{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"bigcodebench","formal_name":"BigCodeBench","introduction":"BigCodeBench poses 1,140 function-level tasks drawn across 139 libraries. It tests whether a model can compose several real APIs correctly rather than write one self-contained function.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://bigcode-bench.github.io/","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":"067ee0f6-a062-554d-8eab-05da6aa215c3","task_key":"default--v0~2e1~2e0~5fhf--067ee0f6-a062-554d-8eab-05da6aa215c3","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 067ee0f6-a062-554d-8eab-05da6aa215c3","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(rows=3, columns=2, seed=42):\\n\",\"complete_prompt\":\"import numpy as np\\nfrom sklearn.preprocessing import MinMaxScaler\\n\\ndef task_func(rows=3, columns=2, seed=42):\\n    \\\"\\\"\\\"\\n    Generate a matrix of random values with specified dimensions and scale it between 0 and 1.\\n    \\n    Parameters:\\n    rows (int): The number of rows for the matrix. Default is 3.\\n    columns (int): The number of columns for the matrix. Default is 2.\\n    \\n    Returns:\\n    ndarray: A numpy ndarray with scaled values between 0 and 1.\\n    \\n    Requirements:\\n    - numpy\\n    - sklearn.preprocessing.MinMaxScaler\\n    \\n    Example:\\n    >>> task_func(3, 2)\\n    array([[0.37939383, 1.        ],\\n           [1.        , 0.55700635],\\n           [0.        , 0.        ]])\\n    \\n    >>> task_func(2, 2)\\n    array([[0., 1.],\\n           [1., 0.]])\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate a matrix of random values with specified dimensions and scale it between 0 and 1. >>> task_func(2, 2) array([[0., 1.], [1., 0.]])\\nThe function should output with:\\n    ndarray: A numpy ndarray with scaled values between 0 and 1.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(rows=3, columns=2, seed=42):\\n```\",\"libs\":\"['numpy', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}