{
  "results": {
    "assin2_rte": {
      "f1_macro,all": 0.9031337575789451,
      "acc,all": 0.9031862745098039,
      "alias": "assin2_rte"
    },
    "assin2_sts": {
      "pearson,all": 0.7654589376771873,
      "mse,all": 0.6662418300653595,
      "alias": "assin2_sts"
    },
    "bluex": {
      "acc,all": 0.48678720445062584,
      "acc,exam_id__UNICAMP_2022": 0.6153846153846154,
      "acc,exam_id__UNICAMP_2024": 0.5555555555555556,
      "acc,exam_id__USP_2024": 0.7073170731707317,
      "acc,exam_id__UNICAMP_2021_2": 0.47058823529411764,
      "acc,exam_id__USP_2021": 0.40384615384615385,
      "acc,exam_id__UNICAMP_2020": 0.5272727272727272,
      "acc,exam_id__USP_2019": 0.375,
      "acc,exam_id__USP_2020": 0.48214285714285715,
      "acc,exam_id__UNICAMP_2019": 0.48,
      "acc,exam_id__UNICAMP_2021_1": 0.45652173913043476,
      "acc,exam_id__UNICAMP_2023": 0.5813953488372093,
      "acc,exam_id__USP_2018": 0.4074074074074074,
      "acc,exam_id__USP_2022": 0.42857142857142855,
      "acc,exam_id__USP_2023": 0.4772727272727273,
      "acc,exam_id__UNICAMP_2018": 0.4074074074074074,
      "alias": "bluex"
    },
    "enem_challenge": {
      "alias": "enem",
      "acc,all": 0.5808257522743177,
      "acc,exam_id__2011": 0.6153846153846154,
      "acc,exam_id__2009": 0.6,
      "acc,exam_id__2012": 0.5948275862068966,
      "acc,exam_id__2016": 0.5702479338842975,
      "acc,exam_id__2017": 0.5431034482758621,
      "acc,exam_id__2022": 0.5714285714285714,
      "acc,exam_id__2023": 0.5851851851851851,
      "acc,exam_id__2013": 0.5648148148148148,
      "acc,exam_id__2015": 0.5630252100840336,
      "acc,exam_id__2010": 0.5641025641025641,
      "acc,exam_id__2016_2": 0.5934959349593496,
      "acc,exam_id__2014": 0.6055045871559633
    },
    "faquad_nli": {
      "f1_macro,all": 0.5884431791136597,
      "acc,all": 0.7830769230769231,
      "alias": "faquad_nli"
    },
    "hatebr_offensive": {
      "alias": "hatebr_offensive_binary",
      "f1_macro,all": 0.7921019662468967,
      "acc,all": 0.7971428571428572
    },
    "oab_exams": {
      "acc,all": 0.37084282460136675,
      "acc,exam_id__2017-23": 0.3625,
      "acc,exam_id__2010-02": 0.37,
      "acc,exam_id__2012-06": 0.4125,
      "acc,exam_id__2016-20a": 0.275,
      "acc,exam_id__2018-25": 0.3,
      "acc,exam_id__2011-05": 0.3375,
      "acc,exam_id__2013-10": 0.375,
      "acc,exam_id__2015-16": 0.3125,
      "acc,exam_id__2013-12": 0.3625,
      "acc,exam_id__2016-21": 0.4,
      "acc,exam_id__2011-04": 0.375,
      "acc,exam_id__2010-01": 0.3176470588235294,
      "acc,exam_id__2012-07": 0.3375,
      "acc,exam_id__2012-08": 0.3875,
      "acc,exam_id__2014-13": 0.3,
      "acc,exam_id__2013-11": 0.375,
      "acc,exam_id__2015-18": 0.325,
      "acc,exam_id__2016-19": 0.48717948717948717,
      "acc,exam_id__2014-14": 0.425,
      "acc,exam_id__2012-09": 0.38961038961038963,
      "acc,exam_id__2012-06a": 0.4125,
      "acc,exam_id__2017-22": 0.475,
      "acc,exam_id__2014-15": 0.3717948717948718,
      "acc,exam_id__2015-17": 0.5512820512820513,
      "acc,exam_id__2017-24": 0.3125,
      "acc,exam_id__2016-20": 0.325,
      "acc,exam_id__2011-03": 0.35353535353535354,
      "alias": "oab_exams"
    },
    "portuguese_hate_speech": {
      "alias": "portuguese_hate_speech_binary",
      "f1_macro,all": 0.6886709589535317,
      "acc,all": 0.745005875440658
    },
    "tweetsentbr": {
      "f1_macro,all": 0.6471160335032415,
      "acc,all": 0.6805970149253732,
      "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": "<function assin2_float_to_pt_str at 0x7f116cc853a0>",
      "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": "<function enem_doc_to_text at 0x7f116cc84d60>",
      "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": "<function enem_doc_to_text at 0x7f116cc84fe0>",
      "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": "<function enem_doc_to_text at 0x7f116cc85580>",
      "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": "<function enem_doc_to_text at 0x7f116cc85800>",
      "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": "<function doc_to_text at 0x7f116cc84720>",
      "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": "<function doc_to_text at 0x7f116cc849a0>",
      "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",
            "861283402523267072",
            "861873108147466240",
            "862139462138171392",
            "861284090271715333",
            "862139446149427201",
            "861629109331525633",
            "861721698609098753",
            "862139453124612096",
            "861283339482914816",
            "861282466291748867",
            "862055346759749632",
            "862003019860389891",
            "862140698346344449",
            "862084376280092672",
            "862003058708017152",
            "862000677345787904",
            "862029129310502913",
            "862005822376882178",
            "861969836297134085",
            "861302955361927168",
            "862064949451005953",
            "861282589541355520",
            "862005476858486784",
            "862004684411850757",
            "862139471101349890",
            "862139467146170368",
            "862139475098558465",
            "862140706550403072",
            "861282777001537536",
            "862003184147079169",
            "861283410656059394",
            "861283417857691649",
            "861888778922856448",
            "861655860812099585",
            "861834248063504384",
            "862005210935382017",
            "861282716930760704",
            "861287082433622022"
          ],
          "id_column": "id"
        }
      },
      "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": [
                "Positivo",
                "Neutro",
                "Negativo"
              ]
            },
            {
              "function": "take_first"
            }
          ]
        }
      ],
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    }
  },
  "versions": {
    "assin2_rte": 1.1,
    "assin2_sts": 1.1,
    "bluex": 1.1,
    "enem_challenge": 1.1,
    "faquad_nli": 1.1,
    "hatebr_offensive": 1.0,
    "oab_exams": 1.5,
    "portuguese_hate_speech": 1.0,
    "tweetsentbr": 1.0
  },
  "n-shot": {
    "assin2_rte": 15,
    "assin2_sts": 15,
    "bluex": 3,
    "enem_challenge": 3,
    "faquad_nli": 15,
    "hatebr_offensive": 25,
    "oab_exams": 3,
    "portuguese_hate_speech": 25,
    "tweetsentbr": 25
  },
  "model_meta": {
    "truncated": 3,
    "non_truncated": 14147,
    "padded": 0,
    "non_padded": 14150,
    "fewshots_truncated": 3,
    "has_chat_template": true,
    "chat_type": "user_assistant",
    "n_gpus": 1,
    "accelerate_num_process": null,
    "model_sha": "54078abd0ad81e60533aa0af17889b9255265d27",
    "model_dtype": "torch.float16",
    "model_memory_footprint": 15020343296,
    "model_num_parameters": 7241732096,
    "model_is_loaded_in_4bit": null,
    "model_is_loaded_in_8bit": null,
    "model_is_quantized": null,
    "model_device": "cuda:1",
    "batch_size": 32,
    "max_length": 2560,
    "max_ctx_length": 2528,
    "max_gen_toks": 32
  },
  "task_model_meta": {
    "assin2_rte": {
      "sample_size": 2448,
      "truncated": 0,
      "non_truncated": 2448,
      "padded": 0,
      "non_padded": 2448,
      "fewshots_truncated": 0,
      "mean_seq_length": 1451.7455065359477,
      "min_seq_length": 1428,
      "max_seq_length": 1518,
      "max_ctx_length": 2528,
      "max_gen_toks": 32,
      "mean_original_fewshots_size": 15.0,
      "mean_effective_fewshot_size": 15.0
    },
    "assin2_sts": {
      "sample_size": 2448,
      "truncated": 0,
      "non_truncated": 2448,
      "padded": 0,
      "non_padded": 2448,
      "fewshots_truncated": 0,
      "mean_seq_length": 1675.7455065359477,
      "min_seq_length": 1652,
      "max_seq_length": 1742,
      "max_ctx_length": 2528,
      "max_gen_toks": 32,
      "mean_original_fewshots_size": 15.0,
      "mean_effective_fewshot_size": 15.0
    },
    "bluex": {
      "sample_size": 719,
      "truncated": 1,
      "non_truncated": 718,
      "padded": 0,
      "non_padded": 719,
      "fewshots_truncated": 1,
      "mean_seq_length": 1744.9262865090404,
      "min_seq_length": 1368,
      "max_seq_length": 2545,
      "max_ctx_length": 2528,
      "max_gen_toks": 32,
      "mean_original_fewshots_size": 3.0,
      "mean_effective_fewshot_size": 2.998609179415855
    },
    "enem_challenge": {
      "sample_size": 1429,
      "truncated": 2,
      "non_truncated": 1427,
      "padded": 0,
      "non_padded": 1429,
      "fewshots_truncated": 2,
      "mean_seq_length": 1645.039188243527,
      "min_seq_length": 1379,
      "max_seq_length": 2643,
      "max_ctx_length": 2528,
      "max_gen_toks": 32,
      "mean_original_fewshots_size": 3.0,
      "mean_effective_fewshot_size": 2.998600419874038
    },
    "faquad_nli": {
      "sample_size": 650,
      "truncated": 0,
      "non_truncated": 650,
      "padded": 0,
      "non_padded": 650,
      "fewshots_truncated": 0,
      "mean_seq_length": 1691.9876923076922,
      "min_seq_length": 1636,
      "max_seq_length": 1812,
      "max_ctx_length": 2528,
      "max_gen_toks": 32,
      "mean_original_fewshots_size": 15.0,
      "mean_effective_fewshot_size": 15.0
    },
    "hatebr_offensive": {
      "sample_size": 1400,
      "truncated": 0,
      "non_truncated": 1400,
      "padded": 0,
      "non_padded": 1400,
      "fewshots_truncated": 0,
      "mean_seq_length": 1462.3878571428572,
      "min_seq_length": 1439,
      "max_seq_length": 1713,
      "max_ctx_length": 2528,
      "max_gen_toks": 32,
      "mean_original_fewshots_size": 25.0,
      "mean_effective_fewshot_size": 25.0
    },
    "oab_exams": {
      "sample_size": 2195,
      "truncated": 0,
      "non_truncated": 2195,
      "padded": 0,
      "non_padded": 2195,
      "fewshots_truncated": 0,
      "mean_seq_length": 1390.764464692483,
      "min_seq_length": 1124,
      "max_seq_length": 1893,
      "max_ctx_length": 2528,
      "max_gen_toks": 32,
      "mean_original_fewshots_size": 3.0,
      "mean_effective_fewshot_size": 3.0
    },
    "portuguese_hate_speech": {
      "sample_size": 851,
      "truncated": 0,
      "non_truncated": 851,
      "padded": 0,
      "non_padded": 851,
      "fewshots_truncated": 0,
      "mean_seq_length": 1963.3360752056403,
      "min_seq_length": 1928,
      "max_seq_length": 2002,
      "max_ctx_length": 2528,
      "max_gen_toks": 32,
      "mean_original_fewshots_size": 25.0,
      "mean_effective_fewshot_size": 25.0
    },
    "tweetsentbr": {
      "sample_size": 2010,
      "truncated": 0,
      "non_truncated": 2010,
      "padded": 0,
      "non_padded": 2010,
      "fewshots_truncated": 0,
      "mean_seq_length": 1709.2492537313433,
      "min_seq_length": 1688,
      "max_seq_length": 1804,
      "max_ctx_length": 2528,
      "max_gen_toks": 32,
      "mean_original_fewshots_size": 25.0,
      "mean_effective_fewshot_size": 25.0
    }
  },
  "config": {
    "model": "huggingface",
    "model_args": "pretrained=cnmoro/Mistral-7B-Portuguese,dtype=float16,device=cuda:1,revision=main,trust_remote_code=True,starting_max_length=2560",
    "batch_size": "auto",
    "batch_sizes": [],
    "device": null,
    "use_cache": null,
    "limit": [
      null,
      null,
      null,
      null,
      null,
      null,
      null,
      null,
      null
    ],
    "bootstrap_iters": 0,
    "gen_kwargs": null
  },
  "git_hash": null
}