Upload results for model NousResearch/Nous-Hermes-llama-2-7b

data/NousResearch/Nous-Hermes-llama-2-7b/cot/24-03-22-00:25:09_idx10.json

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+{
+  "results": {
+    "reprehenderit-dolor-1347_logiqa2_cot": {
+      "acc,none": 0.3008905852417303,
+      "acc_stderr,none": 0.011571476624151531,
+      "alias": "reprehenderit-dolor-1347_logiqa2_cot"
+    },
+    "reprehenderit-dolor-1347_logiqa_cot": {
+      "acc,none": 0.305111821086262,
+      "acc_stderr,none": 0.018418190908759225,
+      "alias": "reprehenderit-dolor-1347_logiqa_cot"
+    },
+    "reprehenderit-dolor-1347_lsat-ar_cot": {
+      "acc,none": 0.22608695652173913,
+      "acc_stderr,none": 0.02764178570724134,
+      "alias": "reprehenderit-dolor-1347_lsat-ar_cot"
+    },
+    "reprehenderit-dolor-1347_lsat-lr_cot": {
+      "acc,none": 0.21764705882352942,
+      "acc_stderr,none": 0.018290217500245263,
+      "alias": "reprehenderit-dolor-1347_lsat-lr_cot"
+    },
+    "reprehenderit-dolor-1347_lsat-rc_cot": {
+      "acc,none": 0.25650557620817843,
+      "acc_stderr,none": 0.026675948246675074,
+      "alias": "reprehenderit-dolor-1347_lsat-rc_cot"
+    }
+  },
+  "configs": {
+    "reprehenderit-dolor-1347_logiqa2_cot": {
+      "task": "reprehenderit-dolor-1347_logiqa2_cot",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "reprehenderit-dolor-1347-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
+      }
+    },
+    "reprehenderit-dolor-1347_logiqa_cot": {
+      "task": "reprehenderit-dolor-1347_logiqa_cot",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "reprehenderit-dolor-1347-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
+      }
+    },
+    "reprehenderit-dolor-1347_lsat-ar_cot": {
+      "task": "reprehenderit-dolor-1347_lsat-ar_cot",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "reprehenderit-dolor-1347-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
+      }
+    },
+    "reprehenderit-dolor-1347_lsat-lr_cot": {
+      "task": "reprehenderit-dolor-1347_lsat-lr_cot",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "reprehenderit-dolor-1347-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
+      }
+    },
+    "reprehenderit-dolor-1347_lsat-rc_cot": {
+      "task": "reprehenderit-dolor-1347_lsat-rc_cot",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "reprehenderit-dolor-1347-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": {
+    "reprehenderit-dolor-1347_logiqa2_cot": 0.0,
+    "reprehenderit-dolor-1347_logiqa_cot": 0.0,
+    "reprehenderit-dolor-1347_lsat-ar_cot": 0.0,
+    "reprehenderit-dolor-1347_lsat-lr_cot": 0.0,
+    "reprehenderit-dolor-1347_lsat-rc_cot": 0.0
+  },
+  "n-shot": {
+    "reprehenderit-dolor-1347_logiqa2_cot": 0,
+    "reprehenderit-dolor-1347_logiqa_cot": 0,
+    "reprehenderit-dolor-1347_lsat-ar_cot": 0,
+    "reprehenderit-dolor-1347_lsat-lr_cot": 0,
+    "reprehenderit-dolor-1347_lsat-rc_cot": 0
+  },
+  "config": {
+    "model": "vllm",
+    "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",
+    "batch_size": "auto",
+    "batch_sizes": [],
+    "device": null,
+    "use_cache": null,
+    "limit": null,
+    "bootstrap_iters": 100000,
+    "gen_kwargs": null
+  },
+  "git_hash": "a550a44"
+}