{"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":"5d437f33-baca-52e4-bd1e-4d0932fbe18e","task_key":"default--v0~2e1~2e0~5fhf--5d437f33-baca-52e4-bd1e-4d0932fbe18e","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 5d437f33-baca-52e4-bd1e-4d0932fbe18e","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.linear_model import LinearRegression\\nROWS = 100\\nCOLUMNS = ['X', 'Y']\\ndef task_func(df):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.linear_model import LinearRegression\\n\\nROWS = 100\\nCOLUMNS = ['X', 'Y']\\n\\ndef task_func(df):\\n    \\\"\\\"\\\"\\n    Given a Pandas DataFrame with random numeric values and columns X & Y, use sklearn's linear regression to match the data to a linear model.\\n\\n    Parameters:\\n    - df (DataFrame): The DataFrame to use.\\n\\n    Returns:\\n    - model (LinearRegression): The fitted linear model.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn\\n\\n    Example:\\n    >>> import numpy as np\\n    >>> np.random.seed(42)\\n    >>> df = pd.DataFrame(np.random.normal(size=(100, 2)), columns=['X', 'Y'])\\n    >>> model = task_func(df)\\n    >>> print(model)\\n    LinearRegression()\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Given a Pandas DataFrame with random numeric values and columns X & Y, use sklearn's linear regression to match the data to a linear model.\\nThe function should output with:\\n    model (LinearRegression): The fitted linear model.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.linear_model import LinearRegression\\nROWS = 100\\nCOLUMNS = ['X', 'Y']\\ndef task_func(df):\\n```\",\"libs\":\"['pandas', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}