Delete data/mistralai/Mistral-Nemo-Instruct-2407

data/mistralai/Mistral-Nemo-Instruct-2407/cot/24-10-02-16:36:00_idx5/mistralai__Mistral-Nemo-Instruct-2407/results_2024-10-02T17-35-51.419113.json

@@ -1,297 +0,0 @@
-{
-  "results": {
-    "nihil-aut-6602_logiqa2_cot": {
-      "alias": "nihil-aut-6602_logiqa2_cot",
-      "acc,none": 0.45229007633587787,
-      "acc_stderr,none": 0.012557284732874588
-    },
-    "nihil-aut-6602_logiqa_cot": {
-      "alias": "nihil-aut-6602_logiqa_cot",
-      "acc,none": 0.3242811501597444,
-      "acc_stderr,none": 0.01872422541247857
-    },
-    "nihil-aut-6602_lsat-ar_cot": {
-      "alias": "nihil-aut-6602_lsat-ar_cot",
-      "acc,none": 0.22608695652173913,
-      "acc_stderr,none": 0.027641785707241327
-    },
-    "nihil-aut-6602_lsat-lr_cot": {
-      "alias": "nihil-aut-6602_lsat-lr_cot",
-      "acc,none": 0.4470588235294118,
-      "acc_stderr,none": 0.022037529689177736
-    },
-    "nihil-aut-6602_lsat-rc_cot": {
-      "alias": "nihil-aut-6602_lsat-rc_cot",
-      "acc,none": 0.5241635687732342,
-      "acc_stderr,none": 0.03050667421128308
-    }
-  },
-  "group_subtasks": {
-    "nihil-aut-6602_logiqa2_cot": [],
-    "nihil-aut-6602_logiqa_cot": [],
-    "nihil-aut-6602_lsat-ar_cot": [],
-    "nihil-aut-6602_lsat-lr_cot": [],
-    "nihil-aut-6602_lsat-rc_cot": []
-  },
-  "configs": {
-    "nihil-aut-6602_logiqa2_cot": {
-      "task": "nihil-aut-6602_logiqa2_cot",
-      "tag": "logikon-bench",
-      "group": "logikon-bench",
-      "dataset_path": "cot-leaderboard/cot-eval-traces-2.0",
-      "dataset_kwargs": {
-        "data_files": {
-          "test": "data/mistralai/Mistral-Nemo-Instruct-2407/nihil-aut-6602-logiqa2.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
-      }
-    },
-    "nihil-aut-6602_logiqa_cot": {
-      "task": "nihil-aut-6602_logiqa_cot",
-      "tag": "logikon-bench",
-      "group": "logikon-bench",
-      "dataset_path": "cot-leaderboard/cot-eval-traces-2.0",
-      "dataset_kwargs": {
-        "data_files": {
-          "test": "data/mistralai/Mistral-Nemo-Instruct-2407/nihil-aut-6602-logiqa.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
-      }
-    },
-    "nihil-aut-6602_lsat-ar_cot": {
-      "task": "nihil-aut-6602_lsat-ar_cot",
-      "tag": "logikon-bench",
-      "group": "logikon-bench",
-      "dataset_path": "cot-leaderboard/cot-eval-traces-2.0",
-      "dataset_kwargs": {
-        "data_files": {
-          "test": "data/mistralai/Mistral-Nemo-Instruct-2407/nihil-aut-6602-lsat-ar.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
-      }
-    },
-    "nihil-aut-6602_lsat-lr_cot": {
-      "task": "nihil-aut-6602_lsat-lr_cot",
-      "tag": "logikon-bench",
-      "group": "logikon-bench",
-      "dataset_path": "cot-leaderboard/cot-eval-traces-2.0",
-      "dataset_kwargs": {
-        "data_files": {
-          "test": "data/mistralai/Mistral-Nemo-Instruct-2407/nihil-aut-6602-lsat-lr.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
-      }
-    },
-    "nihil-aut-6602_lsat-rc_cot": {
-      "task": "nihil-aut-6602_lsat-rc_cot",
-      "tag": "logikon-bench",
-      "group": "logikon-bench",
-      "dataset_path": "cot-leaderboard/cot-eval-traces-2.0",
-      "dataset_kwargs": {
-        "data_files": {
-          "test": "data/mistralai/Mistral-Nemo-Instruct-2407/nihil-aut-6602-lsat-rc.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": {
-    "nihil-aut-6602_logiqa2_cot": 0.0,
-    "nihil-aut-6602_logiqa_cot": 0.0,
-    "nihil-aut-6602_lsat-ar_cot": 0.0,
-    "nihil-aut-6602_lsat-lr_cot": 0.0,
-    "nihil-aut-6602_lsat-rc_cot": 0.0
-  },
-  "n-shot": {
-    "nihil-aut-6602_logiqa2_cot": 0,
-    "nihil-aut-6602_logiqa_cot": 0,
-    "nihil-aut-6602_lsat-ar_cot": 0,
-    "nihil-aut-6602_lsat-lr_cot": 0,
-    "nihil-aut-6602_lsat-rc_cot": 0
-  },
-  "higher_is_better": {
-    "nihil-aut-6602_logiqa2_cot": {
-      "acc": true
-    },
-    "nihil-aut-6602_logiqa_cot": {
-      "acc": true
-    },
-    "nihil-aut-6602_lsat-ar_cot": {
-      "acc": true
-    },
-    "nihil-aut-6602_lsat-lr_cot": {
-      "acc": true
-    },
-    "nihil-aut-6602_lsat-rc_cot": {
-      "acc": true
-    }
-  },
-  "n-samples": {
-    "nihil-aut-6602_lsat-rc_cot": {
-      "original": 269,
-      "effective": 269
-    },
-    "nihil-aut-6602_lsat-lr_cot": {
-      "original": 510,
-      "effective": 510
-    },
-    "nihil-aut-6602_lsat-ar_cot": {
-      "original": 230,
-      "effective": 230
-    },
-    "nihil-aut-6602_logiqa_cot": {
-      "original": 626,
-      "effective": 626
-    },
-    "nihil-aut-6602_logiqa2_cot": {
-      "original": 1572,
-      "effective": 1572
-    }
-  },
-  "config": {
-    "model": "local-completions",
-    "model_args": "base_url=http://localhost:8080/v1/completions,num_concurrent=1,max_retries=3,tokenized_requests=False,model=mistralai/Mistral-Nemo-Instruct-2407,trust_remote_code=True",
-    "batch_size": "1",
-    "batch_sizes": [],
-    "device": null,
-    "use_cache": null,
-    "limit": null,
-    "bootstrap_iters": 100000,
-    "gen_kwargs": null,
-    "random_seed": 0,
-    "numpy_seed": 1234,
-    "torch_seed": 1234,
-    "fewshot_seed": 1234
-  },
-  "git_hash": "f2eb17f",
-  "date": 1727882300.8684413,
-  "pretty_env_info": "PyTorch version: 2.4.1+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Red Hat Enterprise Linux release 8.8 (Ootpa) (x86_64)\nGCC version: (GCC) 8.5.0 20210514 (Red Hat 8.5.0-18)\nClang version: Could not collect\nCMake version: version 3.20.2\nLibc version: glibc-2.28\n\nPython version: 3.11.2 (main, May 20 2024, 08:58:58) [GCC 8.5.0 20210514 (Red Hat 8.5.0-18)] (64-bit runtime)\nPython platform: Linux-4.18.0-477.70.1.el8_8.x86_64-x86_64-with-glibc2.28\nIs CUDA available: True\nCUDA runtime version: 12.2.140\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H100\nGPU 1: NVIDIA H100\n\nNvidia driver version: 550.54.15\ncuDNN version: Probably one of the following:\n/hkfs/home/software/all/devel/cuda/11.2/targets/x86_64-linux/lib/libcudnn.so.8.1.1\n/hkfs/home/software/all/devel/cuda/11.2/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.1.1\n/hkfs/home/software/all/devel/cuda/11.2/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.1.1\n/hkfs/home/software/all/devel/cuda/11.2/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.1.1\n/hkfs/home/software/all/devel/cuda/11.2/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.1.1\n/hkfs/home/software/all/devel/cuda/11.2/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.1.1\n/hkfs/home/software/all/devel/cuda/11.2/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.1.1\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitektur:                   x86_64\nCPU Operationsmodus:           32-bit, 64-bit\nByte-Reihenfolge:              Little Endian\nCPU(s):                        128\nListe der Online-CPU(s):       0-127\nThread(s) pro Kern:            2\nKern(e) pro Socket:            32\nSockel:                        2\nNUMA-Knoten:                   2\nAnbieterkennung:               AuthenticAMD\nProzessorfamilie:              25\nModell:                        17\nModellname:                    AMD EPYC 9354 32-Core Processor\nStepping:                      1\nCPU MHz:                       3800.000\nMaximale Taktfrequenz der CPU: 3800,0000\nMinimale Taktfrequenz der CPU: 400,0000\nBogoMIPS:                      6499.55\nVirtualisierung:               AMD-V\nL1d Cache:                     32K\nL1i Cache:                     32K\nL2 Cache:                      1024K\nL3 Cache:                      32768K\nNUMA-Knoten0 CPU(s):           0-31,64-95\nNUMA-Knoten1 CPU(s):           32-63,96-127\nMarkierungen:                  fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] torch==2.4.1\n[pip3] triton==3.0.0\n[conda] Could not collect",
-  "transformers_version": "4.45.1",
-  "upper_git_hash": null,
-  "tokenizer_pad_token": [
-    "<unk>",
-    "0"
-  ],
-  "tokenizer_eos_token": [
-    "</s>",
-    "2"
-  ],
-  "tokenizer_bos_token": [
-    "<s>",
-    "1"
-  ],
-  "eot_token_id": 2,
-  "max_length": 2047,
-  "task_hashes": {},
-  "model_source": "local-completions",
-  "model_name": "mistralai/Mistral-Nemo-Instruct-2407",
-  "model_name_sanitized": "mistralai__Mistral-Nemo-Instruct-2407",
-  "system_instruction": null,
-  "system_instruction_sha": null,
-  "fewshot_as_multiturn": false,
-  "chat_template": null,
-  "chat_template_sha": null,
-  "start_time": 697031.219934425,
-  "end_time": 698085.214124437,
-  "total_evaluation_time_seconds": "1053.9941900119884"
-}