File size: 9,496 Bytes
872030d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
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"<s>\n{text}</s>\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.")
|