EleutherAI/pythia-12b/raw_2024-05-10T12-08-48.948197/results.json

{
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      "f1_macro,all": 0.4179270639535547,
      "acc,all": 0.5322712418300654,
      "alias": "assin2_rte"
    },
    "assin2_sts": {
      "pearson,all": 0.05030336220324859,
      "mse,all": 2.3283782679738567,
      "alias": "assin2_sts"
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      "acc,all": 0.2267037552155772,
      "acc,exam_id__UNICAMP_2023": 0.20930232558139536,
      "acc,exam_id__UNICAMP_2024": 0.24444444444444444,
      "acc,exam_id__UNICAMP_2019": 0.2,
      "acc,exam_id__USP_2019": 0.3,
      "acc,exam_id__UNICAMP_2020": 0.2545454545454545,
      "acc,exam_id__USP_2022": 0.10204081632653061,
      "acc,exam_id__UNICAMP_2021_1": 0.30434782608695654,
      "acc,exam_id__UNICAMP_2021_2": 0.19607843137254902,
      "acc,exam_id__USP_2024": 0.2682926829268293,
      "acc,exam_id__UNICAMP_2022": 0.28205128205128205,
      "acc,exam_id__USP_2020": 0.21428571428571427,
      "acc,exam_id__USP_2018": 0.3333333333333333,
      "acc,exam_id__USP_2021": 0.09615384615384616,
      "acc,exam_id__USP_2023": 0.18181818181818182,
      "acc,exam_id__UNICAMP_2018": 0.24074074074074073,
      "alias": "bluex"
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    "enem_challenge": {
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      "acc,exam_id__2023": 0.24444444444444444,
      "acc,exam_id__2009": 0.2608695652173913,
      "acc,exam_id__2014": 0.1743119266055046,
      "acc,exam_id__2022": 0.21052631578947367,
      "acc,exam_id__2015": 0.14285714285714285,
      "acc,exam_id__2017": 0.23275862068965517,
      "acc,exam_id__2016": 0.2066115702479339,
      "acc,exam_id__2011": 0.21367521367521367,
      "acc,exam_id__2013": 0.1388888888888889,
      "acc,exam_id__2010": 0.23931623931623933,
      "acc,exam_id__2016_2": 0.22764227642276422,
      "acc,exam_id__2012": 0.20689655172413793
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    "faquad_nli": {
      "f1_macro,all": 0.4396551724137931,
      "acc,all": 0.7846153846153846,
      "alias": "faquad_nli"
    },
    "hatebr_offensive": {
      "alias": "hatebr_offensive_binary",
      "f1_macro,all": 0.4676874308442238,
      "acc,all": 0.555
    },
    "oab_exams": {
      "acc,all": 0.2501138952164009,
      "acc,exam_id__2010-01": 0.3176470588235294,
      "acc,exam_id__2017-24": 0.2125,
      "acc,exam_id__2012-09": 0.11688311688311688,
      "acc,exam_id__2012-06a": 0.2125,
      "acc,exam_id__2012-06": 0.2125,
      "acc,exam_id__2010-02": 0.28,
      "acc,exam_id__2016-19": 0.24358974358974358,
      "acc,exam_id__2014-14": 0.25,
      "acc,exam_id__2014-13": 0.275,
      "acc,exam_id__2015-17": 0.2564102564102564,
      "acc,exam_id__2014-15": 0.24358974358974358,
      "acc,exam_id__2012-07": 0.3125,
      "acc,exam_id__2012-08": 0.25,
      "acc,exam_id__2015-18": 0.3125,
      "acc,exam_id__2015-16": 0.225,
      "acc,exam_id__2011-05": 0.3125,
      "acc,exam_id__2011-03": 0.24242424242424243,
      "acc,exam_id__2016-20": 0.25,
      "acc,exam_id__2013-10": 0.175,
      "acc,exam_id__2016-20a": 0.3375,
      "acc,exam_id__2011-04": 0.2625,
      "acc,exam_id__2017-23": 0.275,
      "acc,exam_id__2013-11": 0.2125,
      "acc,exam_id__2013-12": 0.1625,
      "acc,exam_id__2018-25": 0.2625,
      "acc,exam_id__2017-22": 0.225,
      "acc,exam_id__2016-21": 0.3,
      "alias": "oab_exams"
    },
    "portuguese_hate_speech": {
      "alias": "portuguese_hate_speech_binary",
      "f1_macro,all": 0.558212623295537,
      "acc,all": 0.6509988249118684
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    "tweetsentbr": {
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      "acc,all": 0.581592039800995,
      "alias": "tweetsentbr"
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      "group": [
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        "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": {
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          "id_column": "sentence_pair_id"
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      "num_fewshot": 15,
      "metric_list": [
        {
          "metric": "f1_macro",
          "aggregation": "f1_macro",
          "higher_is_better": true
        },
        {
          "metric": "acc",
          "aggregation": "acc",
          "higher_is_better": true
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      ],
      "output_type": "generate_until",
      "generation_kwargs": {
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        "do_sample": false,
        "temperature": 0.0,
        "top_k": null,
        "top_p": null,
        "until": [
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      "filter_list": [
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          "filter": [
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              "function": "find_similar_label",
              "labels": [
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                "Não"
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            },
            {
              "function": "take_first"
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          ]
        }
      ],
      "should_decontaminate": false,
      "metadata": {
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    },
    "assin2_sts": {
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      "group": [
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        "assin2"
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      "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 0x7f8fe8f444a0>",
      "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": {
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        "sampler_config": {
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          "id_column": "sentence_pair_id"
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      },
      "num_fewshot": 15,
      "metric_list": [
        {
          "metric": "pearson",
          "aggregation": "pearsonr",
          "higher_is_better": true
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        {
          "metric": "mse",
          "aggregation": "mean_squared_error",
          "higher_is_better": false
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      ],
      "output_type": "generate_until",
      "generation_kwargs": {
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        "do_sample": false,
        "temperature": 0.0,
        "top_k": null,
        "top_p": null,
        "until": [
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      "filter_list": [
        {
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          "filter": [
            {
              "function": "number_filter",
              "type": "float",
              "range_min": 1.0,
              "range_max": 5.0,
              "on_outside_range": "clip",
              "fallback": 5.0
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            {
              "function": "take_first"
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          ]
        }
      ],
      "should_decontaminate": false,
      "metadata": {
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    },
    "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 0x7f8fe8f0bb00>",
      "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": [
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            "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"
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      },
      "repeats": 1,
      "filter_list": [
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          "filter": [
            {
              "function": "normalize_spaces"
            },
            {
              "function": "remove_accents"
            },
            {
              "function": "find_choices",
              "choices": [
                "A",
                "B",
                "C",
                "D",
                "E"
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              "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"
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            {
              "function": "take_first"
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      ],
      "should_decontaminate": true,
      "doc_to_decontamination_query": "<function enem_doc_to_text at 0x7f8fe8f0bd80>",
      "metadata": {
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    },
    "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 0x7f8fe8f0b560>",
      "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": [
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        ]
      },
      "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 0x7f8fe8f0b880>",
      "metadata": {
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    },
    "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": [
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        }
      },
      "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": [
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          "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"
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      },
      "repeats": 1,
      "filter_list": [
        {
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          "filter": [
            {
              "function": "find_similar_label",
              "labels": [
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                "Não"
              ]
            },
            {
              "function": "take_first"
            }
          ]
        }
      ],
      "should_decontaminate": false,
      "metadata": {
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    },
    "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 0x7f8fe8f0b1a0>",
      "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",
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