{"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":"1ffabccb-fb16-5164-896c-a023a98060e3","task_key":"default--v0~2e1~2e0~5fhf--1ffabccb-fb16-5164-896c-a023a98060e3","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 1ffabccb-fb16-5164-896c-a023a98060e3","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nfrom scipy.linalg import svd\\ndef task_func(rows=3, columns=2, seed=0):\\n\",\"complete_prompt\":\"import numpy as np\\nfrom scipy.linalg import svd\\n\\ndef task_func(rows=3, columns=2, seed=0):\\n    \\\"\\\"\\\"\\n    Generate a matrix of random values with specified dimensions and perform Singular Value Decomposition (SVD) on it.\\n\\n    Requirements:\\n    - numpy\\n    - scipy.linalg.svd\\n\\n    Parameters:\\n    - rows (int): Number of rows for the random matrix. Default is 3.\\n    - columns (int): Number of columns for the random matrix. Default is 2.\\n    - seed (int, optional): Seed for the random number generator to ensure reproducibility. Default is None.\\n\\n    Returns:\\n    tuple: A tuple containing three elements:\\n        - U (ndarray): The unitary matrix U.\\n        - s (ndarray): The singular values, sorted in descending order.\\n        - Vh (ndarray): The conjugate transpose of the unitary matrix V.\\n\\n    Example:\\n    >>> U, s, Vh = task_func(3, 2, seed=42)\\n    >>> print('U shape:', U.shape)\\n    U shape: (3, 3)\\n    >>> print('s shape:', s.shape)\\n    s shape: (2,)\\n    >>> print('Vh shape:', Vh.shape)\\n    Vh shape: (2, 2)\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate a matrix of random values with specified dimensions and perform Singular Value Decomposition (SVD) on it.\\nThe function should output with:\\n    tuple: A tuple containing three elements:\\n    U (ndarray): The unitary matrix U.\\n    s (ndarray): The singular values, sorted in descending order.\\n    Vh (ndarray): The conjugate transpose of the unitary matrix V.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nfrom scipy.linalg import svd\\ndef task_func(rows=3, columns=2, seed=0):\\n```\",\"libs\":\"['numpy', 'scipy']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}