{"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":"c2c54699-d1c3-5e08-94cd-0156d155e2ea","task_key":"default--v0~2e1~2e0~5fhf--c2c54699-d1c3-5e08-94cd-0156d155e2ea","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf c2c54699-d1c3-5e08-94cd-0156d155e2ea","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import re\\nfrom sklearn.feature_extraction.text import CountVectorizer\\nimport numpy as np\\n# Constants\\nSENTENCES = ['This is a sentence', 'Another sentence here', 'More sentences']\\ndef task_func(s: str) -> np.ndarray:\\n\",\"complete_prompt\":\"import re\\nfrom sklearn.feature_extraction.text import CountVectorizer\\nimport numpy as np\\n\\n# Constants\\nSENTENCES = ['This is a sentence', 'Another sentence here', 'More sentences']\\n\\ndef task_func(s: str) -> np.ndarray:\\n    \\\"\\\"\\\"\\n    Vectorize a string using the Bag-of-Words model. The string is split into words and each word is treated as an attribute. The value of each attribute is the number of occurrences of the word in the string. The function also uses some predefined sentences (SENTENCES constant) for vectorization.\\n\\n    Parameters:\\n    - s (str): The string to vectorize.\\n\\n    Returns:\\n    - np.ndarray: A numpy array with the vectorized string.\\n\\n    Requirements:\\n    - re\\n    - sklearn.feature_extraction.text.CountVectorizer\\n    - numpy\\n\\n    Example:\\n    >>> s = 'This is a test string.'\\n    >>> vec = task_func(s)\\n    >>> print(vec)\\n    [0 0 1 0 0 0 1 1 1]\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Vectorize a string using the Bag-of-Words model. The string is split into words and each word is treated as an attribute. The value of each attribute is the number of occurrences of the word in the string. The function also uses some predefined sentences (SENTENCES constant) for vectorization.\\nThe function should output with:\\n    np.ndarray: A numpy array with the vectorized string.\\nYou should write self-contained code starting with:\\n```\\nimport re\\nfrom sklearn.feature_extraction.text import CountVectorizer\\nimport numpy as np\\n# Constants\\nSENTENCES = ['This is a sentence', 'Another sentence here', 'More sentences']\\ndef task_func(s: str) -> np.ndarray:\\n```\",\"libs\":\"['numpy', 're', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}