Uploading raw results for VAGOsolutions/SauerkrautLM-Gemma-7b

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+      "fewshot_split": "train",
+      "doc_to_text": "<function enem_doc_to_text at 0x7fa14e17a340>",
+      "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": [
+          "\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 0x7fa14e17a5c0>",
+      "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,
+            2910,
+            2227,
+            2813,
+            974,
+            558,
+            1503,
+            1958,
+            2918,
+            601,
+            1560,
+            984,
+            2388,
+            995,
+            2233,
+            1982,
+            165,
+            2788,
+            1312,
+            2285,
+            522,
+            1113,
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+            323,
+            236,
+            1263,
+            1562,
+            2519,
+            1049,
+            432,
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+            1394,
+            2022,
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+            2187,
+            2282,
+            2816,
+            108,
+            301,
+            1185,
+            1315,
+            1420,
+            2436,
+            2322,
+            766
+          ]
+        }
+      },
+      "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,
+            44,
+            36,
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+            3531,
+            28,
+            32,
+            0,
+            12,
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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 0x7fa14e1794e0>",
+      "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"
+        ]
+      },
+      "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 0x7fa14e179760>",
+      "metadata": {
+        "version": 1.5
+      }
+    },
+    "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": {
+          "id_list": [
+            52,
+            50,
+            39,
+            28,
+            3,
+            105,
+            22,
+            25,
+            60,
+            11,
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+            41,
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+            4,
+            91,
+            42,
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+            20,
+            76,
+            1,
+            104,
+            13,
+            67,
+            54,
+            97,
+            27,
+            24,
+            14,
+            16,
+            48,
+            53,
+            40,
+            34,
+            49,
+            32,
+            119,
+            114,
+            2,
+            58,
+            83,
+            18,
+            36,
+            5,
+            6,
+            10,
+            35,
+            38,
+            0,
+            21,
+            46
+          ],
+          "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/tweetsentbr_fewshot",
+      "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": "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
+        }
+      ],
+      "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": [
+                "Positivo",
+                "Neutro",
+                "Negativo"
+              ]
+            },
+            {
+              "function": "take_first"
+            }
+          ]
+        }
+      ],
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    }
+  },
+  "versions": {
+    "assin2_rte": 1.1,
+    "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
+  },
+  "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
+  },
+  "model_meta": {
+    "truncated": 0,
+    "non_truncated": 14150,
+    "padded": 0,
+    "non_padded": 14150,
+    "fewshots_truncated": 0,
+    "has_chat_template": false,
+    "chat_type": null,
+    "n_gpus": 1,
+    "accelerate_num_process": null,
+    "model_sha": "4296bdabf82e900235b094e5348be03ebb0ec891",
+    "model_dtype": "torch.bfloat16",
+    "model_memory_footprint": 17075376128,
+    "model_num_parameters": 8537680896,
+    "model_is_loaded_in_4bit": null,
+    "model_is_loaded_in_8bit": null,
+    "model_is_quantized": null,
+    "model_device": "cuda:0",
+    "batch_size": 2,
+    "max_length": 2560,
+    "max_ctx_length": 2528,
+    "max_gen_toks": 32
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+  "task_model_meta": {
+    "assin2_rte": {
+      "sample_size": 2448,
+      "truncated": 0,
+      "non_truncated": 2448,
+      "padded": 0,
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+      "max_gen_toks": 32,
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+    "assin2_sts": {
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+      "truncated": 0,
+      "non_truncated": 2448,
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+      "non_padded": 2448,
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+      "max_seq_length": 1260,
+      "max_ctx_length": 2528,
+      "max_gen_toks": 32,
+      "mean_original_fewshots_size": 15.0,
+      "mean_effective_fewshot_size": 15.0
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+    "bluex": {
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+      "truncated": 0,
+      "non_truncated": 719,
+      "padded": 0,
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+      "max_seq_length": 1993,
+      "max_ctx_length": 2528,
+      "max_gen_toks": 32,
+      "mean_original_fewshots_size": 3.0,
+      "mean_effective_fewshot_size": 3.0
+    },
+    "enem_challenge": {
+      "sample_size": 1429,
+      "truncated": 0,
+      "non_truncated": 1429,
+      "padded": 0,
+      "non_padded": 1429,
+      "fewshots_truncated": 0,
+      "mean_seq_length": 1215.8278516445066,
+      "min_seq_length": 1019,
+      "max_seq_length": 2062,
+      "max_ctx_length": 2528,
+      "max_gen_toks": 32,
+      "mean_original_fewshots_size": 3.0,
+      "mean_effective_fewshot_size": 3.0
+    },
+    "faquad_nli": {
+      "sample_size": 650,
+      "truncated": 0,
+      "non_truncated": 650,
+      "padded": 0,
+      "non_padded": 650,
+      "fewshots_truncated": 0,
+      "mean_seq_length": 1094.1338461538462,
+      "min_seq_length": 1056,
+      "max_seq_length": 1167,
+      "max_ctx_length": 2528,
+      "max_gen_toks": 32,
+      "mean_original_fewshots_size": 15.0,
+      "mean_effective_fewshot_size": 15.0
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+    "hatebr_offensive": {
+      "sample_size": 1400,
+      "truncated": 0,
+      "non_truncated": 1400,
+      "padded": 0,
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+      "fewshots_truncated": 0,
+      "mean_seq_length": 896.505,
+      "min_seq_length": 881,
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+      "max_ctx_length": 2528,
+      "max_gen_toks": 32,
+      "mean_original_fewshots_size": 25.0,
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+      "truncated": 0,
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+      "fewshots_truncated": 0,
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+      "min_seq_length": 791,
+      "max_seq_length": 1322,
+      "max_ctx_length": 2528,
+      "max_gen_toks": 32,
+      "mean_original_fewshots_size": 3.0,
+      "mean_effective_fewshot_size": 3.0
+    },
+    "portuguese_hate_speech": {
+      "sample_size": 851,
+      "truncated": 0,
+      "non_truncated": 851,
+      "padded": 0,
+      "non_padded": 851,
+      "fewshots_truncated": 0,
+      "mean_seq_length": 1272.13866039953,
+      "min_seq_length": 1244,
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VAGOsolutions/SauerkrautLM-Gemma-7b/results_2024-05-20T03-35-55.668254.json

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