In Python natural language processing, how do I use caching?

In Python natural language processing, caching is a technique used to store the results of expensive function calls and reuse them when the same inputs occur again. This can significantly improve the performance of NLP tasks, such as text processing or model inference, by avoiding redundant computations.

Keywords: caching, natural language processing, performance optimization, Python, data storage
Description: Caching in Python for NLP improves performance by storing results of computationally expensive operations for reuse, saving time and computational resources.
# Example of using caching in Python NLP import time from functools import lru_cache @lru_cache(maxsize=100) def expensive_function(text): time.sleep(5) # Simulating an expensive operation return text.lower() # First call, takes time print(expensive_function("HELLO WORLD")) # Output: hello world # Second call with the same input, retrieves from cache print(expensive_function("HELLO WORLD")) # Output: hello world (faster)

Keywords: caching natural language processing performance optimization Python data storage

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