Oburaco/juridic-falcon-7b/raw_2024-05-27T21-44-42.226357/results.json

{
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      "alias": "assin2_rte"
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      "mse,all": 2.487565359477124,
      "alias": "assin2_sts"
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      "acc,exam_id__USP_2020": 0.16071428571428573,
      "acc,exam_id__UNICAMP_2020": 0.2727272727272727,
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      "acc,exam_id__UNICAMP_2019": 0.18,
      "acc,exam_id__UNICAMP_2021_2": 0.23529411764705882,
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      "acc,exam_id__USP_2021": 0.25,
      "acc,exam_id__USP_2019": 0.275,
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      "acc,exam_id__2017": 0.1896551724137931,
      "acc,exam_id__2015": 0.2184873949579832,
      "acc,exam_id__2016": 0.2066115702479339,
      "acc,exam_id__2016_2": 0.17886178861788618,
      "acc,exam_id__2009": 0.17391304347826086,
      "acc,exam_id__2012": 0.1896551724137931,
      "acc,exam_id__2010": 0.21367521367521367,
      "acc,exam_id__2013": 0.25925925925925924,
      "acc,exam_id__2014": 0.2018348623853211,
      "acc,exam_id__2022": 0.20300751879699247,
      "acc,exam_id__2023": 0.2074074074074074
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      "acc,all": 0.7846153846153846,
      "alias": "faquad_nli"
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    "hatebr_offensive": {
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      "f1_macro,all": 0.7484848438318135,
      "acc,all": 0.7492857142857143
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      "acc,exam_id__2011-03": 0.25252525252525254,
      "acc,exam_id__2014-13": 0.2375,
      "acc,exam_id__2013-10": 0.3,
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      "acc,exam_id__2017-22": 0.25,
      "acc,exam_id__2012-06a": 0.2,
      "acc,exam_id__2016-20a": 0.225,
      "acc,exam_id__2012-09": 0.16883116883116883,
      "acc,exam_id__2015-16": 0.2,
      "acc,exam_id__2011-04": 0.325,
      "acc,exam_id__2012-07": 0.25,
      "acc,exam_id__2014-14": 0.2375,
      "acc,exam_id__2014-15": 0.23076923076923078,
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      "acc,exam_id__2015-18": 0.2375,
      "acc,exam_id__2016-19": 0.23076923076923078,
      "acc,exam_id__2012-06": 0.2375,
      "acc,exam_id__2013-12": 0.175,
      "acc,exam_id__2011-05": 0.1875,
      "acc,exam_id__2017-23": 0.2375,
      "acc,exam_id__2013-11": 0.2375,
      "acc,exam_id__2016-20": 0.1875,
      "acc,exam_id__2016-21": 0.2375,
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      "acc,exam_id__2010-01": 0.3058823529411765,
      "acc,exam_id__2012-08": 0.2125,
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    "portuguese_hate_speech": {
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      "f1_macro,all": 0.5075395898018006,
      "acc,all": 0.5111633372502937
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      "acc,all": 0.5696517412935324,
      "alias": "tweetsentbr"
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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": "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": {
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        "sampler_config": {
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          "id_column": "sentence_pair_id"
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      "num_fewshot": 15,
      "metric_list": [
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          "aggregation": "f1_macro",
          "higher_is_better": true
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        {
          "metric": "acc",
          "aggregation": "acc",
          "higher_is_better": true
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      "output_type": "generate_until",
      "generation_kwargs": {
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        "temperature": 0.0,
        "top_k": null,
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        "until": [
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      "filter_list": [
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              "labels": [
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            {
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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 0x7f80d069a200>",
      "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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      "metric_list": [
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          "aggregation": "pearsonr",
          "higher_is_better": true
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        {
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          "aggregation": "mean_squared_error",
          "higher_is_better": false
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      "generation_kwargs": {
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        "until": [
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      "filter_list": [
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          "filter": [
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              "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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          ]
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      ],
      "should_decontaminate": false,
      "metadata": {
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    },
    "bluex": {
      "task": "bluex",
      "group": [
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        "vestibular"
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      "dataset_path": "eduagarcia-temp/BLUEX_without_images",
      "test_split": "train",
      "fewshot_split": "train",
      "doc_to_text": "<function enem_doc_to_text at 0x7f80d0699bc0>",
      "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"
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          "id_column": "id",
          "exclude_from_task": true
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      },
      "num_fewshot": 3,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "acc",
          "higher_is_better": true
        }
      ],
      "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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      },
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      "filter_list": [
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          "filter": [
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            {
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              "choices": [
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                "C",
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                "\\b([ABCDE])\\.",
                "\\b([ABCDE]) ?[.):-]",
                "\\b([ABCDE])$",
                "\\b([ABCDE])\\b"
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            {
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      "doc_to_decontamination_query": "<function enem_doc_to_text at 0x7f80d0699e40>",
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    },
    "enem_challenge": {
      "task": "enem_challenge",
      "task_alias": "enem",
      "group": [
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        "vestibular"
      ],
      "dataset_path": "eduagarcia/enem_challenge",
      "test_split": "train",
      "fewshot_split": "train",
      "doc_to_text": "<function enem_doc_to_text at 0x7f80d069a3e0>",
      "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": [
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          "metric": "acc",
          "aggregation": "acc",
          "higher_is_better": true
        }
      ],
      "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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            {
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            },
            {
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              "choices": [
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                "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"
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            {
              "function": "take_first"
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      "doc_to_decontamination_query": "<function enem_doc_to_text at 0x7f80d069a660>",
      "metadata": {
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    "faquad_nli": {
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      "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": [
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          "aggregation": "f1_macro",
          "higher_is_better": true
        },
        {
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          "aggregation": "acc",
          "higher_is_better": true
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
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        "do_sample": false,
        "temperature": 0.0,
        "top_k": null,
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        "until": [
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      },
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      "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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    },
    "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": {
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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": {
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        "do_sample": false,
        "temperature": 0.0,
        "top_k": null,
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      "filter_list": [
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            {
              "function": "take_first"
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          ]
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      "should_decontaminate": false,
      "metadata": {
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    "oab_exams": {
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      "group": [
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        "pt_benchmark"
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      "dataset_path": "eduagarcia/oab_exams",
      "test_split": "train",
      "fewshot_split": "train",
      "doc_to_text": "<function doc_to_text at 0x7f80d0699580>",
      "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",
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            "2010-01_85",
            "2010-01_91",
            "2010-01_99"
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          "id_column": "id",
          "exclude_from_task": true
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      },
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      "metric_list": [
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          "aggregation": "acc",
          "higher_is_better": true
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      ],
      "output_type": "generate_until",
      "generation_kwargs": {
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