Update demo-leaderboard/gpt2-demo/results_2023-11-21T18-10-08.json

demo-leaderboard/gpt2-demo/results_2023-11-21T18-10-08.json

@@ -1,15 +1,436 @@
 {
-    "config": {
-        "model_dtype": "torch.float16", 
-        "model_name": "demo-leaderboard/gpt2-demo",
-        "model_sha": "ac3299b02780836378b9e1e68c6eead546e89f90"
+  "results": {
+    "multimedqa": {
+      "alias": "stem",
+      "acc_norm,none": 0.33614369501466274,
+      "acc_norm_stderr,none": 0.006394243385413683,
+      "acc,none": 0.3666430092264017,
+      "acc_stderr,none": 0.005640719286996951
+    },
+    "medmcqa": {
+      "acc,none": 0.34305522352378676,
+      "acc_stderr,none": 0.007340986677455324,
+      "acc_norm,none": 0.34305522352378676,
+      "acc_norm_stderr,none": 0.007340986677455324,
+      "alias": " - medmcqa"
+    },
+    "medqa_4options": {
+      "acc,none": 0.31343283582089554,
+      "acc_stderr,none": 0.013006792288528347,
+      "acc_norm,none": 0.31343283582089554,
+      "acc_norm_stderr,none": 0.013006792288528347,
+      "alias": " - medqa_4options"
+    },
+    "mmlu_anatomy": {
+      "alias": " - anatomy (mmlu)",
+      "acc,none": 0.3925925925925926,
+      "acc_stderr,none": 0.0421850621536888
+    },
+    "mmlu_clinical_knowledge": {
+      "alias": " - clinical_knowledge (mmlu)",
+      "acc,none": 0.46037735849056605,
+      "acc_stderr,none": 0.030676096599389188
+    },
+    "mmlu_college_biology": {
+      "alias": " - college_biology (mmlu)",
+      "acc,none": 0.3402777777777778,
+      "acc_stderr,none": 0.03962135573486219
+    },
+    "mmlu_college_medicine": {
+      "alias": " - college_medicine (mmlu)",
+      "acc,none": 0.37572254335260113,
+      "acc_stderr,none": 0.036928207672648664
     },
-    "results": {
-        "medmcqa": {
-            "acc,none": 0
+    "mmlu_medical_genetics": {
+      "alias": " - medical_genetics (mmlu)",
+      "acc,none": 0.42,
+      "acc_stderr,none": 0.04960449637488584
+    },
+    "mmlu_professional_medicine": {
+      "alias": " - professional_medicine (mmlu)",
+      "acc,none": 0.29044117647058826,
+      "acc_stderr,none": 0.027576468622740522
+    },
+    "pubmedqa": {
+      "acc,none": 0.678,
+      "acc_stderr,none": 0.02091666833001987,
+      "alias": " - pubmedqa"
+    }
+  },
+  "groups": {
+    "multimedqa": {
+      "alias": "stem",
+      "acc_norm,none": 0.33614369501466274,
+      "acc_norm_stderr,none": 0.006394243385413683,
+      "acc,none": 0.3666430092264017,
+      "acc_stderr,none": 0.005640719286996951
+    }
+  },
+  "group_subtasks": {
+    "multimedqa": [
+      "mmlu_college_biology",
+      "mmlu_professional_medicine",
+      "mmlu_medical_genetics",
+      "mmlu_college_medicine",
+      "mmlu_clinical_knowledge",
+      "mmlu_anatomy",
+      "medqa_4options",
+      "medmcqa",
+      "pubmedqa"
+    ]
+  },
+  "configs": {
+    "medmcqa": {
+      "task": "medmcqa",
+      "dataset_path": "medmcqa",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "validation",
+      "doc_to_text": "def doc_to_text(doc) -> str:\n    \"\"\"\n    Question: <question>\n    Choices:\n    A. <choice1>\n    B. <choice2>\n    C. <choice3>\n    D. <choice4>\n    Answer:\n    \"\"\"\n    choices = [doc[\"opa\"], doc[\"opb\"], doc[\"opc\"], doc[\"opd\"]]\n    option_choices = {\n        \"A\": choices[0],\n        \"B\": choices[1],\n        \"C\": choices[2],\n        \"D\": choices[3],\n    }\n\n    prompt = \"Question: \" + doc[\"question\"] + \"\\nChoices:\\n\"\n    for choice, option in option_choices.items():\n        prompt += f\"{choice.upper()}. {option}\\n\"\n    prompt += \"Answer:\"\n    return prompt\n",
+      "doc_to_target": "cop",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
         },
-        "task_name2": {
-            "metric_name": 0.90
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "{{question}}"
+    },
+    "medqa_4options": {
+      "task": "medqa_4options",
+      "dataset_path": "GBaker/MedQA-USMLE-4-options-hf",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "def doc_to_text(doc) -> str:\n    option_choices = {\n        \"A\": doc[\"ending0\"],\n        \"B\": doc[\"ending1\"],\n        \"C\": doc[\"ending2\"],\n        \"D\": doc[\"ending3\"],\n    }\n    answers = \"\".join((f\"{k}. {v}\\n\") for k, v in option_choices.items())\n    return f\"Question: {doc['sent1']}\\n{answers}Answer:\"\n",
+      "doc_to_target": "def doc_to_target(doc) -> int:\n    return doc[\"label\"]\n",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false
+    },
+    "mmlu_anatomy": {
+      "task": "mmlu_anatomy",
+      "task_alias": "anatomy (mmlu)",
+      "group": "multimedqa",
+      "group_alias": "stem",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "anatomy",
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about anatomy.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 0.0
+      }
+    },
+    "mmlu_clinical_knowledge": {
+      "task": "mmlu_clinical_knowledge",
+      "task_alias": "clinical_knowledge (mmlu)",
+      "group": "multimedqa",
+      "group_alias": "other",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "clinical_knowledge",
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about clinical knowledge.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 0.0
+      }
+    },
+    "mmlu_college_biology": {
+      "task": "mmlu_college_biology",
+      "task_alias": "college_biology (mmlu)",
+      "group": "multimedqa",
+      "group_alias": "stem",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "college_biology",
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about college biology.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 0.0
+      }
+    },
+    "mmlu_college_medicine": {
+      "task": "mmlu_college_medicine",
+      "task_alias": "college_medicine (mmlu)",
+      "group": "multimedqa",
+      "group_alias": "other",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "college_medicine",
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about college medicine.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 0.0
+      }
+    },
+    "mmlu_medical_genetics": {
+      "task": "mmlu_medical_genetics",
+      "task_alias": "medical_genetics (mmlu)",
+      "group": "multimedqa",
+      "group_alias": "other",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "medical_genetics",
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about medical genetics.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
         }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 0.0
+      }
+    },
+    "mmlu_professional_medicine": {
+      "task": "mmlu_professional_medicine",
+      "task_alias": "professional_medicine (mmlu)",
+      "group": "multimedqa",
+      "group_alias": "other",
+      "dataset_path": "hails/mmlu_no_train",
+      "dataset_name": "professional_medicine",
+      "test_split": "test",
+      "fewshot_split": "dev",
+      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
+      "doc_to_target": "answer",
+      "doc_to_choice": [
+        "A",
+        "B",
+        "C",
+        "D"
+      ],
+      "description": "The following are multiple choice questions (with answers) about professional medicine.\n\n",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "fewshot_config": {
+        "sampler": "first_n"
+      },
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 0.0
+      }
+    },
+    "pubmedqa": {
+      "task": "pubmedqa",
+      "dataset_path": "bigbio/pubmed_qa",
+      "dataset_name": "pubmed_qa_labeled_fold0_source",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "def doc_to_text(doc) -> str:\n    ctxs = \"\\n\".join(doc[\"CONTEXTS\"])\n    return \"Abstract: {}\\nQuestion: {}\\nAnswer:\".format(\n        ctxs,\n        doc[\"QUESTION\"],\n    )\n",
+      "doc_to_target": "final_decision",
+      "doc_to_choice": [
+        "yes",
+        "no",
+        "maybe"
+      ],
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
     }
+  },
+  "versions": {
+    "medmcqa": "Yaml",
+    "medqa_4options": "Yaml",
+    "mmlu_anatomy": 0.0,
+    "mmlu_clinical_knowledge": 0.0,
+    "mmlu_college_biology": 0.0,
+    "mmlu_college_medicine": 0.0,
+    "mmlu_medical_genetics": 0.0,
+    "mmlu_professional_medicine": 0.0,
+    "pubmedqa": 1.0
+  },
+  "n-shot": {
+    "medmcqa": null,
+    "medqa_4options": null,
+    "mmlu_anatomy": null,
+    "mmlu_clinical_knowledge": null,
+    "mmlu_college_biology": null,
+    "mmlu_college_medicine": null,
+    "mmlu_medical_genetics": null,
+    "mmlu_professional_medicine": null,
+    "multimedqa": null,
+    "pubmedqa": null
+  },
+  "config": {
+        "model_dtype": "torch.float16", 
+        "model_name": "demo-leaderboard/gpt2-demo",
+        "model_sha": "ac3299b02780836378b9e1e68c6eead546e89f90"
+    },
+  "git_hash": "a3e56afe",
+  "pretty_env_info": "PyTorch version: 2.1.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: 14.0.0-1ubuntu1.1\nCMake version: version 3.27.9\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-6.1.58+-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.140\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: GPU 0: Tesla V100-SXM2-16GB\nNvidia driver version: 535.104.05\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture:                       x86_64\nCPU op-mode(s):                     32-bit, 64-bit\nAddress sizes:                      46 bits physical, 48 bits virtual\nByte Order:                         Little Endian\nCPU(s):                             2\nOn-line CPU(s) list:                0,1\nVendor ID:                          GenuineIntel\nModel name:                         Intel(R) Xeon(R) CPU @ 2.00GHz\nCPU family:                         6\nModel:                              85\nThread(s) per core:                 2\nCore(s) per socket:                 1\nSocket(s):                          1\nStepping:                           3\nBogoMIPS:                           4000.32\nFlags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities\nHypervisor vendor:                  KVM\nVirtualization type:                full\nL1d cache:                          32 KiB (1 instance)\nL1i cache:                          32 KiB (1 instance)\nL2 cache:                           1 MiB (1 instance)\nL3 cache:                           38.5 MiB (1 instance)\nNUMA node(s):                       1\nNUMA node0 CPU(s):                  0,1\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit:        Not affected\nVulnerability L1tf:                 Mitigation; PTE Inversion\nVulnerability Mds:                  Vulnerable; SMT Host state unknown\nVulnerability Meltdown:             Vulnerable\nVulnerability Mmio stale data:      Vulnerable\nVulnerability Retbleed:             Vulnerable\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass:    Vulnerable\nVulnerability Spectre v1:           Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers\nVulnerability Spectre v2:           Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected\nVulnerability Srbds:                Not affected\nVulnerability Tsx async abort:      Vulnerable\n\nVersions of relevant libraries:\n[pip3] numpy==1.25.2\n[pip3] torch==2.1.0+cu121\n[pip3] torchaudio==2.1.0+cu121\n[pip3] torchdata==0.7.0\n[pip3] torchsummary==1.5.1\n[pip3] torchtext==0.16.0\n[pip3] torchvision==0.16.0+cu121\n[pip3] triton==2.1.0\n[conda] Could not collect",
+  "transformers_version": "4.38.2",
+  "upper_git_hash": null
 }