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{ |
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"results": { |
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"logiqa2_base": { |
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"acc,none": 0.29834605597964375, |
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"acc_stderr,none": 0.011543394639779799, |
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"alias": "logiqa2_base" |
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}, |
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"logiqa_base": { |
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"acc,none": 0.2571884984025559, |
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"acc_stderr,none": 0.01748336693852745, |
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"alias": "logiqa_base" |
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}, |
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"lsat-ar_base": { |
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"acc,none": 0.18695652173913044, |
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"acc_stderr,none": 0.025763772398512325, |
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"alias": "lsat-ar_base" |
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}, |
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"lsat-lr_base": { |
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"acc,none": 0.21372549019607842, |
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"acc_stderr,none": 0.018170060276318237, |
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"alias": "lsat-lr_base" |
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}, |
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"lsat-rc_base": { |
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"acc,none": 0.27137546468401486, |
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"acc_stderr,none": 0.02716250308923951, |
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"alias": "lsat-rc_base" |
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} |
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}, |
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"configs": { |
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"logiqa2_base": { |
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"task": "logiqa2_base", |
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"group": "logikon-bench", |
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"dataset_path": "logikon/logikon-bench", |
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"dataset_name": "logiqa2", |
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"test_split": "test", |
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"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\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 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.\\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 += \"Answer:\"\n return prompt\n", |
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"doc_to_target": "{{answer}}", |
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"doc_to_choice": "{{options}}", |
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"description": "", |
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"target_delimiter": " ", |
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"fewshot_delimiter": "\n\n", |
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{ |
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"metric": "acc", |
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"aggregation": "mean", |
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"higher_is_better": true |
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} |
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], |
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"output_type": "multiple_choice", |
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"repeats": 1, |
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"should_decontaminate": false, |
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"metadata": { |
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"version": 0.0 |
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} |
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}, |
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"logiqa_base": { |
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"task": "logiqa_base", |
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"group": "logikon-bench", |
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"dataset_path": "logikon/logikon-bench", |
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"dataset_name": "logiqa", |
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"test_split": "test", |
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"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\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 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.\\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 += \"Answer:\"\n return prompt\n", |
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"doc_to_target": "{{answer}}", |
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"description": "", |
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"target_delimiter": " ", |
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{ |
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"metric": "acc", |
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} |
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"output_type": "multiple_choice", |
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"metadata": { |
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} |
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}, |
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"task": "lsat-ar_base", |
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"group": "logikon-bench", |
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"dataset_path": "logikon/logikon-bench", |
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"dataset_name": "lsat-ar", |
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"test_split": "test", |
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"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\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 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.\\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 += \"Answer:\"\n return prompt\n", |
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"doc_to_target": "{{answer}}", |
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"doc_to_choice": "{{options}}", |
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"description": "", |
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"target_delimiter": " ", |
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"fewshot_delimiter": "\n\n", |
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"metric_list": [ |
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{ |
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"metric": "acc", |
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"higher_is_better": true |
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} |
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], |
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"output_type": "multiple_choice", |
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"repeats": 1, |
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"should_decontaminate": false, |
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"metadata": { |
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"version": 0.0 |
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} |
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}, |
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"lsat-lr_base": { |
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"task": "lsat-lr_base", |
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"group": "logikon-bench", |
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"dataset_path": "logikon/logikon-bench", |
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"dataset_name": "lsat-lr", |
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"test_split": "test", |
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"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\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 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.\\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 += \"Answer:\"\n return prompt\n", |
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"doc_to_target": "{{answer}}", |
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"description": "", |
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"target_delimiter": " ", |
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"fewshot_delimiter": "\n\n", |
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{ |
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"metric": "acc", |
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} |
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], |
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"output_type": "multiple_choice", |
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"should_decontaminate": false, |
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"metadata": { |
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"version": 0.0 |
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} |
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}, |
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"lsat-rc_base": { |
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"task": "lsat-rc_base", |
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"group": "logikon-bench", |
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"dataset_path": "logikon/logikon-bench", |
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"dataset_name": "lsat-rc", |
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"test_split": "test", |
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"doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\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 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.\\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 += \"Answer:\"\n return prompt\n", |
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"doc_to_target": "{{answer}}", |
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"description": "", |
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"target_delimiter": " ", |
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"fewshot_delimiter": "\n\n", |
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{ |
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"metric": "acc", |
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} |
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], |
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"metadata": { |
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} |
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} |
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}, |
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"versions": { |
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}, |
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"config": { |
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"model": "vllm", |
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"model_args": "pretrained=NousResearch/Nous-Hermes-llama-2-7b,revision=main,dtype=bfloat16,tensor_parallel_size=1,gpu_memory_utilization=0.8,trust_remote_code=true,max_length=2048", |
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"batch_size": "auto", |
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"batch_sizes": [], |
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"device": null, |
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}, |
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"git_hash": "a550a44" |
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} |