Uploading raw results for mosaicml/mpt-7b

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+      "task": "assin2_rte",
+      "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?\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": {
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+      "metric_list": [
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+          "metric": "f1_macro",
+          "aggregation": "f1_macro",
+          "higher_is_better": true
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+        {
+          "metric": "acc",
+          "aggregation": "acc",
+          "higher_is_better": true
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+        "top_k": null,
+        "top_p": null,
+        "until": [
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+      "repeats": 1,
+      "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": [
+        "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 0x7fe12fa9d800>",
+      "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": {
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+            3251,
+            2,
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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
+        },
+        {
+          "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
+      }
+    },
+    "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 0x7fe12fa9d1c0>",
+      "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"
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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"
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+            {
+              "function": "take_first"
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+          "group_by": {
+            "column": "exam_id"
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+        }
+      ],
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "<function enem_doc_to_text at 0x7fe12fa9d440>",
+      "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 0x7fe12fa9d9e0>",
+      "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
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+      "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,
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+        {
+          "name": "all",
+          "filter": [
+            {
+              "function": "normalize_spaces"
+            },
+            {
+              "function": "remove_accents"
+            },
+            {
+              "function": "find_choices",
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+                "B",
+                "C",
+                "D",
+                "E"
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+                "(?:[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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+          "group_by": {
+            "column": "exam_id"
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+      ],
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "<function enem_doc_to_text at 0x7fe12fa9dc60>",
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "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",
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+      "num_fewshot": 15,
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+          "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": [
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+      },
+      "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 0x7fe12fa9cb80>",
+      "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",
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+            "2010-01_83",
+            "2010-01_85",
+            "2010-01_91",
+            "2010-01_99"
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+          "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,
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+        "top_p": null,
+        "until": [
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+      "repeats": 1,
+      "filter_list": [
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+          "name": "all",
+          "filter": [
+            {
+              "function": "normalize_spaces"
+            },
+            {
+              "function": "remove_accents"
+            },
+            {
+              "function": "find_choices",
+              "choices": [
+                "A",
+                "B",
+                "C",
+                "D"
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+              "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 0x7fe12fa9ce00>",
+      "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 0x7fe12fa9d080>",
+      "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"
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+      "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": [
+        {
+          "name": "all",
+          "filter": [
+            {
+              "function": "find_similar_label",
+              "labels": [
+                "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",
+                "Surpresa"
+              ]
+            },
+            {
+              "function": "take_first"
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+      ],
+      "should_decontaminate": false,
+      "limit": 500,
+      "metadata": {
+        "version": 1.0
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+      "task": "sparrow_hate-2019-fortuna-por",
+      "task_alias": "hate-2019-fortuna-por",
+      "group": [
+        "pt_benchmark",
+        "sparrow"
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+      "dataset_name": "hate-2019-fortuna-por",
+      "test_split": "validation",
+      "fewshot_split": "train",
+      "doc_to_text": "Texto: {{content}}\nPergunta: O texto contém discurso de ódio?\nResposta:",
+      "doc_to_target": "{{'Sim' if label == 'Hate' else 'Não'}}",
+      "description": "Abaixo contém o conteúdo de tweets de usuarios do Twitter em português, sua tarefa é classificar se o texto contem discurso de ódio our não. Responda apenas com Sim ou Não.\n\n",
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+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
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+      "num_fewshot": 25,
+      "metric_list": [
+        {
+          "metric": "f1_macro",
+          "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",
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+        "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": "find_similar_label",
+              "labels": [
+                "Sim",
+                "Não"
+              ]
+            },
+            {
+              "function": "take_first"
+            }
+          ]
+        }
+      ],
+      "should_decontaminate": false,
+      "limit": 500,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "sparrow_sentiment-2016-mozetic-por": {
+      "task": "sparrow_sentiment-2016-mozetic-por",
+      "task_alias": "sentiment-2016-mozetic-por",
+      "group": [
+        "pt_benchmark",
+        "sparrow"
+      ],
+      "dataset_path": "UBC-NLP/sparrow",
+      "dataset_name": "sentiment-2016-mozetic-por",
+      "test_split": "validation",
+      "fewshot_split": "train",
+      "doc_to_text": "Texto: {{content}}\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 conteúdo de tweets de usuarios 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": "first_n"
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+      "num_fewshot": 25,
+      "metric_list": [
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+          "metric": "f1_macro",
+          "aggregation": "f1_macro",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc",
+          "aggregation": "acc",
+          "higher_is_better": true
+        }
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+        "temperature": 0.0,
+        "top_k": null,
+        "top_p": null,
+        "until": [
+          "\n\n"
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+      "repeats": 1,
+      "filter_list": [
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+          "name": "all",
+          "filter": [
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+              "function": "find_similar_label",
+              "labels": [
+                "Positivo",
+                "Neutro",
+                "Negativo"
+              ]
+            },
+            {
+              "function": "take_first"
+            }
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+      "should_decontaminate": false,
+      "limit": 500,
+      "metadata": {
+        "version": 1.0
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+    "sparrow_sentiment-2018-brum-por": {
+      "task": "sparrow_sentiment-2018-brum-por",
+      "task_alias": "sentiment-2018-brum-por",
+      "group": [
+        "pt_benchmark",
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+      "dataset_name": "sentiment-2018-brum-por",
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+      "doc_to_text": "Texto: {{content}}\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 conteúdo de tweets de usuarios 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",
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+          "aggregation": "f1_macro",
+          "higher_is_better": true
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+          "metric": "acc",
+          "aggregation": "acc",
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+        "temperature": 0.0,
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+              "function": "find_similar_label",
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+              "function": "take_first"
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+      "should_decontaminate": false,
+      "limit": 500,
+      "metadata": {
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