{"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":"8dd4af8d-79bc-527b-8cc0-f6b4a9f57887","task_key":"default--v0~2e1~2e0~5fhf--8dd4af8d-79bc-527b-8cc0-f6b4a9f57887","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 8dd4af8d-79bc-527b-8cc0-f6b4a9f57887","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import re\\nfrom nltk.corpus import stopwords\\ndef task_func(text: str) -> dict:\\n\",\"complete_prompt\":\"import re\\nfrom nltk.corpus import stopwords\\n\\n\\ndef task_func(text: str) -> dict:\\n    \\\"\\\"\\\"\\n    Analyzes a given text string by removing duplicate words and stopwords defined by nltk.corpus ,\\n    and then returns a frequency distribution of the remaining words.\\n\\n    Parameters:\\n    - text (str): The text string to analyze.\\n\\n    Returns:\\n    - dict: The frequency distribution of the words in the text after filtering.\\n\\n    Requirements:\\n    - re\\n    - nltk.corpus\\n\\n    Note:\\n    - A manually defined set of common English stopwords is used for filtering.\\n\\n    Examples:\\n    >>> task_func(\\\"The quick brown fox jumps over the lazy dog and the dog was not that quick to respond.\\\")\\n    {'quick': 1, 'brown': 1, 'fox': 1, 'jumps': 1, 'lazy': 1, 'dog': 1, 'respond': 1}\\n\\n    >>> task_func(\\\"hello hello world\\\")\\n    {'hello': 1, 'world': 1}\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Analyzes a given text string by removing duplicate words and stopwords defined by nltk.corpus , and then returns a frequency distribution of the remaining words. >>> task_func(\\\"hello hello world\\\") {'hello': 1, 'world': 1}\\nNote that: A manually defined set of common English stopwords is used for filtering.\\nThe function should output with:\\n    dict: The frequency distribution of the words in the text after filtering.\\nYou should write self-contained code starting with:\\n```\\nimport re\\nfrom nltk.corpus import stopwords\\ndef task_func(text: str) -> dict:\\n```\",\"libs\":\"['nltk', 're']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}