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Upload results for model meta-llama/Llama-2-13b-hf (#129)

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- Upload results for model meta-llama/Llama-2-13b-hf (f4cafd1a1046af8837959c649e53b5c0cb2b6b21)

data/meta-llama/Llama-2-13b-hf/cot/24-03-25-06:04:55_idx20.json ADDED
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+ {
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+ "results": {
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+ "tenetur-nihil-3418_logiqa2_cot": {
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+ "acc,none": 0.28498727735368956,
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+ "acc_stderr,none": 0.011388893410930608,
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+ "alias": "tenetur-nihil-3418_logiqa2_cot"
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+ },
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+ "tenetur-nihil-3418_logiqa_cot": {
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+ "acc,none": 0.29073482428115016,
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+ "acc_stderr,none": 0.0181640562091778,
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+ "alias": "tenetur-nihil-3418_logiqa_cot"
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+ },
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+ "tenetur-nihil-3418_lsat-ar_cot": {
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+ "acc,none": 0.16521739130434782,
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+ "acc_stderr,none": 0.02454125880854541,
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+ "alias": "tenetur-nihil-3418_lsat-ar_cot"
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+ },
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+ "tenetur-nihil-3418_lsat-lr_cot": {
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+ "acc,none": 0.2372549019607843,
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+ "acc_stderr,none": 0.018855515858847986,
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+ "alias": "tenetur-nihil-3418_lsat-lr_cot"
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+ },
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+ "tenetur-nihil-3418_lsat-rc_cot": {
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+ "acc,none": 0.30855018587360594,
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+ "acc_stderr,none": 0.028214726272339067,
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+ "alias": "tenetur-nihil-3418_lsat-rc_cot"
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+ }
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+ },
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+ "configs": {
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+ "tenetur-nihil-3418_logiqa2_cot": {
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+ "task": "tenetur-nihil-3418_logiqa2_cot",
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+ "group": "logikon-bench",
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+ "dataset_path": "cot-leaderboard/cot-eval-traces",
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+ "dataset_kwargs": {
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+ "data_files": {
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+ "test": "tenetur-nihil-3418-logiqa2/test-00000-of-00001.parquet"
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+ }
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+ },
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+ "test_split": "test",
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+ "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",
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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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+ "num_fewshot": 0,
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+ "metric_list": [
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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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+ "tenetur-nihil-3418_logiqa_cot": {
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+ "task": "tenetur-nihil-3418_logiqa_cot",
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+ "group": "logikon-bench",
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+ "dataset_path": "cot-leaderboard/cot-eval-traces",
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+ "dataset_kwargs": {
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+ "data_files": {
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+ "test": "tenetur-nihil-3418-logiqa/test-00000-of-00001.parquet"
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+ }
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+ },
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+ "test_split": "test",
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+ "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",
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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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+ "num_fewshot": 0,
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+ "metric_list": [
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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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+ "tenetur-nihil-3418_lsat-ar_cot": {
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+ "task": "tenetur-nihil-3418_lsat-ar_cot",
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+ "group": "logikon-bench",
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+ "dataset_path": "cot-leaderboard/cot-eval-traces",
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+ "dataset_kwargs": {
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+ "data_files": {
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+ "test": "tenetur-nihil-3418-lsat-ar/test-00000-of-00001.parquet"
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+ }
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+ },
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+ "test_split": "test",
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+ "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",
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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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+ "num_fewshot": 0,
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+ "metric_list": [
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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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+ "tenetur-nihil-3418_lsat-lr_cot": {
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+ "task": "tenetur-nihil-3418_lsat-lr_cot",
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+ "group": "logikon-bench",
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+ "dataset_path": "cot-leaderboard/cot-eval-traces",
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+ "dataset_kwargs": {
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+ "data_files": {
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+ "test": "tenetur-nihil-3418-lsat-lr/test-00000-of-00001.parquet"
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+ }
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+ },
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+ "test_split": "test",
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+ "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",
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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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+ "num_fewshot": 0,
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+ "metric_list": [
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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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+ "tenetur-nihil-3418_lsat-rc_cot": {
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+ "task": "tenetur-nihil-3418_lsat-rc_cot",
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+ "group": "logikon-bench",
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+ "dataset_path": "cot-leaderboard/cot-eval-traces",
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+ "dataset_kwargs": {
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+ "data_files": {
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+ "test": "tenetur-nihil-3418-lsat-rc/test-00000-of-00001.parquet"
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+ }
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+ },
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+ "test_split": "test",
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+ "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",
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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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+ "num_fewshot": 0,
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+ "metric_list": [
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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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+ },
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+ "versions": {
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+ "tenetur-nihil-3418_logiqa2_cot": 0.0,
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+ "tenetur-nihil-3418_logiqa_cot": 0.0,
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+ "tenetur-nihil-3418_lsat-ar_cot": 0.0,
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+ "tenetur-nihil-3418_lsat-lr_cot": 0.0,
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+ "tenetur-nihil-3418_lsat-rc_cot": 0.0
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+ },
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+ "n-shot": {
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+ "tenetur-nihil-3418_logiqa2_cot": 0,
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+ "tenetur-nihil-3418_logiqa_cot": 0,
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+ "tenetur-nihil-3418_lsat-ar_cot": 0,
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+ "tenetur-nihil-3418_lsat-lr_cot": 0,
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+ "tenetur-nihil-3418_lsat-rc_cot": 0
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+ },
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+ "config": {
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+ "model": "vllm",
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+ "model_args": "pretrained=meta-llama/Llama-2-13b-hf,revision=main,dtype=float16,tensor_parallel_size=2,gpu_memory_utilization=0.7,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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+ "use_cache": null,
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+ "limit": null,
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+ "bootstrap_iters": 100000,
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+ "gen_kwargs": null
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+ },
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+ "git_hash": "fa353d4"
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+ }