{
"results": {
"minerva_math": {
"exact_match,none": 0.191,
"exact_match_stderr,none": 0.005425238616812189,
"alias": "minerva_math"
},
"minerva_math_algebra": {
"alias": " - minerva_math_algebra",
"exact_match,none": 0.2679022746419545,
"exact_match_stderr,none": 0.012859686603136161
},
"minerva_math_counting_and_prob": {
"alias": " - minerva_math_counting_and_prob",
"exact_match,none": 0.18354430379746836,
"exact_match_stderr,none": 0.01779943417521061
},
"minerva_math_geometry": {
"alias": " - minerva_math_geometry",
"exact_match,none": 0.13987473903966596,
"exact_match_stderr,none": 0.015864871092013833
},
"minerva_math_intermediate_algebra": {
"alias": " - minerva_math_intermediate_algebra",
"exact_match,none": 0.09080841638981174,
"exact_match_stderr,none": 0.009567257998644276
},
"minerva_math_num_theory": {
"alias": " - minerva_math_num_theory",
"exact_match,none": 0.15,
"exact_match_stderr,none": 0.015380154912112986
},
"minerva_math_prealgebra": {
"alias": " - minerva_math_prealgebra",
"exact_match,none": 0.3145809414466131,
"exact_match_stderr,none": 0.015742897421514867
},
"minerva_math_precalc": {
"alias": " - minerva_math_precalc",
"exact_match,none": 0.08424908424908426,
"exact_match_stderr,none": 0.011897974236045666
}
},
"groups": {
"minerva_math": {
"exact_match,none": 0.191,
"exact_match_stderr,none": 0.005425238616812189,
"alias": "minerva_math"
}
},
"group_subtasks": {
"minerva_math": [
"minerva_math_algebra",
"minerva_math_counting_and_prob",
"minerva_math_geometry",
"minerva_math_intermediate_algebra",
"minerva_math_num_theory",
"minerva_math_prealgebra",
"minerva_math_precalc"
]
},
"configs": {
"minerva_math_algebra": {
"task": "minerva_math_algebra",
"tag": [
"math_word_problems"
],
"group": [
"math_word_problems"
],
"dataset_path": "EleutherAI/hendrycks_math",
"dataset_name": "algebra",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
"doc_to_target": "{{answer if few_shot is undefined else solution}}",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"samples": "<function list_fewshot_samples at 0x151adcecf760>"
},
"num_fewshot": 4,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"Problem:"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"minerva_math_counting_and_prob": {
"task": "minerva_math_counting_and_prob",
"tag": [
"math_word_problems"
],
"group": [
"math_word_problems"
],
"dataset_path": "EleutherAI/hendrycks_math",
"dataset_name": "counting_and_probability",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
"doc_to_target": "{{answer if few_shot is undefined else solution}}",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"samples": "<function list_fewshot_samples at 0x151adcecd750>"
},
"num_fewshot": 4,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"Problem:"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"minerva_math_geometry": {
"task": "minerva_math_geometry",
"tag": [
"math_word_problems"
],
"group": [
"math_word_problems"
],
"dataset_path": "EleutherAI/hendrycks_math",
"dataset_name": "geometry",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
"doc_to_target": "{{answer if few_shot is undefined else solution}}",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"samples": "<function list_fewshot_samples at 0x151adcebdb40>"
},
"num_fewshot": 4,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"Problem:"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"minerva_math_intermediate_algebra": {
"task": "minerva_math_intermediate_algebra",
"tag": [
"math_word_problems"
],
"group": [
"math_word_problems"
],
"dataset_path": "EleutherAI/hendrycks_math",
"dataset_name": "intermediate_algebra",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
"doc_to_target": "{{answer if few_shot is undefined else solution}}",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"samples": "<function list_fewshot_samples at 0x151adcebca60>"
},
"num_fewshot": 4,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"Problem:"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"minerva_math_num_theory": {
"task": "minerva_math_num_theory",
"tag": [
"math_word_problems"
],
"group": [
"math_word_problems"
],
"dataset_path": "EleutherAI/hendrycks_math",
"dataset_name": "number_theory",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
"doc_to_target": "{{answer if few_shot is undefined else solution}}",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"samples": "<function list_fewshot_samples at 0x151ade1a5b40>"
},
"num_fewshot": 4,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"Problem:"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"minerva_math_prealgebra": {
"task": "minerva_math_prealgebra",
"tag": [
"math_word_problems"
],
"group": [
"math_word_problems"
],
"dataset_path": "EleutherAI/hendrycks_math",
"dataset_name": "prealgebra",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
"doc_to_target": "{{answer if few_shot is undefined else solution}}",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"samples": "<function list_fewshot_samples at 0x151ade1a4c10>"
},
"num_fewshot": 4,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"Problem:"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"minerva_math_precalc": {
"task": "minerva_math_precalc",
"tag": [
"math_word_problems"
],
"group": [
"math_word_problems"
],
"dataset_path": "EleutherAI/hendrycks_math",
"dataset_name": "precalculus",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
"doc_to_target": "{{answer if few_shot is undefined else solution}}",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"samples": "<function list_fewshot_samples at 0x151ade32b370>"
},
"num_fewshot": 4,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"Problem:"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
}
},
"versions": {
"minerva_math": 1.0,
"minerva_math_algebra": 1.0,
"minerva_math_counting_and_prob": 1.0,
"minerva_math_geometry": 1.0,
"minerva_math_intermediate_algebra": 1.0,
"minerva_math_num_theory": 1.0,
"minerva_math_prealgebra": 1.0,
"minerva_math_precalc": 1.0
},
"n-shot": {
"minerva_math_algebra": 4,
"minerva_math_counting_and_prob": 4,
"minerva_math_geometry": 4,
"minerva_math_intermediate_algebra": 4,
"minerva_math_num_theory": 4,
"minerva_math_prealgebra": 4,
"minerva_math_precalc": 4
},
"higher_is_better": {
"minerva_math": {
"exact_match": true
},
"minerva_math_algebra": {
"exact_match": true
},
"minerva_math_counting_and_prob": {
"exact_match": true
},
"minerva_math_geometry": {
"exact_match": true
},
"minerva_math_intermediate_algebra": {
"exact_match": true
},
"minerva_math_num_theory": {
"exact_match": true
},
"minerva_math_prealgebra": {
"exact_match": true
},
"minerva_math_precalc": {
"exact_match": true
}
},
"n-samples": {
"minerva_math_algebra": {
"original": 1187,
"effective": 1187
},
"minerva_math_counting_and_prob": {
"original": 474,
"effective": 474
},
"minerva_math_geometry": {
"original": 479,
"effective": 479
},
"minerva_math_intermediate_algebra": {
"original": 903,
"effective": 903
},
"minerva_math_num_theory": {
"original": 540,
"effective": 540
},
"minerva_math_prealgebra": {
"original": 871,
"effective": 871
},
"minerva_math_precalc": {
"original": 546,
"effective": 546
}
},
"config": {
"model": "vllm",
"model_args": "pretrained=inceptionai/jais-family-13b-chat,tensor_parallel_size=1,data_parallel_size=2,gpu_memory_utilization=0.9,download_dir=/tmp,enforce_eager=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": "788a3672",
"date": 1737537267.1351902,
"pretty_env_info": "PyTorch version: 2.4.0+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: Could not collect\nCMake version: version 3.27.1\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-1064-azure-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.128\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100 80GB PCIe\nGPU 1: NVIDIA A100 80GB PCIe\n\nNvidia driver version: 535.161.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\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: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 48\nOn-line CPU(s) list: 0-47\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V13 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 1\nStepping: 1\nBogoMIPS: 4890.86\nFlags: 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 tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 24 MiB (48 instances)\nL3 cache: 192 MiB (6 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.0.0.dev0\n[pip3] torchaudio==2.1.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchmetrics==1.2.0\n[pip3] torchvision==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
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"tokenizer_pad_token": [
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"tokenizer_eos_token": [
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"tokenizer_bos_token": [
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"eot_token_id": 0,
"max_length": 2048,
"task_hashes": {},
"model_source": "vllm",
"model_name": "inceptionai/jais-family-13b-chat",
"model_name_sanitized": "inceptionai__jais-family-13b-chat",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 15079.535210181,
"end_time": 15875.649049077,
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}