Datasets:
File size: 13,417 Bytes
23d652c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 |
#!/usr/bin/env python3
"""
Script to normalize JSONL entries for LLM fine-tuning.
Filters out entries without text and normalizes content using OpenAI-compatible API.
"""
import json
import logging
import os
import sys
from pathlib import Path
from typing import Dict, Any, Optional
import time
import argparse
try:
import openai
except ImportError:
print("Error: openai package not found. Install with: pip install openai")
sys.exit(1)
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('normalize_log.txt'),
logging.StreamHandler()
]
)
logger = logging.getLogger(__name__)
class JSONLNormalizer:
def __init__(self, api_key: str, base_url: str = None, model: str = "gpt-3.5-turbo"):
"""
Initialize the normalizer with OpenAI-compatible API settings.
Args:
api_key: API key for the service
base_url: Base URL for API (optional, defaults to OpenAI)
model: Model name to use for normalization
"""
self.client = openai.OpenAI(
api_key=api_key,
base_url=base_url
)
self.model = model
self.processed_count = 0
self.skipped_count = 0
self.failed_count = 0
self.already_normalized_count = 0
def normalize_text(self, text: str, title: str = "", subtitle: str = "") -> Optional[str]:
"""
Normalize text content using the API.
Args:
text: Main text content to normalize
title: Article title for context
subtitle: Article subtitle for context
Returns:
Normalized text or None if normalization fails
"""
try:
system_prompt = """You are an expert text editor helping to prepare content for LLM fine-tuning.
Your task is to normalize and clean text while preserving its meaning and literary quality. Make these improvements:
1. Fix obvious typos and spelling errors
2. Normalize punctuation and spacing inconsistencies
3. Remove excessive whitespace and newlines (but preserve intentional line breaks for poetry/paragraphs)
4. Ensure proper capitalization
5. Fix encoding issues or strange characters
6. Maintain the original style and voice
7. Preserve intentional formatting (like poetry line breaks)
8. Remove any metadata or non-content text
Return ONLY the cleaned text, nothing else."""
user_prompt = f"""Title: {title}
Subtitle: {subtitle}
Text to normalize:
{text}"""
response = self.client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
temperature=0.1,
max_tokens=4000
)
normalized_text = response.choices[0].message.content.strip()
return normalized_text
except Exception as e:
logger.error(f"API normalization failed: {str(e)}")
return None
def is_valid_entry(self, entry: Dict[Any, Any]) -> bool:
"""
Check if entry has valid text content.
Args:
entry: JSONL entry dictionary
Returns:
True if entry has non-empty text field
"""
text = entry.get('text', '')
return isinstance(text, str) and text.strip() != ''
def is_already_normalized(self, entry: Dict[Any, Any]) -> bool:
"""
Check if entry has already been normalized.
Args:
entry: JSONL entry dictionary
Returns:
True if entry has already been normalized
"""
return entry.get('_normalized', False) or entry.get('_normalization_failed', False)
def process_jsonl(self, input_file: str, output_file: str, failed_file: str,
max_entries: Optional[int] = None, delay: float = 0.5,
force_reprocess: bool = False, append: bool = False):
"""
Process the JSONL file and normalize entries.
Args:
input_file: Path to input JSONL file
output_file: Path to output normalized JSONL file
failed_file: Path to file for failed normalizations
max_entries: Maximum number of entries to process (for testing)
delay: Delay between API calls to avoid rate limits
force_reprocess: If True, reprocess already normalized entries
append: If True, append to existing output files instead of overwriting them
"""
logger.info(f"Starting normalization of {input_file}")
logger.info(f"Output file: {output_file} (mode: {'append' if append else 'overwrite'})")
logger.info(f"Failed entries file: {failed_file} (mode: {'append' if append else 'overwrite'})")
if force_reprocess:
logger.info("Force reprocess enabled - will reprocess already normalized entries")
# Determine file modes based on append flag
output_mode = 'a' if append else 'w'
failed_mode = 'a' if append else 'w'
with open(input_file, 'r', encoding='utf-8') as infile, \
open(output_file, output_mode, encoding='utf-8') as outfile, \
open(failed_file, failed_mode, encoding='utf-8') as failfile:
for line_num, line in enumerate(infile, 1):
try:
# Parse JSON line
entry = json.loads(line.strip())
# Skip entries without valid text
if not self.is_valid_entry(entry):
logger.debug(f"Line {line_num}: Skipping entry without text")
self.skipped_count += 1
continue
# Check if already normalized or failed (unless forcing reprocess)
if not force_reprocess and self.is_already_normalized(entry):
title = entry.get('title', '')
logger.debug(f"Line {line_num}: Entry '{title[:50]}...' already processed")
# Write to appropriate file based on previous result
if entry.get('_normalized', False):
outfile.write(json.dumps(entry, ensure_ascii=False) + '\n')
elif entry.get('_normalization_failed', False):
failfile.write(json.dumps(entry, ensure_ascii=False) + '\n')
self.already_normalized_count += 1
# Check max_entries limit after counting already normalized entries
if max_entries and (self.processed_count + self.already_normalized_count) >= max_entries:
logger.info(f"Reached maximum entries limit: {max_entries}")
break
continue
# Check max_entries limit before processing new entries
if max_entries and (self.processed_count + self.already_normalized_count) >= max_entries:
logger.info(f"Reached maximum entries limit: {max_entries}")
break
# Extract content for normalization
original_text = entry['text']
title = entry.get('title', '')
subtitle = entry.get('subtitle', '')
logger.info(f"Line {line_num}: Normalizing entry '{title[:50]}...'")
# Normalize the text
normalized_text = self.normalize_text(original_text, title, subtitle)
if normalized_text:
# Update entry with normalized text
entry['text'] = normalized_text
entry['_original_length'] = len(original_text)
entry['_normalized_length'] = len(normalized_text)
entry['_normalized'] = True
# Write to output file
outfile.write(json.dumps(entry, ensure_ascii=False) + '\n')
self.processed_count += 1
logger.info(f"Line {line_num}: Successfully normalized")
else:
# Write failed entry to failed file
entry['_normalization_failed'] = True
failfile.write(json.dumps(entry, ensure_ascii=False) + '\n')
self.failed_count += 1
logger.warning(f"Line {line_num}: Normalization failed")
# Rate limiting delay (only for new API calls)
if delay > 0:
time.sleep(delay)
except json.JSONDecodeError as e:
logger.error(f"Line {line_num}: JSON decode error: {str(e)}")
self.failed_count += 1
except Exception as e:
logger.error(f"Line {line_num}: Unexpected error: {str(e)}")
self.failed_count += 1
# Progress update
if line_num % 10 == 0:
total_processed = self.processed_count + self.already_normalized_count
logger.info(f"Progress: Processed {line_num} lines, "
f"Total processed: {total_processed}, "
f"Newly normalized: {self.processed_count}, "
f"Already processed: {self.already_normalized_count}, "
f"Skipped: {self.skipped_count}, "
f"Failed: {self.failed_count}")
# Final summary
total_processed = self.processed_count + self.already_normalized_count
logger.info("=" * 50)
logger.info("NORMALIZATION COMPLETE")
logger.info(f"Total lines processed: {line_num}")
logger.info(f"Total entries processed: {total_processed}")
logger.info(f"Newly normalized: {self.processed_count}")
logger.info(f"Already processed (skipped): {self.already_normalized_count}")
logger.info(f"Skipped (no text): {self.skipped_count}")
logger.info(f"Failed: {self.failed_count}")
logger.info("=" * 50)
def main():
parser = argparse.ArgumentParser(description='Normalize JSONL entries for LLM fine-tuning')
parser.add_argument('input_file', help='Input JSONL file path')
parser.add_argument('-o', '--output', default='normalized_entries.jsonl',
help='Output file for normalized entries (default: normalized_entries.jsonl)')
parser.add_argument('-f', '--failed', default='failed_normalizations.jsonl',
help='Output file for failed entries (default: failed_normalizations.jsonl)')
parser.add_argument('-k', '--api-key', help='OpenAI API key (or set OPENAI_API_KEY env var)')
parser.add_argument('-u', '--base-url', help='Base URL for OpenAI-compatible API')
parser.add_argument('-m', '--model', default='gpt-3.5-turbo',
help='Model to use (default: gpt-3.5-turbo)')
parser.add_argument('--max-entries', type=int, help='Maximum entries to process (for testing)')
parser.add_argument('--delay', type=float, default=0.5,
help='Delay between API calls in seconds (default: 0.5)')
parser.add_argument('--force-reprocess', action='store_true',
help='Force reprocessing of already normalized entries')
parser.add_argument('--append', action='store_true',
help='Append to existing output files instead of overwriting them')
args = parser.parse_args()
# Get API key
api_key = args.api_key or os.getenv('OPENAI_API_KEY')
if not api_key:
logger.error("API key required. Use --api-key or set OPENAI_API_KEY environment variable")
sys.exit(1)
# Check input file exists
if not Path(args.input_file).exists():
logger.error(f"Input file not found: {args.input_file}")
sys.exit(1)
# Initialize normalizer
normalizer = JSONLNormalizer(
api_key=api_key,
base_url=args.base_url,
model=args.model
)
# Process the file
try:
normalizer.process_jsonl(
input_file=args.input_file,
output_file=args.output,
failed_file=args.failed,
max_entries=args.max_entries,
delay=args.delay,
force_reprocess=args.force_reprocess,
append=args.append
)
except KeyboardInterrupt:
logger.info("Process interrupted by user")
except Exception as e:
logger.error(f"Process failed: {str(e)}")
sys.exit(1)
if __name__ == "__main__":
main() |