{"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":"ae96296d-7915-575f-a859-282c5116332b","task_key":"default--v0~2e1~2e0~5fhf--ae96296d-7915-575f-a859-282c5116332b","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf ae96296d-7915-575f-a859-282c5116332b","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import re\\nimport string\\nimport nltk\\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\\nnltk.download('vader_lexicon')\\n# Constants\\nALPHANUMERIC = re.compile('[\\\\W_]+')\\nPUNCTUATIONS = string.punctuation\\ndef task_func(text: str, sia: SentimentIntensityAnalyzer) -> dict:\\n\",\"complete_prompt\":\"import re\\nimport string\\nimport nltk\\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\\n\\nnltk.download('vader_lexicon')\\n# Constants\\nALPHANUMERIC = re.compile('[\\\\W_]+')\\nPUNCTUATIONS = string.punctuation\\n\\n\\ndef task_func(text: str, sia: SentimentIntensityAnalyzer) -> dict:\\n    \\\"\\\"\\\"Analyze the sentiment of a text using the provided SentimentIntensityAnalyzer.\\n    The text is first cleaned by:\\n    - Removing all non-alphanumeric characters except spaces.\\n    - Converting to lowercase.\\n    - Removing punctuation.\\n    \\n    Parameters:\\n    text (str): The string to analyze.\\n    sia (SentimentIntensityAnalyzer): An instance of the SentimentIntensityAnalyzer for sentiment analysis.\\n    \\n    Returns:\\n    dict: A dictionary with sentiment scores. The dictionary contains four scores:\\n          - 'compound': The overall sentiment score.\\n          - 'neg': Negative sentiment score.\\n          - 'neu': Neutral sentiment score.\\n          - 'pos': Positive sentiment score.\\n    \\n    Requirements:\\n    - re\\n    - string\\n    - nltk\\n    - nltk.sentiment.vader\\n    \\n    Example:\\n    >>> from nltk.sentiment import SentimentIntensityAnalyzer\\n    >>> sia = SentimentIntensityAnalyzer()\\n    >>> task_func(\\\"I love Python!\\\", sia)\\n    {'neg': 0.0, 'neu': 0.192, 'pos': 0.808, 'compound': 0.6369}\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Analyze the sentiment of a text using the provided SentimentIntensityAnalyzer. The text is first cleaned by: - Removing all non-alphanumeric characters except spaces. - Converting to lowercase. - Removing punctuation.\\nThe function should output with:\\n    dict: A dictionary with sentiment scores. The dictionary contains four scores:\\n    'compound': The overall sentiment score.\\n    'neg': Negative sentiment score.\\n    'neu': Neutral sentiment score.\\n    'pos': Positive sentiment score.\\nYou should write self-contained code starting with:\\n```\\nimport re\\nimport string\\nimport nltk\\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\\nnltk.download('vader_lexicon')\\n# Constants\\nALPHANUMERIC = re.compile('[\\\\W_]+')\\nPUNCTUATIONS = string.punctuation\\ndef task_func(text: str, sia: SentimentIntensityAnalyzer) -> dict:\\n```\",\"libs\":\"['nltk', 'string', 're']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}