Upload results for model CohereForAI/aya-23-8B (#851)

- Upload results for model CohereForAI/aya-23-8B (ee52f89eee5cff79c6968e5acf0ff79b9691d9bf)

data/CohereForAI/aya-23-8B/cot/24-10-02-15:57:28_idx25/CohereForAI__aya-23-8B/results_2024-10-02T17-19-57.717430.json

@@ -0,0 +1,297 @@
+{
+  "results": {
+    "numquam-repellat-4135_logiqa2_cot": {
+      "alias": "numquam-repellat-4135_logiqa2_cot",
+      "acc,none": 0.29325699745547074,
+      "acc_stderr,none": 0.01148594832162971
+    },
+    "numquam-repellat-4135_logiqa_cot": {
+      "alias": "numquam-repellat-4135_logiqa_cot",
+      "acc,none": 0.2763578274760383,
+      "acc_stderr,none": 0.017887836254561917
+    },
+    "numquam-repellat-4135_lsat-ar_cot": {
+      "alias": "numquam-repellat-4135_lsat-ar_cot",
+      "acc,none": 0.1956521739130435,
+      "acc_stderr,none": 0.0262147997098196
+    },
+    "numquam-repellat-4135_lsat-lr_cot": {
+      "alias": "numquam-repellat-4135_lsat-lr_cot",
+      "acc,none": 0.20980392156862746,
+      "acc_stderr,none": 0.018047429112476088
+    },
+    "numquam-repellat-4135_lsat-rc_cot": {
+      "alias": "numquam-repellat-4135_lsat-rc_cot",
+      "acc,none": 0.3048327137546468,
+      "acc_stderr,none": 0.02811952967561345
+    }
+  },
+  "group_subtasks": {
+    "numquam-repellat-4135_logiqa2_cot": [],
+    "numquam-repellat-4135_logiqa_cot": [],
+    "numquam-repellat-4135_lsat-ar_cot": [],
+    "numquam-repellat-4135_lsat-lr_cot": [],
+    "numquam-repellat-4135_lsat-rc_cot": []
+  },
+  "configs": {
+    "numquam-repellat-4135_logiqa2_cot": {
+      "task": "numquam-repellat-4135_logiqa2_cot",
+      "tag": "logikon-bench",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces-2.0",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "data/CohereForAI/aya-23-8B/numquam-repellat-4135-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
+      }
+    },
+    "numquam-repellat-4135_logiqa_cot": {
+      "task": "numquam-repellat-4135_logiqa_cot",
+      "tag": "logikon-bench",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces-2.0",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "data/CohereForAI/aya-23-8B/numquam-repellat-4135-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
+      }
+    },
+    "numquam-repellat-4135_lsat-ar_cot": {
+      "task": "numquam-repellat-4135_lsat-ar_cot",
+      "tag": "logikon-bench",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces-2.0",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "data/CohereForAI/aya-23-8B/numquam-repellat-4135-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
+      }
+    },
+    "numquam-repellat-4135_lsat-lr_cot": {
+      "task": "numquam-repellat-4135_lsat-lr_cot",
+      "tag": "logikon-bench",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces-2.0",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "data/CohereForAI/aya-23-8B/numquam-repellat-4135-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
+      }
+    },
+    "numquam-repellat-4135_lsat-rc_cot": {
+      "task": "numquam-repellat-4135_lsat-rc_cot",
+      "tag": "logikon-bench",
+      "group": "logikon-bench",
+      "dataset_path": "cot-leaderboard/cot-eval-traces-2.0",
+      "dataset_kwargs": {
+        "data_files": {
+          "test": "data/CohereForAI/aya-23-8B/numquam-repellat-4135-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": {
+    "numquam-repellat-4135_logiqa2_cot": 0.0,
+    "numquam-repellat-4135_logiqa_cot": 0.0,
+    "numquam-repellat-4135_lsat-ar_cot": 0.0,
+    "numquam-repellat-4135_lsat-lr_cot": 0.0,
+    "numquam-repellat-4135_lsat-rc_cot": 0.0
+  },
+  "n-shot": {
+    "numquam-repellat-4135_logiqa2_cot": 0,
+    "numquam-repellat-4135_logiqa_cot": 0,
+    "numquam-repellat-4135_lsat-ar_cot": 0,
+    "numquam-repellat-4135_lsat-lr_cot": 0,
+    "numquam-repellat-4135_lsat-rc_cot": 0
+  },
+  "higher_is_better": {
+    "numquam-repellat-4135_logiqa2_cot": {
+      "acc": true
+    },
+    "numquam-repellat-4135_logiqa_cot": {
+      "acc": true
+    },
+    "numquam-repellat-4135_lsat-ar_cot": {
+      "acc": true
+    },
+    "numquam-repellat-4135_lsat-lr_cot": {
+      "acc": true
+    },
+    "numquam-repellat-4135_lsat-rc_cot": {
+      "acc": true
+    }
+  },
+  "n-samples": {
+    "numquam-repellat-4135_lsat-rc_cot": {
+      "original": 269,
+      "effective": 269
+    },
+    "numquam-repellat-4135_lsat-lr_cot": {
+      "original": 510,
+      "effective": 510
+    },
+    "numquam-repellat-4135_lsat-ar_cot": {
+      "original": 230,
+      "effective": 230
+    },
+    "numquam-repellat-4135_logiqa_cot": {
+      "original": 626,
+      "effective": 626
+    },
+    "numquam-repellat-4135_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=CohereForAI/aya-23-8B,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": 1727881672.3049135,
+  "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: GPU 0: NVIDIA H100\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:                       3799.948\nMaximale Taktfrequenz der CPU: 3800,0000\nMinimale Taktfrequenz der CPU: 400,0000\nBogoMIPS:                      6500.40\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": [
+    "<PAD>",
+    "0"
+  ],
+  "tokenizer_eos_token": [
+    "<|END_OF_TURN_TOKEN|>",
+    "255001"
+  ],
+  "tokenizer_bos_token": [
+    "<BOS_TOKEN>",
+    "5"
+  ],
+  "eot_token_id": 255001,
+  "max_length": 2047,
+  "task_hashes": {},
+  "model_source": "local-completions",
+  "model_name": "CohereForAI/aya-23-8B",
+  "model_name_sanitized": "CohereForAI__aya-23-8B",
+  "system_instruction": null,
+  "system_instruction_sha": null,
+  "fewshot_as_multiturn": false,
+  "chat_template": null,
+  "chat_template_sha": null,
+  "start_time": 632230.794601604,
+  "end_time": 632959.599023957,
+  "total_evaluation_time_seconds": "728.8044223530451"
+}