In Python natural language processing, how do I retry transient errors?

In Python natural language processing (NLP), transient errors can occur due to network issues, API rate limits, or temporary unavailability of resources. To handle these errors gracefully, you can implement a retry mechanism using libraries like `time` for sleep intervals and `requests` for making API calls. Here's an example demonstrating this concept:

import time import requests def fetch_data_with_retries(url, retries=5, backoff_factor=0.3): for attempt in range(retries): try: response = requests.get(url) response.raise_for_status() # Raise an error for bad responses return response.json() # Return the successful result except (requests.ConnectionError, requests.Timeout) as e: if attempt < retries - 1: # Only sleep if not the last attempt time.sleep(backoff_factor * (2 ** attempt)) # Exponential backoff else: raise # Re-raise the last exception after final attempt data = fetch_data_with_retries("https://api.example.com/nlp-data")

Python NLP transient errors retry mechanism API calls

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