{"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":"b187bdcb-d56b-5d00-a6ad-399ec1d8bb33","task_key":"default--v0~2e1~2e0~5fhf--b187bdcb-d56b-5d00-a6ad-399ec1d8bb33","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf b187bdcb-d56b-5d00-a6ad-399ec1d8bb33","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nfrom sklearn.linear_model import LinearRegression\\ndef task_func(df):\\n\",\"complete_prompt\":\"import numpy as np\\nfrom sklearn.linear_model import LinearRegression\\n\\ndef task_func(df):\\n    \\\"\\\"\\\"\\n    Use a linear regression model to predict the \\\"value\\\" of \\\"feature\\\" in the given dataframe and return the coefficients and intercept.\\n\\n    Parameters:\\n    - df (pd.DataFrame): pandas DataFrame that contains columns named 'feature' and 'value'.\\n\\n    Returns:\\n    - result (dict): A dictionary with the coefficients and the intercept of the fitted linear regression model.\\n\\n    Requirements:\\n    - numpy\\n    - sklearn\\n\\n    Example:\\n    >>> import pandas as pd\\n    >>> np.random.seed(42)\\n    >>> df = pd.DataFrame({'feature': np.random.rand(100), 'value': np.random.rand(100)})\\n    >>> coefficients = task_func(df)\\n    >>> print(coefficients)\\n    {'coefficients': [[-0.03353164387961974]], 'intercept': [0.5135976564010359]}\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Use a linear regression model to predict the \\\"value\\\" of \\\"feature\\\" in the given dataframe and return the coefficients and intercept.\\nThe function should output with:\\n    result (dict): A dictionary with the coefficients and the intercept of the fitted linear regression model.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nfrom sklearn.linear_model import LinearRegression\\ndef task_func(df):\\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":[]}