{"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":"36d2ef90-3024-566d-bd00-2ccf5e06667c","task_key":"default--v0~2e1~2e0~5fhf--36d2ef90-3024-566d-bd00-2ccf5e06667c","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 36d2ef90-3024-566d-bd00-2ccf5e06667c","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport random\\nfrom difflib import SequenceMatcher\\ndef task_func(s, min_length, max_length, letters):\\n\",\"complete_prompt\":\"import numpy as np\\nimport random\\nfrom difflib import SequenceMatcher\\n\\ndef task_func(s, min_length, max_length, letters):\\n    \\\"\\\"\\\"\\n    Generates a random string of length between `min_length` and `max_length`, inclusive,\\n    using characters from `letters`, and evaluates its similarity to the provided string `s`.\\n    A similarity score of 0.5 or higher considered 'similar'.\\n\\n    Parameters:\\n    s (str): The string to which the generated string's similarity is evaluated.\\n    min_length (int): The minimum length for the generated string.\\n    max_length (int): The maximum length for the generated string.\\n    letters (str): A string of characters from which the random string is generated.\\n\\n    Returns:\\n    tuple: A tuple containing the generated string and a boolean indicating whether it's\\n           considered similar to `s` based on the similarity threshold.\\n           \\n    Requirements:\\n    - numpy\\n    - random\\n    - difflib.SequenceMatcher\\n\\n    Examples:\\n    >>> s = 'apple'\\n    >>> min_length = 5\\n    >>> max_length = 10\\n    >>> letters = 'abcdefghijklmnopqrstuvwxyz'\\n    >>> generated_s, is_similar = task_func(s, min_length, max_length, letters)\\n    >>> len(generated_s) >= min_length and len(generated_s) <= max_length\\n    True\\n    >>> isinstance(is_similar, bool)\\n    True\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generates a random string of length between `min_length` and `max_length`, inclusive, using characters from `letters`, and evaluates its similarity to the provided string `s`. A similarity score of 0.5 or higher considered 'similar'.\\nThe function should output with:\\n    tuple: A tuple containing the generated string and a boolean indicating whether it's\\n    considered similar to `s` based on the similarity threshold.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport random\\nfrom difflib import SequenceMatcher\\ndef task_func(s, min_length, max_length, letters):\\n```\",\"libs\":\"['difflib', 'numpy', 'random']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}