Uploading raw results for princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2

.gitattributes

@@ -11047,3 +11047,6 @@ Magpie-Align/MagpieLM-8B-Chat-v0.1/raw_2025-05-07T15-22-16.111876/pretrained__Ma
 Magpie-Align/MagpieLM-8B-Chat-v0.1/raw_2025-05-07T15-22-16.111876/pretrained__Magpie-Align__MagpieLM-8B-Chat-v0.1,dtype__bfloat16,revision__main,trust_remote_code__True,max_length__4098_enem_challenge.jsonl filter=lfs diff=lfs merge=lfs -text
 Magpie-Align/MagpieLM-8B-Chat-v0.1/raw_2025-05-07T15-22-16.111876/pretrained__Magpie-Align__MagpieLM-8B-Chat-v0.1,dtype__bfloat16,revision__main,trust_remote_code__True,max_length__4098_oab_exams.jsonl filter=lfs diff=lfs merge=lfs -text
 Magpie-Align/MagpieLM-8B-Chat-v0.1/raw_2025-05-07T15-22-16.111876/pretrained__Magpie-Align__MagpieLM-8B-Chat-v0.1,dtype__bfloat16,revision__main,trust_remote_code__True,max_length__4098_tweetsentbr.jsonl filter=lfs diff=lfs merge=lfs -text
+princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2/raw_2025-05-07T15-51-57.421344/pretrained__princeton-nlp__Llama-3-Instruct-8B-SimPO-v0.2,dtype__bfloat16,revision__9ac0fbee445e7755e50520e9881d67588b4b854c,trust_remote_code__True,max_length__4098_assin2_rte.jsonl filter=lfs diff=lfs merge=lfs -text
+princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2/raw_2025-05-07T15-51-57.421344/pretrained__princeton-nlp__Llama-3-Instruct-8B-SimPO-v0.2,dtype__bfloat16,revision__9ac0fbee445e7755e50520e9881d67588b4b854c,trust_remote_code__True,max_length__4098_assin2_sts.jsonl filter=lfs diff=lfs merge=lfs -text
+princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2/raw_2025-05-07T15-51-57.421344/pretrained__princeton-nlp__Llama-3-Instruct-8B-SimPO-v0.2,dtype__bfloat16,revision__9ac0fbee445e7755e50520e9881d67588b4b854c,trust_remote_code__True,max_length__4098_oab_exams.jsonl filter=lfs diff=lfs merge=lfs -text

princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2/raw_2024-08-11T19-34-03.542478/results.json

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+      "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",
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-            "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",
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-            "should_decontaminate": false,
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+      "doc_to_target": "<function assin2_float_to_pt_str at 0x7fd7dd0f54e0>",
+      "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": " ",
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+      "metric_list": [
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+          "aggregation": "pearsonr",
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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 0x7fd7dd0f54e0>",
-            "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": " ",
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+        {
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+          "aggregation": "mean_squared_error",
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+        }
+      ],
+      "output_type": "generate_until",
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-                        }
-                    ]
-                }
-            ],
-            "should_decontaminate": false,
-            "metadata": {
-                "version": 1.1
+            {
+              "function": "take_first"
             }
-        },
-        "bluex": {
-            "task": "bluex",
-            "group": [
-                "pt_benchmark",
-                "vestibular"
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-            "doc_to_text": "<function enem_doc_to_text at 0x7fd7dd0f4ea0>",
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-                        "UNICAMP_2018_2",
-                        "USP_2018_35",
-                        "UNICAMP_2018_16",
-                        "USP_2018_89"
-                    ],
-                    "id_column": "id",
-                    "exclude_from_task": true
-                }
+          ]
+        }
+      ],
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.1
+      }
+    },
+    "bluex": {
+      "task": "bluex",
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+        "pt_benchmark",
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+      ],
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+      "doc_to_text": "<function enem_doc_to_text at 0x7fd7dd0f4ea0>",
+      "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",
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+        "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,
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+              "function": "remove_accents"
             },
-            "num_fewshot": 3,
-            "metric_list": [
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-                    "metric": "acc",
-                    "aggregation": "acc",
-                    "higher_is_better": true
-                }
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-                "temperature": 0.0,
-                "top_k": null,
-                "top_p": null,
-                "until": [
-                    "\n\n"
-                ]
+            {
+              "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"
+              ]
             },
-            "repeats": 1,
-            "filter_list": [
-                {
-                    "name": "all",
-                    "filter": [
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-                        },
-                        {
-                            "function": "find_choices",
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-                                "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",
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-            ],
-            "should_decontaminate": true,
-            "doc_to_decontamination_query": "<function enem_doc_to_text at 0x7fd7dd0f5120>",
-            "metadata": {
-                "version": 1.1
+            {
+              "function": "take_first"
             }
-        },
-        "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 0x7fd7dd0f56c0>",
-            "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
-                }
+          ],
+          "group_by": {
+            "column": "exam_id"
+          }
+        }
+      ],
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "<function enem_doc_to_text at 0x7fd7dd0f5120>",
+      "metadata": {
+        "version": 1.1
+      }
+    },
+    "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 0x7fd7dd0f56c0>",
+      "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"
             },
-            "num_fewshot": 3,
-            "metric_list": [
-                {
-                    "metric": "acc",
-                    "aggregation": "acc",
-                    "higher_is_better": true
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-            ],
-            "output_type": "generate_until",
-            "generation_kwargs": {
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-                "do_sample": false,
-                "temperature": 0.0,
-                "top_k": null,
-                "top_p": null,
-                "until": [
-                    "\n\n"
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+            {
+              "function": "remove_accents"
             },
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-            "filter_list": [
-                {
-                    "name": "all",
-                    "filter": [
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-                            "function": "remove_accents"
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-                        {
-                            "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 0x7fd7dd0f5940>",
-            "metadata": {
-                "version": 1.1
+            {
+              "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 0x7fd7dd0f5940>",
+      "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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+            301,
+            1185,
+            1315,
+            1420,
+            2436,
+            2322,
+            766
+          ]
+        }
+      },
+      "num_fewshot": 15,
+      "metric_list": [
+        {
+          "metric": "f1_macro",
+          "aggregation": "f1_macro",
+          "higher_is_better": true
         },
-        "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,
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-                        1503,
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-                        601,
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-                        995,
-                        2233,
-                        1982,
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-                        236,
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-                        2436,
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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"
-                ]
+        {
+          "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"
+              ]
             },
-            "repeats": 1,
-            "filter_list": [
-                {
-                    "name": "all",
-                    "filter": [
-                        {
-                            "function": "find_similar_label",
-                            "labels": [
-                                "Sim",
-                                "Não"
-                            ]
-                        },
-                        {
-                            "function": "take_first"
-                        }
-                    ]
-                }
-            ],
-            "should_decontaminate": false,
-            "metadata": {
-                "version": 1.1
+            {
+              "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": [
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+            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
         },
-        "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",
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+        }
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+      "output_type": "generate_until",
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-                        }
-                    ]
-                }
-            ],
-            "should_decontaminate": false,
-            "metadata": {
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+            {
+              "function": "take_first"
             }
-        },
-        "oab_exams": {
-            "task": "oab_exams",
-            "group": [
-                "legal_benchmark",
-                "pt_benchmark"
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-                        "2010-01_38",
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-                        "2010-01_76",
-                        "2010-01_83",
-                        "2010-01_85",
-                        "2010-01_91",
-                        "2010-01_99"
-                    ],
-                    "id_column": "id",
-                    "exclude_from_task": true
-                }
+          ]
+        }
+      ],
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "oab_exams": {
+      "task": "oab_exams",
+      "group": [
+        "legal_benchmark",
+        "pt_benchmark"
+      ],
+      "dataset_path": "eduagarcia/oab_exams",
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+      "doc_to_text": "<function doc_to_text at 0x7fd7dd0f4860>",
+      "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",
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+          "id_list": [
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+            "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": [
+        {
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+          "aggregation": "acc",
+          "higher_is_better": true
+        }
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-                "temperature": 0.0,
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-                                "C",
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+                "B",
+                "C",
+                "D"
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+                "(?:[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"
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             }
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+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "<function doc_to_text at 0x7fd7dd0f4ae0>",
+      "metadata": {
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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",
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+          "metric": "f1_macro",
+          "aggregation": "f1_macro",
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         },
-        "portuguese_hate_speech": {
-            "task": "portuguese_hate_speech",
-            "task_alias": "portuguese_hate_speech_binary",
-            "group": [
-                "pt_benchmark"
-            ],
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-            "dataset_name": "Portuguese_Hate_Speech_binary",
-            "test_split": "test",
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-            "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",
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-                "until": [
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+        {
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+              "function": "find_similar_label",
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+                "Não"
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             },
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-                    "name": "all",
-                    "filter": [
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-                                "Não"
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-                            "function": "take_first"
-                        }
-                    ]
-                }
-            ],
-            "should_decontaminate": false,
-            "metadata": {
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+            {
+              "function": "take_first"
             }
+          ]
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+      "should_decontaminate": false,
+      "metadata": {
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+    "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"
+      },
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+      "metric_list": [
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+          "aggregation": "f1_macro",
+          "higher_is_better": true
         },
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-            "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": " ",
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-                    "aggregation": "f1_macro",
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-                    ]
-                }
-            ],
-            "should_decontaminate": false,
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         }
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+          "id_list": [
+            1,
+            3251,
+            2,
+            3252,
+            3,
+            4,
+            5,
+            6,
+            3253,
+            7,
+            3254,
+            3255,
+            3256,
+            8,
+            9,
+            10,
+            3257,
+            11,
+            3258,
+            12,
+            13,
+            14,
+            15,
+            3259,
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+            3262,
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+            16,
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+            19,
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+            22,
+            3272,
+            3273,
+            23,
+            3274,
+            24,
+            25,
+            3275
+          ],
+          "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": {
+        "do_sample": false,
+        "temperature": 0.0,
+        "top_k": null,
+        "top_p": null,
+        "until": [
+          "\n\n"
+        ]
+      },
+      "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": 1.1
+      }
+    },
+    "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 0x7f753d173920>",
+      "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": [
+            "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": {
+        "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 0x7f753d173c40>",
+      "metadata": {
+        "version": 1.1
+      }
+    },
+    "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 0x7f753cfac4a0>",
+      "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": {
+        "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 0x7f753cfac7c0>",
+      "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,
+            1670,
+            323,
+            236,
+            1263,
+            1562,
+            2519,
+            1049,
+            432,
+            1167,
+            1394,
+            2022,
+            2551,
+            2194,
+            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": {
+        "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,
+            20,
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+            88,
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+            16,
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+            40,
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+            4,
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+            3523,
+            3551,
+            68,
+            3503,
+            84,
+            3539,
+            64,
+            3599,
+            80,
+            3563,
+            3559,
+            3543,
+            3547,
+            3587,
+            3595,
+            3575,
+            3567,
+            3591,
+            24,
+            96,
+            92,
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+            52,
+            72,
+            8,
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+            3515,
+            3519,
+            3531,
+            28,
+            32,
+            0,
+            12,
+            3583
+          ],
+          "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": {
+        "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 0x7f753d1732e0>",
+      "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 0x7f753d173420>",
+      "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,
+            66,
+            41,
+            9,
+            4,
+            91,
+            42,
+            7,
+            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": {
+        "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": {
+        "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": {
+    "assin2_rte": 15,
+    "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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princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2/results_2024-08-11T19-34-03.542478.json

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princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2/results_2025-05-07T15-51-57.421344.json

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