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BigCodeBench / BigCodeBench v0.1.0_hf 68a53204-325c-5208-9aed-437a3ab15baa
Problem
Answer published by the source. Consult the official source to check your work against its answer.
complete prompt
from nltk.tokenize import RegexpTokenizer
from collections import Counter
def task_func(text):
"""
Identifies and counts words in a given text that start with the "
$" symbol. It returns the five most frequent
dollar-prefixed words along with their counts. Words solely consisting of "$" symbols without any following
alphanumeric characters are ignored in the frequency count.
Parameters:
- text (str): The input text to analyze.
Returns:
- list of tuples: Each tuple contains a dollar-prefixed word (excluding the "$" symbol) and its frequency,
ordered by most to least common.
Requirements:
- nltk.tokenize.RegexpTokenizer
- collections.Counter
Example:
>>> text = "$abc def hij klm $ $abc hij $hij"
>>> task_func(text)
[('abc', 3), ('hij', 3), ('efg', 1)]
"""
Plain-text mathematical notation (without MathML)
from nltk.tokenize import RegexpTokenizer
from collections import Counter
def task_func(text):
"""
Identifies and counts words in a given text that start with the "$" symbol. It returns the five most frequent
dollar-prefixed words along with their counts. Words solely consisting of "$" symbols without any following
alphanumeric characters are ignored in the frequency count.
Parameters:
- text (str): The input text to analyze.
Returns:
- list of tuples: Each tuple contains a dollar-prefixed word (excluding the "$" symbol) and its frequency,
ordered by most to least common.
Requirements:
- nltk.tokenize.RegexpTokenizer
- collections.Counter
Example:
>>> text = "$abc def efghij klm $ $abc abchij $hij"
>>> task_func(text)
[('abc', 3), ('hij', 3), ('efg', 1)]
"""
Original LaTeX notation
from nltk.tokenize import RegexpTokenizer
from collections import Counter
def task_func(text):
"""
Identifies and counts words in a given text that start with the "$" symbol. It returns the five most frequent
dollar-prefixed words along with their counts. Words solely consisting of "$" symbols without any following
alphanumeric characters are ignored in the frequency count.
Parameters:
- text (str): The input text to analyze.
Returns:
- list of tuples: Each tuple contains a dollar-prefixed word (excluding the "$" symbol) and its frequency,
ordered by most to least common.
Requirements:
- nltk.tokenize.RegexpTokenizer
- collections.Counter
Example:
>>> text = "$abc def $efg $hij klm $ $abc $abc $hij $hij"
>>> task_func(text)
[('abc', 3), ('hij', 3), ('efg', 1)]
"""
instruct prompt
Identifies and counts words in a given text that start with the "
$" symbol. It returns the five most frequent dollar-prefixed words along with their counts. Words solely consisting of "$" symbols without any following alphanumeric characters are ignored in the frequency count.
The function should output with:
list of tuples: Each tuple contains a dollar-prefixed word (excluding the "$" symbol) and its frequency,
ordered by most to least common.
You should write self-contained code starting with:
Plain-text mathematical notation (without MathML)
Identifies and counts words in a given text that start with the "$" symbol. It returns the five most frequent dollar-prefixed words along with their counts. Words solely consisting of "$" symbols without any following alphanumeric characters are ignored in the frequency count.
The function should output with:
list of tuples: Each tuple contains a dollar-prefixed word (excluding the "$" symbol) and its frequency,
ordered by most to least common.
You should write self-contained code starting with:
Original LaTeX notation
Identifies and counts words in a given text that start with the "$" symbol. It returns the five most frequent dollar-prefixed words along with their counts. Words solely consisting of "$" symbols without any following alphanumeric characters are ignored in the frequency count.
The function should output with:
list of tuples: Each tuple contains a dollar-prefixed word (excluding the "$" symbol) and its frequency,
ordered by most to least common.
You should write self-contained code starting with:
Code
from nltk.tokenize import RegexpTokenizer
from collections import Counter
def task_func(text):
code prompt
Code
from nltk.tokenize import RegexpTokenizer
from collections import Counter
def task_func(text):
entry point
task_func
libs
- nltk
- collections
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initial import