { "alias": "hatebr_offensive_binary", "f1_macro,all": 0.8176778106453834, "acc,all": 0.82 }, "oab_exams": { "acc,all": 0.3931662870159453, "acc,exam_id__2010-02": 0.46, "acc,exam_id__2016-19": 0.5, "acc,exam_id__2015-17": 0.4358974358974359, "acc,exam_id__2016-21": 0.4, "acc,exam_id__2017-24": 0.325, "acc,exam_id__2012-09": 0.37662337662337664, "acc,exam_id__2011-04": 0.3125, "acc,exam_id__2017-23": 0.4375, "acc,exam_id__2011-03": 0.41414141414141414, "acc,exam_id__2012-07": 0.3375, "acc,exam_id__2012-06": 0.375, "acc,exam_id__2014-13": 0.35, "acc,exam_id__2016-20a": 0.225, "acc,exam_id__2011-05": 0.3875, "acc,exam_id__2015-18": 0.425, "acc,exam_id__2014-15": 0.5384615384615384, "acc,exam_id__2018-25": 0.4125, "acc,exam_id__2017-22": 0.425, "acc,exam_id__2013-11": 0.425, "acc,exam_id__2014-14": 0.3625, "acc,exam_id__2013-10": 0.3375, "acc,exam_id__2010-01": 0.32941176470588235, "acc,exam_id__2013-12": 0.475, "acc,exam_id__2015-16": 0.3875, "acc,exam_id__2012-06a": 0.3875, "acc,exam_id__2016-20": 0.3875, "acc,exam_id__2012-08": 0.375, "alias": "oab_exams" }, "portuguese_hate_speech": { "alias": "portuguese_hate_speech_binary", "f1_macro,all": 0.6658626171810755, "acc,all": 0.6886016451233843 }, "tweetsentbr": { "f1_macro,all": 0.614191091794639, "acc,all": 0.6681592039800995, "alias": "tweetsentbr" } }, "configs": { "assin2_rte": { "task": "assin2_rte", "group": [ "pt_benchmark", "assin2" ], "dataset_path": "assin2", "test_split": "test", "fewshot_split": "train", "doc_to_text": "Premissa: {{premise}}\nHipótese: {{hypothesis}}\nPergunta: A hipótese pode ser inferida pela premissa? Sim ou Não?\nResposta:", "doc_to_target": "{{['Não', 'Sim'][entailment_judgment]}}", "description": "Abaixo estão pares de premissa e hipótese. Para cada par, indique se a hipótese pode ser inferida a partir da premissa, responda apenas com \"Sim\" ou \"Não\".\n\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "id_sampler", "sampler_config": { "id_list": [ 1, 3251, 2, 3252, 3, 4, 5, 6, 3253, 7, 3254, 3255, 3256, 8, 9, 10, 3257, 11, 3258, 12, 13, 14, 15, 3259, 3260, 3261, 3262, 3263, 16, 17, 3264, 18, 3265, 3266, 3267, 19, 20, 3268, 3269, 21, 3270, 3271, 22, 3272, 3273, 23, 3274, 24, 25, 3275 ], "id_column": "sentence_pair_id" } }, "num_fewshot": 15, "metric_list": [ { "metric": "f1_macro", "aggregation": "f1_macro", "higher_is_better": true }, { "metric": "acc", "aggregation": "acc", "higher_is_better": true } ], "output_type": "generate_until", "generation_kwargs": { "max_gen_toks": 32, "do_sample": false, "temperature": 0.0, "top_k": null, "top_p": null, "until": [ "\n\n" ] }, "repeats": 1, "filter_list": [ { "name": "all", "filter": [ { "function": "find_similar_label", "labels": [ "Sim", "Não" ] }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.1 } }, "assin2_sts": { "task": "assin2_sts", "group": [ "pt_benchmark", "assin2" ], "dataset_path": "assin2", "test_split": "test", "fewshot_split": "train", "doc_to_text": "Frase 1: {{premise}}\nFrase 2: {{hypothesis}}\nPergunta: Quão similares são as duas frases? Dê uma pontuação entre 1,0 a 5,0.\nResposta:", "doc_to_target": "", "description": "Abaixo estão pares de frases que você deve avaliar o grau de similaridade. Dê uma pontuação entre 1,0 e 5,0, sendo 1,0 pouco similar e 5,0 muito similar.\n\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "id_sampler", "sampler_config": { "id_list": [ 1, 3251, 2, 3252, 3, 4, 5, 6, 3253, 7, 3254, 3255, 3256, 8, 9, 10, 3257, 11, 3258, 12, 13, 14, 15, 3259, 3260, 3261, 3262, 3263, 16, 17, 3264, 18, 3265, 3266, 3267, 19, 20, 3268, 3269, 21, 3270, 3271, 22, 3272, 3273, 23, 3274, 24, 25, 3275 ], "id_column": "sentence_pair_id" } }, "num_fewshot": 15, "metric_list": [ { "metric": "pearson", "aggregation": "pearsonr", "higher_is_better": true }, { "metric": "mse", "aggregation": "mean_squared_error", "higher_is_better": false } ], "output_type": "generate_until", "generation_kwargs": { "max_gen_toks": 32, "do_sample": false, "temperature": 0.0, "top_k": null, "top_p": null, "until": [ "\n\n" ] }, "repeats": 1, "filter_list": [ { "name": "all", "filter": [ { "function": "number_filter", "type": "float", "range_min": 1.0, "range_max": 5.0, "on_outside_range": "clip", "fallback": 5.0 }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.1 } }, "bluex": { "task": "bluex", "group": [ "pt_benchmark", "vestibular" ], "dataset_path": "eduagarcia-temp/BLUEX_without_images", "test_split": "train", "fewshot_split": "train", "doc_to_text": "", "doc_to_target": "{{answerKey}}", "description": "As perguntas a seguir são questões de múltipla escolha de provas de vestibular de universidades brasileiras, selecione a única alternativa correta e responda apenas com as letras \"A\", \"B\", \"C\", \"D\" ou \"E\".\n\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "id_sampler", "sampler_config": { "id_list": [ "USP_2018_3", "UNICAMP_2018_2", "USP_2018_35", "UNICAMP_2018_16", "USP_2018_89" ], "id_column": "id", "exclude_from_task": true } }, "num_fewshot": 3, "metric_list": [ { "metric": "acc", "aggregation": "acc", "higher_is_better": true } ], "output_type": "generate_until", "generation_kwargs": { "max_gen_toks": 32, "do_sample": false, "temperature": 0.0, "top_k": null, "top_p": null, "until": [ "\n\n" ] }, "repeats": 1, "filter_list": [ { "name": "all", "filter": [ { "function": "normalize_spaces" }, { "function": "remove_accents" }, { "function": "find_choices", "choices": [ "A", "B", "C", "D", "E" ], "regex_patterns": [ "(?:[Ll]etra|[Aa]lternativa|[Rr]esposta|[Rr]esposta [Cc]orreta|[Rr]esposta [Cc]orreta e|[Oo]pcao):? ([ABCDE])\\b", "\\b([ABCDE])\\.", "\\b([ABCDE]) ?[.):-]", "\\b([ABCDE])$", "\\b([ABCDE])\\b" ] }, { "function": "take_first" } ], "group_by": { "column": "exam_id" } } ], "should_decontaminate": true, "doc_to_decontamination_query": "", "metadata": { "version": 1.1 } }, "enem_challenge": { "task": "enem_challenge", "task_alias": "enem", "group": [ "pt_benchmark", "vestibular" ], "dataset_path": "eduagarcia/enem_challenge", "test_split": "train", "fewshot_split": "train", "doc_to_text": "", "doc_to_target": "{{answerKey}}", "description": "As perguntas a seguir são questões de múltipla escolha do Exame Nacional do Ensino Médio (ENEM), selecione a única alternativa correta e responda apenas com as letras \"A\", \"B\", \"C\", \"D\" ou \"E\".\n\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "id_sampler", "sampler_config": { "id_list": [ "2022_21", "2022_88", "2022_143" ], "id_column": "id", "exclude_from_task": true } }, "num_fewshot": 3, "metric_list": [ { "metric": "acc", "aggregation": "acc", "higher_is_better": true } ], "output_type": "generate_until", "generation_kwargs": { "max_gen_toks": 32, "do_sample": false, "temperature": 0.0, "top_k": null, "top_p": null, "until": [ "\n\n" ] }, "repeats": 1, "filter_list": [ { "name": "all", "filter": [ { "function": "normalize_spaces" }, { "function": "remove_accents" }, { "function": "find_choices", "choices": [ "A", "B", "C", "D", "E" ], "regex_patterns": [ "(?:[Ll]etra|[Aa]lternativa|[Rr]esposta|[Rr]esposta [Cc]orreta|[Rr]esposta [Cc]orreta e|[Oo]pcao):? ([ABCDE])\\b", "\\b([ABCDE])\\.", "\\b([ABCDE]) ?[.):-]", "\\b([ABCDE])$", "\\b([ABCDE])\\b" ] }, { "function": "take_first" } ], "group_by": { "column": "exam_id" } } ], "should_decontaminate": true, "doc_to_decontamination_query": "", "metadata": { "version": 1.1 } }, "faquad_nli": { "task": "faquad_nli", "group": [ "pt_benchmark" ], "dataset_path": "ruanchaves/faquad-nli", "test_split": "test", "fewshot_split": "train", "doc_to_text": "Pergunta: {{question}}\nResposta: {{answer}}\nA resposta dada satisfaz à pergunta? Sim ou Não?", "doc_to_target": "{{['Não', 'Sim'][label]}}", "description": "Abaixo estão pares de pergunta e resposta. Para cada par, você deve julgar se a resposta responde à pergunta de maneira satisfatória e aparenta estar correta. Escreva apenas \"Sim\" ou \"Não\".\n\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "sampler_config": { "fewshot_indices": [ 1893, 949, 663, 105, 1169, 2910, 2227, 2813, 974, 558, 1503, 1958, 2918, 601, 1560, 984, 2388, 995, 2233, 1982, 165, 2788, 1312, 2285, 522, 1113, 1670, 323, 236, 1263, 1562, 2519, 1049, 432, 1167, 1394, 2022, 2551, 2194, 2187, 2282, 2816, 108, 301, 1185, 1315, 1420, 2436, 2322, 766 ] } }, "num_fewshot": 15, "metric_list": [ { "metric": "f1_macro", "aggregation": "f1_macro", "higher_is_better": true }, { "metric": "acc", "aggregation": "acc", "higher_is_better": true } ], "output_type": "generate_until", "generation_kwargs": { "max_gen_toks": 32, "do_sample": false, "temperature": 0.0, "top_k": null, "top_p": null, "until": [ "\n\n" ] }, "repeats": 1, "filter_list": [ { "name": "all", "filter": [ { "function": "find_similar_label", "labels": [ "Sim", "Não" ] }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.1 } }, "hatebr_offensive": { "task": "hatebr_offensive", "task_alias": "hatebr_offensive_binary", "group": [ "pt_benchmark" ], "dataset_path": "eduagarcia/portuguese_benchmark", "dataset_name": "HateBR_offensive_binary", "test_split": "test", "fewshot_split": "train", "doc_to_text": "Texto: {{sentence}}\nPergunta: O texto é ofensivo?\nResposta:", "doc_to_target": "{{'Sim' if label == 1 else 'Não'}}", "description": "Abaixo contém o texto de comentários de usuários do Instagram em português, sua tarefa é classificar se o texto é ofensivo ou não. Responda apenas com \"Sim\" ou \"Não\".\n\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "id_sampler", "sampler_config": { "id_list": [ 48, 44, 36, 20, 3511, 88, 3555, 16, 56, 3535, 60, 40, 3527, 4, 76, 3579, 3523, 3551, 68, 3503, 84, 3539, 64, 3599, 80, 3563, 3559, 3543, 3547, 3587, 3595, 3575, 3567, 3591, 24, 96, 92, 3507, 52, 72, 8, 3571, 3515, 3519, 3531, 28, 32, 0, 12, 3583 ], "id_column": "idx" } }, "num_fewshot": 25, "metric_list": [ { "metric": "f1_macro", "aggregation": "f1_macro", "higher_is_better": true }, { "metric": "acc", "aggregation": "acc", "higher_is_better": true } ], "output_type": "generate_until", "generation_kwargs": { "max_gen_toks": 32, "do_sample": false, "temperature": 0.0, "top_k": null, "top_p": null, "until": [ "\n\n" ] }, "repeats": 1, "filter_list": [ { "name": "all", "filter": [ { "function": "find_similar_label", "labels": [ "Sim", "Não" ] }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "oab_exams": { "task": "oab_exams", "group": [ "legal_benchmark", "pt_benchmark" ], "dataset_path": "eduagarcia/oab_exams", "test_split": "train", "fewshot_split": "train", "doc_to_text": "", "doc_to_target": "{{answerKey}}", "description": "As perguntas a seguir são questões de múltipla escolha do Exame de Ordem da Ordem dos Advogados do Brasil (OAB), selecione a única alternativa correta e responda apenas com as letras \"A\", \"B\", \"C\" ou \"D\".\n\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "id_sampler", "sampler_config": { "id_list": [ "2010-01_1", "2010-01_11", "2010-01_13", "2010-01_23", "2010-01_26", "2010-01_28", "2010-01_38", "2010-01_48", "2010-01_58", "2010-01_68", "2010-01_76", "2010-01_83", "2010-01_85", "2010-01_91", "2010-01_99" ], "id_column": "id", "exclude_from_task": true } }, "num_fewshot": 3, "metric_list": [ { "metric": "acc", "aggregation": "acc", "higher_is_better": true } ], "output_type": "generate_until", "generation_kwargs": { "max_gen_toks": 32, "do_sample": false, "temperature": 0.0, "top_k": null, "top_p": null, "until": [ "\n\n" ] }, "repeats": 1, "filter_list": [ { "name": "all", "filter": [ { "function": "normalize_spaces" }, { "function": "remove_accents" }, { "function": "find_choices", "choices": [ "A", "B", "C", "D" ], "regex_patterns": [ "(?:[Ll]etra|[Aa]lternativa|[Rr]esposta|[Rr]esposta [Cc]orreta|[Rr]esposta [Cc]orreta e|[Oo]pcao):? ([ABCD])\\b", "\\b([ABCD])\\.", "\\b([ABCD]) ?[.):-]", "\\b([ABCD])$", "\\b([ABCD])\\b" ] }, { "function": "take_first" } ], "group_by": { "column": "exam_id" } } ], "should_decontaminate": true, "doc_to_decontamination_query": "", "metadata": { "version": 1.5 } }, "portuguese_hate_speech": { "task": "portuguese_hate_speech", "task_alias": "portuguese_hate_speech_binary", "group": [ "pt_benchmark" ], "dataset_path": "eduagarcia/portuguese_benchmark", "dataset_name": "Portuguese_Hate_Speech_binary", "test_split": "test", "fewshot_split": "train", "doc_to_text": "Texto: {{sentence}}\nPergunta: O texto contém discurso de ódio?\nResposta:", "doc_to_target": "{{'Sim' if label == 1 else 'Não'}}", "description": "Abaixo contém o texto de tweets de usuários do Twitter em português, sua tarefa é classificar se o texto contém discurso de ódio ou não. Responda apenas com \"Sim\" ou \"Não\".\n\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "id_sampler", "sampler_config": { "id_list": [ 52, 50, 39, 28, 3, 105, 22, 25, 60, 11, 66, 41, 9, 4, 91, 42, 7, 20, 76, 1, 104, 13, 67, 54, 97, 27, 24, 14, 16, 48, 53, 40, 34, 49, 32, 119, 114, 2, 58, 83, 18, 36, 5, 6, 10, 35, 38, 0, 21, 46 ], "id_column": "idx" } }, "num_fewshot": 25, "metric_list": [ { "metric": "f1_macro", "aggregation": "f1_macro", "higher_is_better": true }, { "metric": "acc", "aggregation": "acc", "higher_is_better": true } ], "output_type": "generate_until", "generation_kwargs": { "max_gen_toks": 32, "do_sample": false, "temperature": 0.0, "top_k": null, "top_p": null, "until": [ "\n\n" ] }, "repeats": 1, "filter_list": [ { "name": "all", "filter": [ { "function": "find_similar_label", "labels": [ "Sim", "Não" ] }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "tweetsentbr": { "task": "tweetsentbr", "group": [ "pt_benchmark" ], "dataset_path": "eduagarcia-temp/tweetsentbr", "test_split": "test", "fewshot_split": "train", "doc_to_text": "Texto: {{sentence}}\nPergunta: O sentimento do texto é Positivo, Neutro ou Negativo?\nResposta:", "doc_to_target": "{{'Positivo' if label == 'Positive' else ('Negativo' if label == 'Negative' else 'Neutro')}}", "description": "Abaixo contém o texto de tweets de usuários do Twitter em português, sua tarefa é classificar se o sentimento do texto é Positivo, Neutro ou Negativo. Responda apenas com uma das opções.\n\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "id_sampler", "sampler_config": { "id_list": [ "862006098672459776", "861612241703063552", "861833257087848448", "861283345476571138", "861283000335695873", "862139461274152962", "862139468702265344", "862006107702734848", "862004354458537984", "861833322925883392", "861603063190171648", "862139462716989440", "862005877355810818", "861751885862244353", "862045180261695489", "862004252499226630", "862023970828292097", "862041752127107074", "862034961863503872", "861293756548608001", "861993527575695360", "862003099355021315", "862002404086206467", "861282989602463744", "862139454399668229", "862139463769743361", "862054906689138688", "862139446535360513", "861997363744911361", "862057988898648065", "861329080083521536", "861286289034838016", "861833050526806017", "861300658565255169", "861989003821813760", "861682750398631938", "861283275716907008", 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