{"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":"noindex","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[]}},"task_id":"a84410f5-1634-5d71-ab59-f288f936fb25","task_key":"default--v0~2e1~2e0~5fhf--a84410f5-1634-5d71-ab59-f288f936fb25","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf a84410f5-1634-5d71-ab59-f288f936fb25","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import re\\nimport pandas as pd\\nSTOPWORDS = [\\\"Those\\\", \\\"are\\\", \\\"the\\\", \\\"words\\\", \\\"to\\\", \\\"ignore\\\"]\\ndef task_func(text):\\n\",\"complete_prompt\":\"import re\\nimport pandas as pd\\n\\nSTOPWORDS = [\\\"Those\\\", \\\"are\\\", \\\"the\\\", \\\"words\\\", \\\"to\\\", \\\"ignore\\\"]\\n\\n\\ndef task_func(text):\\n    \\\"\\\"\\\"\\n    Given a text as input, the function should split it into multiple sentences and build a dictionary where each key is associated with a sentence and the corresponding value is the number of words in the sentence. The function returns a pandas Series built from the dictionary.\\n    - The keys of the dictionary (which correspond to the Index of the pandas Series) should be named \\\"Sentence 1\\\", \\\"Sentence 2\\\" etc.\\n    - When counting the words in a sentence, do not consider those included in the constant STOPWORDS.\\n    - Do not consider empty sentences.\\n\\n    Parameters:\\n    text (str): The text to analyze.\\n\\n    Returns:\\n    pandas.core.series.Series: A pandas Series each sentence and its number of words that are not in STOPWORDS.\\n\\n    Requirements:\\n    - pandas\\n    - regex\\n\\n    Example:\\n    >>> text = \\\"This is a sample sentence. This sentence contains sample words.\\\"\\n    >>> df = task_func(\\\"I am good at programming. I learned it in college.\\\")\\n    >>> print(df)\\n    Sentence 1    5\\n    Sentence 2    5\\n    dtype: int64\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Given a text as input, the function should split it into multiple sentences and build a dictionary where each key is associated with a sentence and the corresponding value is the number of words in the sentence. The function returns a pandas Series built from the dictionary. - The keys of the dictionary (which correspond to the Index of the pandas Series) should be named \\\"Sentence 1\\\", \\\"Sentence 2\\\" etc. - When counting the words in a sentence, do not consider those included in the constant STOPWORDS. - Do not consider empty sentences.\\nThe function should output with:\\n    pandas.core.series.Series: A pandas Series each sentence and its number of words that are not in STOPWORDS.\\nYou should write self-contained code starting with:\\n```\\nimport re\\nimport pandas as pd\\nSTOPWORDS = [\\\"Those\\\", \\\"are\\\", \\\"the\\\", \\\"words\\\", \\\"to\\\", \\\"ignore\\\"]\\ndef task_func(text):\\n```\",\"libs\":\"['pandas', 're']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}