cot-eval-results / data /openbmb /Eurus-70b-sft /cot /24-04-09-20:47:23_idx25.json
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{
"results": {
"quas-magnam-7785_logiqa2_cot": {
"acc,none": 0.3651399491094148,
"acc_stderr,none": 0.012147323083674137,
"alias": "quas-magnam-7785_logiqa2_cot"
},
"quas-magnam-7785_logiqa_cot": {
"acc,none": 0.3146964856230032,
"acc_stderr,none": 0.018575795328740333,
"alias": "quas-magnam-7785_logiqa_cot"
},
"quas-magnam-7785_lsat-ar_cot": {
"acc,none": 0.23043478260869565,
"acc_stderr,none": 0.02782780752227616,
"alias": "quas-magnam-7785_lsat-ar_cot"
},
"quas-magnam-7785_lsat-lr_cot": {
"acc,none": 0.3352941176470588,
"acc_stderr,none": 0.020925162390233513,
"alias": "quas-magnam-7785_lsat-lr_cot"
},
"quas-magnam-7785_lsat-rc_cot": {
"acc,none": 0.3828996282527881,
"acc_stderr,none": 0.029692924865649523,
"alias": "quas-magnam-7785_lsat-rc_cot"
}
},
"configs": {
"quas-magnam-7785_logiqa2_cot": {
"task": "quas-magnam-7785_logiqa2_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "quas-magnam-7785-logiqa2/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"quas-magnam-7785_logiqa_cot": {
"task": "quas-magnam-7785_logiqa_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "quas-magnam-7785-logiqa/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"quas-magnam-7785_lsat-ar_cot": {
"task": "quas-magnam-7785_lsat-ar_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "quas-magnam-7785-lsat-ar/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"quas-magnam-7785_lsat-lr_cot": {
"task": "quas-magnam-7785_lsat-lr_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "quas-magnam-7785-lsat-lr/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"quas-magnam-7785_lsat-rc_cot": {
"task": "quas-magnam-7785_lsat-rc_cot",
"group": "logikon-bench",
"dataset_path": "cot-leaderboard/cot-eval-traces",
"dataset_kwargs": {
"data_files": {
"test": "quas-magnam-7785-lsat-rc/test-00000-of-00001.parquet"
}
},
"test_split": "test",
"doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
"doc_to_target": "{{answer}}",
"doc_to_choice": "{{options}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
}
},
"versions": {
"quas-magnam-7785_logiqa2_cot": 0.0,
"quas-magnam-7785_logiqa_cot": 0.0,
"quas-magnam-7785_lsat-ar_cot": 0.0,
"quas-magnam-7785_lsat-lr_cot": 0.0,
"quas-magnam-7785_lsat-rc_cot": 0.0
},
"n-shot": {
"quas-magnam-7785_logiqa2_cot": 0,
"quas-magnam-7785_logiqa_cot": 0,
"quas-magnam-7785_lsat-ar_cot": 0,
"quas-magnam-7785_lsat-lr_cot": 0,
"quas-magnam-7785_lsat-rc_cot": 0
},
"config": {
"model": "vllm",
"model_args": "pretrained=openbmb/Eurus-70b-sft,revision=main,dtype=float16,tensor_parallel_size=8,gpu_memory_utilization=0.8,trust_remote_code=true,max_length=2048",
"batch_size": "auto",
"batch_sizes": [],
"device": null,
"use_cache": null,
"limit": null,
"bootstrap_iters": 100000,
"gen_kwargs": null
},
"git_hash": "741db1c"
}