{"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":"179b68f4-dac4-5793-a07c-62fb8d72e251","task_key":"default--v0~2e1~2e0~5fhf--179b68f4-dac4-5793-a07c-62fb8d72e251","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 179b68f4-dac4-5793-a07c-62fb8d72e251","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport random\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(list_of_lists, seed=42):\\n\",\"complete_prompt\":\"import numpy as np\\nimport random\\nfrom sklearn.preprocessing import MinMaxScaler\\n\\n\\ndef task_func(list_of_lists, seed=42):\\n    \\\"\\\"\\\"\\n    Scale the values in a list of lists to a (0,1) range using MinMaxScaler.\\n    If any inner list is empty, the function fills it with five random integers between 0 and 100, and then scales the values.\\n    \\n    Parameters:\\n    list_of_lists (list of list of int): A list containing inner lists of integers.\\n    seed (int, Optional): Seed for random number generation. Default is 42.\\n    \\n    Returns:\\n    list of list of float: A list of lists containing scaled values between the range [0, 1].\\n    \\n    Requirements:\\n    - numpy\\n    - random\\n    - sklearn.preprocessing.MinMaxScaler\\n    \\n    Example:\\n    >>> task_func([[1, 2, 3], [], [4, 5, 6]])\\n    [[0.0, 0.5, 1.0], [0.8571428571428572, 0.1208791208791209, 0.0, 1.0, 0.3516483516483517], [0.0, 0.5, 1.0]]\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Scale the values in a list of lists to a (0,1) range using MinMaxScaler. If any inner list is empty, the function fills it with five random integers between 0 and 100, and then scales the values.\\nThe function should output with:\\n    list of list of float: A list of lists containing scaled values between the range [0, 1].\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport random\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(list_of_lists, seed=42):\\n```\",\"libs\":\"['numpy', 'random', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}