Upload results for model mistralai/Mixtral-8x7B-Instruct-v0.1

data/mistralai/Mixtral-8x7B-Instruct-v0.1/orig/results_24-04-09-09:24:28.json

@@ -0,0 +1,192 @@
+{
+  "results": {
+    "logiqa2_base": {
+      "acc,none": 0.43384223918575066,
+      "acc_stderr,none": 0.012503930653393674,
+      "alias": "logiqa2_base"
+    },
+    "logiqa_base": {
+      "acc,none": 0.33386581469648563,
+      "acc_stderr,none": 0.018863697727584026,
+      "alias": "logiqa_base"
+    },
+    "lsat-ar_base": {
+      "acc,none": 0.19130434782608696,
+      "acc_stderr,none": 0.025991852462828483,
+      "alias": "lsat-ar_base"
+    },
+    "lsat-lr_base": {
+      "acc,none": 0.3509803921568627,
+      "acc_stderr,none": 0.021154922814544782,
+      "alias": "lsat-lr_base"
+    },
+    "lsat-rc_base": {
+      "acc,none": 0.40892193308550184,
+      "acc_stderr,none": 0.030031375739924634,
+      "alias": "lsat-rc_base"
+    }
+  },
+  "configs": {
+    "logiqa2_base": {
+      "task": "logiqa2_base",
+      "group": "logikon-bench",
+      "dataset_path": "logikon/logikon-bench",
+      "dataset_name": "logiqa2",
+      "test_split": "test",
+      "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",
+      "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
+      }
+    },
+    "logiqa_base": {
+      "task": "logiqa_base",
+      "group": "logikon-bench",
+      "dataset_path": "logikon/logikon-bench",
+      "dataset_name": "logiqa",
+      "test_split": "test",
+      "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",
+      "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
+      }
+    },
+    "lsat-ar_base": {
+      "task": "lsat-ar_base",
+      "group": "logikon-bench",
+      "dataset_path": "logikon/logikon-bench",
+      "dataset_name": "lsat-ar",
+      "test_split": "test",
+      "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",
+      "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
+      }
+    },
+    "lsat-lr_base": {
+      "task": "lsat-lr_base",
+      "group": "logikon-bench",
+      "dataset_path": "logikon/logikon-bench",
+      "dataset_name": "lsat-lr",
+      "test_split": "test",
+      "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",
+      "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
+      }
+    },
+    "lsat-rc_base": {
+      "task": "lsat-rc_base",
+      "group": "logikon-bench",
+      "dataset_path": "logikon/logikon-bench",
+      "dataset_name": "lsat-rc",
+      "test_split": "test",
+      "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",
+      "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": {
+    "logiqa2_base": 0.0,
+    "logiqa_base": 0.0,
+    "lsat-ar_base": 0.0,
+    "lsat-lr_base": 0.0,
+    "lsat-rc_base": 0.0
+  },
+  "n-shot": {
+    "logiqa2_base": 0,
+    "logiqa_base": 0,
+    "lsat-ar_base": 0,
+    "lsat-lr_base": 0,
+    "lsat-rc_base": 0
+  },
+  "config": {
+    "model": "vllm",
+    "model_args": "pretrained=mistralai/Mixtral-8x7B-Instruct-v0.1,revision=main,dtype=bfloat16,tensor_parallel_size=4,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"
+}