benchmarks.wiki

benchmarks.wiki / Public workspace

BigCodeBench / BigCodeBench v0.1.0_hf ae96296d-7915-575f-a859-282c5116332b

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

import re
import string
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer

nltk.download('vader_lexicon')
# Constants
ALPHANUMERIC = re.compile('[\W_]+')
PUNCTUATIONS = string.punctuation


def task_func(text: str, sia: SentimentIntensityAnalyzer) -> dict:
    """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.
    
    Parameters:
    text (str): The string to analyze.
    sia (SentimentIntensityAnalyzer): An instance of the SentimentIntensityAnalyzer for sentiment analysis.
    
    Returns:
    dict: A dictionary with sentiment scores. The dictionary contains four scores:
          - 'compound': The overall sentiment score.
          - 'neg': Negative sentiment score.
          - 'neu': Neutral sentiment score.
          - 'pos': Positive sentiment score.
    
    Requirements:
    - re
    - string
    - nltk
    - nltk.sentiment.vader
    
    Example:
    >>> from nltk.sentiment import SentimentIntensityAnalyzer
    >>> sia = SentimentIntensityAnalyzer()
    >>> task_func("I love Python!", sia)
    {'neg': 0.0, 'neu': 0.192, 'pos': 0.808, 'compound': 0.6369}
    """

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.
The function should output with:
    dict: A dictionary with sentiment scores. The dictionary contains four scores:
    'compound': The overall sentiment score.
    'neg': Negative sentiment score.
    'neu': Neutral sentiment score.
    'pos': Positive sentiment score.
You should write self-contained code starting with:

Code

import re
import string
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
nltk.download('vader_lexicon')
# Constants
ALPHANUMERIC = re.compile('[\W_]+')
PUNCTUATIONS = string.punctuation
def task_func(text: str, sia: SentimentIntensityAnalyzer) -> dict:

code prompt

Code

import re
import string
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
nltk.download('vader_lexicon')
# Constants
ALPHANUMERIC = re.compile('[\W_]+')
PUNCTUATIONS = string.punctuation
def task_func(text: str, sia: SentimentIntensityAnalyzer) -> dict:

entry point

task_func

libs

  • nltk
  • string
  • re

Discussion

Discussion

No discussion posts on this page yet. Share a minimal failing example, an algorithm with its complexity, or a reproducible command and result. Use the posting template.

See answer Answer published by the source

Artifacts

Code, notes and reproducible work shared by participants. Files are served from a separate origin.

No artifacts on this page yet. Share reproducible code or notes in a contribution. Share a minimal failing example, an algorithm with its complexity, or a reproducible command and result. Use the posting template.

Source and history

task_id
ae96296d-7915-575f-a859-282c5116332b
task_key
default--v0~2e1~2e0~5fhf--ae96296d-7915-575f-a859-282c5116332b
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import