{"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":"cd06b920-4f35-5aa1-b2b0-0d71f068b828","task_key":"default--v0~2e1~2e0~5fhf--cd06b920-4f35-5aa1-b2b0-0d71f068b828","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf cd06b920-4f35-5aa1-b2b0-0d71f068b828","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.linear_model import LinearRegression\\nDATA = {\\n    \\\"Area_String\\\": [\\\"1,000\\\", \\\"2,000\\\", \\\"3,000\\\", \\\"4,000\\\", \\\"5,000\\\"],\\n    \\\"Price\\\": [100, 200, 300, 400, 500],\\n}\\ndef task_func(area_string, data=DATA):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.linear_model import LinearRegression\\n\\nDATA = {\\n    \\\"Area_String\\\": [\\\"1,000\\\", \\\"2,000\\\", \\\"3,000\\\", \\\"4,000\\\", \\\"5,000\\\"],\\n    \\\"Price\\\": [100, 200, 300, 400, 500],\\n}\\n\\n\\ndef task_func(area_string, data=DATA):\\n    \\\"\\\"\\\"\\n    Predicts the price based on a given area after training a linear regression model.\\n\\n    Parameters:\\n    - area_string (str): A string representing the area (in square units) for\\n    which the price needs to be predicted. The string may contain commas.\\n    - data (dict): Optional. A dictionary with keys 'Area_String' and 'Price'\\n    representing area values (as strings) and their corresponding prices. Defaults to a predefined dataset.\\n\\n    Returns:\\n    - float: The predicted price for the given area.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn.linear_model\\n\\n    Example:\\n    >>> task_func('6,000')\\n    600.0\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Predicts the price based on a given area after training a linear regression model.\\nThe function should output with:\\n    float: The predicted price for the given area.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.linear_model import LinearRegression\\nDATA = {\\n    \\\"Area_String\\\": [\\\"1,000\\\", \\\"2,000\\\", \\\"3,000\\\", \\\"4,000\\\", \\\"5,000\\\"],\\n    \\\"Price\\\": [100, 200, 300, 400, 500],\\n}\\ndef task_func(area_string, data=DATA):\\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":[]}