internlm/internlm-20b/raw_2024-02-07T08-37-33.747354/results.json

{
  "results": {
    "assin2_rte": {
      "f1_macro,all": 0.8925812286460775,
      "acc,all": 0.8929738562091504,
      "alias": "assin2_rte"
    },
    "assin2_sts": {
      "pearson,all": 0.6923784013644456,
      "mse,all": 1.0991462418300655,
      "alias": "assin2_sts"
    },
    "bluex": {
      "acc,all": 0.5299026425591099,
      "acc,exam_id__USP_2019": 0.4,
      "acc,exam_id__UNICAMP_2018": 0.48148148148148145,
      "acc,exam_id__UNICAMP_2020": 0.5272727272727272,
      "acc,exam_id__UNICAMP_2023": 0.627906976744186,
      "acc,exam_id__UNICAMP_2024": 0.5333333333333333,
      "acc,exam_id__UNICAMP_2019": 0.48,
      "acc,exam_id__UNICAMP_2021_2": 0.5490196078431373,
      "acc,exam_id__USP_2022": 0.3877551020408163,
      "acc,exam_id__USP_2020": 0.5892857142857143,
      "acc,exam_id__USP_2018": 0.37037037037037035,
      "acc,exam_id__USP_2021": 0.5384615384615384,
      "acc,exam_id__USP_2023": 0.6363636363636364,
      "acc,exam_id__USP_2024": 0.6829268292682927,
      "acc,exam_id__UNICAMP_2021_1": 0.5434782608695652,
      "acc,exam_id__UNICAMP_2022": 0.6666666666666666,
      "alias": "bluex"
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    "enem_challenge": {
      "alias": "enem",
      "acc,all": 0.6004198740377886,
      "acc,exam_id__2016_2": 0.5853658536585366,
      "acc,exam_id__2016": 0.5785123966942148,
      "acc,exam_id__2010": 0.5641025641025641,
      "acc,exam_id__2014": 0.6055045871559633,
      "acc,exam_id__2023": 0.6666666666666666,
      "acc,exam_id__2017": 0.5689655172413793,
      "acc,exam_id__2015": 0.5882352941176471,
      "acc,exam_id__2012": 0.603448275862069,
      "acc,exam_id__2022": 0.556390977443609,
      "acc,exam_id__2013": 0.5555555555555556,
      "acc,exam_id__2009": 0.6,
      "acc,exam_id__2011": 0.7264957264957265
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    "faquad_nli": {
      "f1_macro,all": 0.7342526326652933,
      "acc,all": 0.8415384615384616,
      "alias": "faquad_nli"
    },
    "oab_exams": {
      "acc,all": 0.44236902050113897,
      "acc,exam_id__2014-13": 0.45,
      "acc,exam_id__2012-07": 0.3875,
      "acc,exam_id__2012-09": 0.35064935064935066,
      "acc,exam_id__2011-03": 0.3333333333333333,
      "acc,exam_id__2012-08": 0.4375,
      "acc,exam_id__2015-17": 0.5128205128205128,
      "acc,exam_id__2014-15": 0.5256410256410257,
      "acc,exam_id__2017-24": 0.45,
      "acc,exam_id__2015-16": 0.4125,
      "acc,exam_id__2017-23": 0.45,
      "acc,exam_id__2011-04": 0.375,
      "acc,exam_id__2010-02": 0.51,
      "acc,exam_id__2016-19": 0.5,
      "acc,exam_id__2012-06": 0.425,
      "acc,exam_id__2012-06a": 0.475,
      "acc,exam_id__2013-12": 0.5,
      "acc,exam_id__2017-22": 0.4875,
      "acc,exam_id__2010-01": 0.3764705882352941,
      "acc,exam_id__2013-10": 0.3875,
      "acc,exam_id__2014-14": 0.5,
      "acc,exam_id__2018-25": 0.425,
      "acc,exam_id__2016-20a": 0.3875,
      "acc,exam_id__2015-18": 0.4375,
      "acc,exam_id__2011-05": 0.4375,
      "acc,exam_id__2013-11": 0.5125,
      "acc,exam_id__2016-20": 0.475,
      "acc,exam_id__2016-21": 0.4375,
      "alias": "oab_exams"
    },
    "sparrow_emotion-2021-cortiz-por": {
      "alias": "emotion-2021-cortiz-por",
      "f1_macro,all": 0.06473706233151803,
      "acc,all": 0.126
    },
    "sparrow_hate-2019-fortuna-por": {
      "alias": "hate-2019-fortuna-por",
      "f1_macro,all": 0.5806101137700618,
      "acc,all": 0.702
    },
    "sparrow_sentiment-2016-mozetic-por": {
      "alias": "sentiment-2016-mozetic-por",
      "f1_macro,all": 0.2949408095557224,
      "acc,all": 0.266
    },
    "sparrow_sentiment-2018-brum-por": {
      "alias": "sentiment-2018-brum-por",
      "f1_macro,all": 0.3443292232024626,
      "acc,all": 0.384
    }
  },
  "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?\nResposta:",
      "doc_to_target": "{{['Não', 'Sim'][entailment_judgment]}}",
      "description": "Abaixo contém pares de premissa e hipótese, para cada par você deve julgar 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,
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            25,
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          "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": 1.0
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    },
    "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: Qual o grau de similaridade entre as duas frases de 1,0 a 5,0?\nResposta:",
      "doc_to_target": "<function assin2_float_to_pt_str at 0x7f8bf0f71800>",
      "description": "Abaixo contém pares de frases, para cada par você deve julgar o grau de similaridade de 1,0 a 5,0, responda apenas com o número.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "id_sampler",
        "sampler_config": {
          "id_list": [
            1,
            3251,
            2,
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            3,
            4,
            5,
            6,
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            15,
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            17,
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            18,
            3265,
            3266,
            3267,
            19,
            20,
            3268,
            3269,
            21,
            3270,
            3271,
            22,
            3272,
            3273,
            23,
            3274,
            24,
            25,
            3275
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          "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"
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      },
      "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": 1.0
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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 0x7f8bf0f711c0>",
      "doc_to_target": "{{answerKey}}",
      "description": "As perguntas a seguir são questões de multipla escolha de provas de vestibular de Universidades Brasileiras, reponda 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"
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          ],
          "group_by": {
            "column": "exam_id"
          }
        }
      ],
      "should_decontaminate": true,
      "doc_to_decontamination_query": "<function enem_doc_to_text at 0x7f8bf0f71440>",
      "metadata": {
        "version": 1.0
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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 0x7f8bf0f719e0>",
      "doc_to_target": "{{answerKey}}",
      "description": "As perguntas a seguir são questões de multipla escolha do Exame Nacional do Ensino Médio (ENEM), reponda 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"
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          ],
          "group_by": {
            "column": "exam_id"
          }
        }
      ],
      "should_decontaminate": true,
      "doc_to_decontamination_query": "<function enem_doc_to_text at 0x7f8bf0f71c60>",
      "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 satisfaz a pergunta? Sim ou Não?",
      "doc_to_target": "{{['Não', 'Sim'][label]}}",
      "description": "Abaixo contém pares de pergunta e reposta, para cada par você deve julgar resposta responde a 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": 1.0
      }
    },
    "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 0x7f8bf0f70b80>",
      "doc_to_target": "{{answerKey}}",
      "description": "As perguntas a seguir são questões de multipla escolha do Exame de Ordem da Ordem dos Advogados do Brasil (OAB), reponda 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 0x7f8bf0f70e00>",
      "metadata": {
        "version": 1.4
      }
    },
    "sparrow_emotion-2021-cortiz-por": {
      "task": "sparrow_emotion-2021-cortiz-por",
      "task_alias": "emotion-2021-cortiz-por",
      "group": [
        "pt_benchmark",
        "sparrow"
      ],
      "dataset_path": "UBC-NLP/sparrow",
      "dataset_name": "emotion-2021-cortiz-por",
      "test_split": "validation",
      "fewshot_split": "train",
      "doc_to_text": "Texto: {{content}}\nPergunta: Qual a principal emoção apresentada no texto?\nResposta:",
      "doc_to_target": "<function sparrow_emotion_por_trans_label at 0x7f8bf0f71080>",
      "description": "Abaixo contém o conteúdo de tweets de usuarios do Twitter em português, sua tarefa é extrair qual a principal emoção dos textos. Responda com apenas uma das seguintes opções:\n Admiração, Diversão, Raiva, Aborrecimento, Aprovação, Compaixão, Confusão, Curiosidade, Desejo, Decepção, Desaprovação, Nojo, Vergonha, Inveja, Entusiasmo, Medo, Gratidão, Luto, Alegria, Saudade, Amor, Nervosismo, Otimismo, Orgulho, Alívio, Remorso, Tristeza ou Surpresa.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 25,
      "metric_list": [
        {
          "metric": "f1_macro",
          "aggregation": "f1_macro",
          "higher_is_better": true
        },
        {
          "metric": "acc",
          "aggregation": "acc",
          "higher_is_better": true
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