Upload results for model microsoft/Orca-2-13b (#204)

- Upload results for model microsoft/Orca-2-13b (97cba47e4f2661ce800ce87c8505cd552da1efd6)

data/microsoft/Orca-2-13b/cot/24-04-08-20:20:22_idx20.json

@@ -0,0 +1,212 @@
+{
+  "results": {
+    "error-tempore-3994_logiqa2_cot": {
+      "acc,none": 0.40458015267175573,
+      "acc_stderr,none": 0.012382999288486018,
+      "alias": "error-tempore-3994_logiqa2_cot"
+    },
+    "error-tempore-3994_logiqa_cot": {
+      "acc,none": 0.2971246006389776,
+      "acc_stderr,none": 0.018279674935145002,
+      "alias": "error-tempore-3994_logiqa_cot"
+    },
+    "error-tempore-3994_lsat-ar_cot": {
+      "acc,none": 0.22608695652173913,
+      "acc_stderr,none": 0.02764178570724133,
+      "alias": "error-tempore-3994_lsat-ar_cot"
+    },
+    "error-tempore-3994_lsat-lr_cot": {
+      "acc,none": 0.3509803921568627,
+      "acc_stderr,none": 0.021154922814544782,
+      "alias": "error-tempore-3994_lsat-lr_cot"
+    },
+    "error-tempore-3994_lsat-rc_cot": {
+      "acc,none": 0.4646840148698885,
+      "acc_stderr,none": 0.030466079816439715,
+      "alias": "error-tempore-3994_lsat-rc_cot"
+    }
+  },
+  "configs": {
+    "error-tempore-3994_logiqa2_cot": {
+      "task": "error-tempore-3994_logiqa2_cot",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "error-tempore-3994-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
+      }
+    },
+    "error-tempore-3994_logiqa_cot": {
+      "task": "error-tempore-3994_logiqa_cot",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "error-tempore-3994-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
+      }
+    },
+    "error-tempore-3994_lsat-ar_cot": {
+      "task": "error-tempore-3994_lsat-ar_cot",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "error-tempore-3994-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
+      }
+    },
+    "error-tempore-3994_lsat-lr_cot": {
+      "task": "error-tempore-3994_lsat-lr_cot",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "error-tempore-3994-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
+      }
+    },
+    "error-tempore-3994_lsat-rc_cot": {
+      "task": "error-tempore-3994_lsat-rc_cot",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "error-tempore-3994-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": {
+    "error-tempore-3994_logiqa2_cot": 0.0,
+    "error-tempore-3994_logiqa_cot": 0.0,
+    "error-tempore-3994_lsat-ar_cot": 0.0,
+    "error-tempore-3994_lsat-lr_cot": 0.0,
+    "error-tempore-3994_lsat-rc_cot": 0.0
+  },
+  "n-shot": {
+    "error-tempore-3994_logiqa2_cot": 0,
+    "error-tempore-3994_logiqa_cot": 0,
+    "error-tempore-3994_lsat-ar_cot": 0,
+    "error-tempore-3994_lsat-lr_cot": 0,
+    "error-tempore-3994_lsat-rc_cot": 0
+  },
+  "config": {
+    "model": "vllm",
+    "model_args": "pretrained=microsoft/Orca-2-13b,revision=main,dtype=float16,tensor_parallel_size=2,gpu_memory_utilization=0.7,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"
+}