{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"mmlu-pro","formal_name":"MMLU-Pro","introduction":"MMLU-Pro rebuilds MMLU with ten answer options instead of four and removes items that no longer separate models. Questions span fourteen academic and professional subjects.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"3d3b21cb-abea-5076-aeb0-73712f880f0e","task_key":"default--test--10562","task_revision_id":"1","upstream_id":"10562","short_description":"Which PyTorch 1.8 command(s) produce $10\\times 5$ Gaussian matrix with each…","config":"default","split":"test","body":"{\"category\":\"computer science\",\"options\":\"['\\\\\\\\texttt{torch.normal(torch.ones(10,5)*5,torch.ones(5,5)*16)} ; \\\\\\\\texttt{(torch.rand(10,10) - 0.5) / 0.5}', '\\\\\\\\texttt{5 + torch.randn(10,5) * 4} ; \\\\\\\\texttt{2 * torch.rand(10,10) - 1}', '\\\\\\\\texttt{5 + torch.randn(10,5) * 16} ; \\\\\\\\texttt{torch.uniform_(10,10,low=-1,high=1)}', '\\\\\\\\texttt{torch.normal(torch.ones(10,5)*5,torch.ones(5,5)*16)} ; \\\\\\\\texttt{torch.uniform_(10,10,low=-1,high=1)}', '\\\\\\\\texttt{5 + torch.randn(10,5) * 16} ; \\\\\\\\texttt{torch.rand(10,10,low=-1,high=1)}', '\\\\\\\\texttt{5 + torch.randn(10,5) * 4} ; \\\\\\\\texttt{torch.rand(10,10,low=-1,high=1)}', '\\\\\\\\texttt{torch.normal(torch.ones(10,5)*5,torch.ones(5,5)*16)} ; \\\\\\\\texttt{2 * torch.rand(10,10) - 1}', '\\\\\\\\texttt{torch.normal(torch.ones(10,5)*5,torch.ones(5,5)*4)} ; \\\\\\\\texttt{2 * torch.rand(10,10) - 1}', '\\\\\\\\texttt{5 + torch.randn(10,5) * 4} ; \\\\\\\\texttt{(torch.rand(10,10) - 0.5) / 0.5}', '\\\\\\\\texttt{5 + torch.randn(10,5) * 16} ; \\\\\\\\texttt{(torch.rand(10,10) - 0.5) / 0.5}']\",\"question\":\"Which PyTorch 1.8 command(s) produce $10\\\\times 5$ Gaussian matrix with each entry i.i.d. sampled from $\\\\mathcal{N}(\\\\mu=5,\\\\sigma^2=16)$ and a $10\\\\times 10$ uniform matrix with each entry i.i.d. sampled from $U[-1,1)$?\",\"src\":\"ori_mmlu-machine_learning\"}","display_format":"text","language":"","answer_status":"published","assets":[],"source_url":"https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}