Uploading raw results for bigscience/bloom-7b1

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+      "doc_to_text": "<function enem_doc_to_text at 0x7fc649311300>",
+      "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",
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+          "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 0x7fc649311580>",
+      "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,
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+            2227,
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+            1185,
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+            1420,
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+            2322,
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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": [
+            48,
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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"
+        ]
+      },
+      "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 0x7fc6493104a0>",
+      "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"
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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"
+              ],
+              "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 0x7fc649310720>",
+      "metadata": {
+        "version": 1.5
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+    },
+    "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": {
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+            34,
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+            32,
+            119,
+            114,
+            2,
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+            5,
+            6,
+            10,
+            35,
+            38,
+            0,
+            21,
+            46
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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"
+        ]
+      },
+      "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": {
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+          "aggregation": "f1_macro",
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+        {
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+          "aggregation": "acc",
+          "higher_is_better": true
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+        "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": [
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+                "Neutro",
+                "Negativo"
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+            },
+            {
+              "function": "take_first"
+            }
+          ]
+        }
+      ],
+      "should_decontaminate": false,
+      "metadata": {
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+  },
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+    "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
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+  "n-shot": {
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+    "assin2_sts": 15,
+    "bluex": 3,
+    "enem_challenge": 3,
+    "faquad_nli": 15,
+    "hatebr_offensive": 25,
+    "oab_exams": 3,
+    "portuguese_hate_speech": 25,
+    "tweetsentbr": 25
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+  "model_meta": {
+    "truncated": 1,
+    "non_truncated": 14149,
+    "padded": 0,
+    "non_padded": 14150,
+    "fewshots_truncated": 1,
+    "has_chat_template": false,
+    "chat_type": null,
+    "n_gpus": 1,
+    "accelerate_num_process": null,
+    "model_sha": "6232703e399354503377bf59dfbb8397fd569e4a",
+    "model_dtype": "torch.float16",
+    "model_memory_footprint": 14138032128,
+    "model_num_parameters": 7069016064,
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+    "model_is_loaded_in_8bit": null,
+    "model_is_quantized": null,
+    "model_device": "cuda:0",
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+    "max_length": 2048,
+    "max_ctx_length": 2016,
+    "max_gen_toks": 32
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+  "task_model_meta": {
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+      "max_ctx_length": 2016,
+      "max_gen_toks": 32,
+      "mean_original_fewshots_size": 3.0,
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bigscience/bloom-7b1/results_2024-02-22T11-33-09.451361.json

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