import csv from datasets import load_dataset import os import time import signal import requests from multiprocessing import Pool, Manager from functools import partial # Define parameters score_threshold = 4 error_log_file = "error_log.txt" # List of dataset folders to process dataset_folders = [ "CC-MAIN-2013-20", "CC-MAIN-2013-48", "CC-MAIN-2014-10", "CC-MAIN-2014-15", "CC-MAIN-2014-23", "CC-MAIN-2014-35", "CC-MAIN-2014-41", "CC-MAIN-2014-42", "CC-MAIN-2014-49", "CC-MAIN-2014-52", "CC-MAIN-2015-06", "CC-MAIN-2015-11", "CC-MAIN-2015-14", "CC-MAIN-2015-18", "CC-MAIN-2015-22", "CC-MAIN-2015-27", "CC-MAIN-2015-32", "CC-MAIN-2015-35", "CC-MAIN-2015-40", "CC-MAIN-2015-48", "CC-MAIN-2016-07", "CC-MAIN-2016-18", "CC-MAIN-2016-22", "CC-MAIN-2016-26", "CC-MAIN-2016-30", "CC-MAIN-2016-36", "CC-MAIN-2016-40", "CC-MAIN-2016-44", "CC-MAIN-2016-50", "CC-MAIN-2017-04", "CC-MAIN-2017-09", "CC-MAIN-2017-13", "CC-MAIN-2017-17", "CC-MAIN-2017-22", "CC-MAIN-2017-26", "CC-MAIN-2017-30", "CC-MAIN-2017-34", "CC-MAIN-2017-39", "CC-MAIN-2017-43", "CC-MAIN-2017-47", "CC-MAIN-2017-51", "CC-MAIN-2018-05", "CC-MAIN-2018-09", "CC-MAIN-2018-13", "CC-MAIN-2018-17", "CC-MAIN-2018-22", "CC-MAIN-2018-26", "CC-MAIN-2018-30", "CC-MAIN-2018-34", "CC-MAIN-2018-39", "CC-MAIN-2018-43", "CC-MAIN-2018-47", "CC-MAIN-2018-51", "CC-MAIN-2019-04", "CC-MAIN-2019-09", "CC-MAIN-2019-13", "CC-MAIN-2019-18", "CC-MAIN-2019-22", "CC-MAIN-2019-26", "CC-MAIN-2019-30", "CC-MAIN-2019-35", "CC-MAIN-2019-39", "CC-MAIN-2019-43", "CC-MAIN-2019-47", "CC-MAIN-2019-51", "CC-MAIN-2020-05", "CC-MAIN-2020-10", "CC-MAIN-2020-16", "CC-MAIN-2020-24", "CC-MAIN-2020-29", "CC-MAIN-2020-34", "CC-MAIN-2020-40", "CC-MAIN-2020-45", "CC-MAIN-2020-50", "CC-MAIN-2021-04", "CC-MAIN-2021-10", "CC-MAIN-2021-17", "CC-MAIN-2021-21", "CC-MAIN-2021-25", "CC-MAIN-2021-31", "CC-MAIN-2021-39", "CC-MAIN-2021-43", "CC-MAIN-2021-49", "CC-MAIN-2022-05", "CC-MAIN-2022-21", "CC-MAIN-2022-27", "CC-MAIN-2022-33", "CC-MAIN-2022-40", "CC-MAIN-2022-49", "CC-MAIN-2023-06", "CC-MAIN-2023-14", "CC-MAIN-2023-23", "CC-MAIN-2023-40", "CC-MAIN-2023-50", "CC-MAIN-2024-10" ] # Global variable for interruption interrupt_flag = Manager().Value('i', False) # Function to log errors def log_error(error_message): with open(error_log_file, "a") as error_log: error_log.write(f"{error_message}\n") # Retry mechanism to handle connection errors when loading the dataset def retry_request(load_dataset_function, max_retries=5, wait_time=5): retries = 0 while retries < max_retries: try: dataset = load_dataset_function() return dataset except requests.exceptions.ConnectionError as e: log_error(f"Connection error: {e}. Retrying in {wait_time} seconds...") retries += 1 time.sleep(wait_time) except Exception as e: log_error(f"Unexpected error: {e}. Retrying in {wait_time} seconds...") retries += 1 time.sleep(wait_time) log_error("Max retries exceeded.") return None # Function to save the text column to a file with start and stop tokens def save_text_column(entry, output_text_file): try: text = entry["text"] with open(output_text_file, "a", encoding='utf-8') as f: f.write(f"\n{text}\n") except KeyError as e: log_error(f"Missing 'text' field: {e}") # Function to save score, text, and URL to a CSV file, with error handling def save_to_csv(entry, output_csv_file, write_header=False): try: with open(output_csv_file, mode='a', newline='', encoding='utf-8') as file: writer = csv.writer(file) if write_header: writer.writerow(["score", "text", "url"]) # CSV headers score = entry["score"] text = entry["text"] url = entry.get("url", "N/A") # Ensure 'url' is included writer.writerow([score, text, url]) except KeyError as e: log_error(f"Missing field in entry: {e}") # Graceful exit handling def signal_handler(sig, frame): global interrupt_flag print("Interrupt received, saving progress and exiting...") interrupt_flag.value = True # Set the flag to stop processing exit(0) signal.signal(signal.SIGINT, signal_handler) # Function to process a single folder def process_folder(folder, score_threshold): global interrupt_flag # Define per-folder log file log_file = f"processing_log_{folder}.txt" # Function to log progress to a file def log_progress(last_id): with open(log_file, "w") as log: log.write(f"{last_id}") # Function to resume from a specific point by reading the log file def resume_progress(): if os.path.exists(log_file): with open(log_file, "r") as log: last_id = log.read().strip() if last_id == 'None' or last_id == '': last_id = None return last_id return None print(f"Processing dataset folder: {folder}") # Define per-folder output files output_text_file = f"forti-sampled_text_dataset_{folder}.txt" output_csv_file = f"forti-sampled_dataset_{folder}.csv" # Load dataset with retry mechanism dataset = retry_request(lambda: load_dataset( "airtrain-ai/fineweb-edu-fortified", folder, split="train", streaming=True )) if not dataset: log_error(f"Failed to load dataset {folder}. Skipping.") return # Retrieve last processed ID for resuming last_processed_id = resume_progress() # Initialize variables found_last_id = last_processed_id is None processed_entries = 0 # Reset processed_entries for the new folder # Process entries while True: try: for entry in dataset: if interrupt_flag.value: break # Exit loop if interrupt flag is set # Skip entries until we reach the last processed ID entry_id = entry.get('id') if not found_last_id: if entry_id == last_processed_id: found_last_id = True continue # Update last_processed_id last_processed_id = entry_id # Check if entry meets the score threshold if entry.get('score', 0) >= score_threshold: # Save entry to text file and CSV # Write headers if files are new if processed_entries == 0: write_header = True else: write_header = False save_to_csv(entry, output_csv_file, write_header=write_header) save_text_column(entry, output_text_file) processed_entries += 1 if processed_entries % 100 == 0: log_progress(last_processed_id) print(f"Processed {processed_entries} entries from {folder}...") # Terminal output break # Exit while loop when dataset iteration is complete except requests.exceptions.ConnectionError as e: # Handle connection error during iteration log_error(f"Connection error during iteration in {folder}: {e}") print(f"Connection error during iteration in {folder}: {e}. Retrying in 5 seconds...") time.sleep(5) # Re-initialize dataset streaming from the point after last_processed_id dataset = retry_request(lambda: load_dataset( "airtrain-ai/fineweb-edu-fortified", folder, split="train", streaming=True )) if not dataset: log_error(f"Failed to reload dataset {folder} after connection error. Skipping.") break # Reset found_last_id to False to skip entries up to last_processed_id found_last_id = False except Exception as e: log_error(f"Error during processing in {folder}: {e}") print(f"Error during processing in {folder}: {e}. Skipping entry.") continue # After processing all entries in the folder, log progress log_progress(last_processed_id) print(f"Completed processing folder: {folder}") if interrupt_flag.value: print(f"Processing interrupted in folder: {folder}") # Main process function to process multiple folders in parallel def process_all_folders_parallel(dataset_folders, score_threshold): global interrupt_flag # Use a multiprocessing Pool to process folders in parallel with Pool(processes=os.cpu_count()) as pool: try: # Partial function to fix the score_threshold parameter func = partial(process_folder, score_threshold=score_threshold) pool.map(func, dataset_folders) except KeyboardInterrupt: print("KeyboardInterrupt received, terminating pool...") pool.terminate() pool.join() print("Pool terminated.") interrupt_flag.value = True print("Processing complete.") # Start processing all folders in parallel process_all_folders_parallel(dataset_folders, score_threshold) print("Filtered datasets saved to individual files per folder.")