{"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":"46683eb6-a998-511e-9701-48e9703a7212","task_key":"default--v0~2e1~2e0~5fhf--46683eb6-a998-511e-9701-48e9703a7212","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 46683eb6-a998-511e-9701-48e9703a7212","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from sklearn.preprocessing import MinMaxScaler\\nimport numpy as np\\ndef task_func(myList):\\n\",\"complete_prompt\":\"from sklearn.preprocessing import MinMaxScaler\\nimport numpy as np\\n\\ndef task_func(myList):\\n    \\\"\\\"\\\"\\n    Normalize a list of numeric values to the range [0, 1] using min-max scaling.\\n\\n    Parameters:\\n    - myList (list): List of numerical values to normalize.\\n\\n    Returns:\\n    - ndarray: An array of normalized values.\\n\\n    Requirements:\\n    - sklearn.preprocessing.MinMaxScaler\\n    - numpy\\n\\n    Example:\\n    >>> myList = [10, 20, 30, 40, 50]\\n    >>> task_func(myList)\\n    array([0.  , 0.25, 0.5 , 0.75, 1.  ])\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Normalize a list of numeric values to the range [0, 1] using min-max scaling.\\nThe function should output with:\\n    ndarray: An array of normalized values.\\nYou should write self-contained code starting with:\\n```\\nfrom sklearn.preprocessing import MinMaxScaler\\nimport numpy as np\\ndef task_func(myList):\\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":[]}