File size: 12,687 Bytes
91ac3cc | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 | from __future__ import annotations
import argparse
import json
import sqlite3
from collections import Counter
from collections import deque
from concurrent.futures import ProcessPoolExecutor
from collections.abc import Iterable, Iterator
from dataclasses import asdict
from pathlib import Path
from typing import Any
from tqdm import tqdm
from kawk50m.cli import configure_utf8_stdio
from kawk50m.io import ShardedJsonlWriter, discover_paths, iter_documents
from kawk50m.text import (
FilterConfig,
canonical_hash,
measure_text,
near_duplicate_signature,
normalize_text,
quality_score,
rejection_reason,
split_for_hash,
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description=(
"Clean, quality-filter, exactly/approximately deduplicate, "
"and split Korean documents."
)
)
parser.add_argument(
"--input",
nargs="+",
required=True,
help="Input files, directories, or glob patterns (.jsonl/.txt, optionally .gz).",
)
parser.add_argument("--output-dir", default="data/clean")
parser.add_argument("--text-field", default="text")
parser.add_argument("--txt-mode", choices=("line", "document"), default="line")
parser.add_argument("--documents-per-shard", type=int, default=50_000)
parser.add_argument("--validation-ratio", type=float, default=0.002)
parser.add_argument("--test-ratio", type=float, default=0.001)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--limit", type=int, default=None)
parser.add_argument(
"--workers",
type=int,
default=1,
help="Process normalization/filtering/hash batches in parallel.",
)
parser.add_argument(
"--worker-batch-size",
type=int,
default=64,
help="Documents submitted to each filtering worker at once.",
)
parser.add_argument("--min-chars", type=int, default=300)
parser.add_argument("--max-chars", type=int, default=100_000)
parser.add_argument("--min-hangul-chars", type=int, default=50)
parser.add_argument("--min-hangul-ratio", type=float, default=0.70)
parser.add_argument("--min-hangul-visible-ratio", type=float, default=0.55)
parser.add_argument("--max-digit-ratio", type=float, default=0.08)
parser.add_argument("--max-symbol-ratio", type=float, default=0.15)
parser.add_argument("--max-url-per-word", type=float, default=0.01)
parser.add_argument("--min-unique-line-ratio", type=float, default=0.70)
parser.add_argument("--min-unique-word-ratio", type=float, default=0.20)
parser.add_argument("--max-short-line-ratio", type=float, default=0.50)
parser.add_argument("--max-list-line-ratio", type=float, default=0.25)
parser.add_argument("--max-table-line-ratio", type=float, default=0.10)
parser.add_argument(
"--max-boilerplate-hits-per-1k", type=float, default=1.5
)
parser.add_argument("--max-repeated-character-run", type=int, default=20)
parser.add_argument("--max-code-line-ratio", type=float, default=0.25)
parser.add_argument("--max-code-math-char-ratio", type=float, default=0.15)
parser.add_argument("--min-quality-score", type=float, default=0.70)
parser.add_argument(
"--disable-near-dedupe",
action="store_true",
help="Disable bottom-k paragraph/chunk near-duplicate sketches.",
)
parser.add_argument(
"--keep-html", action="store_true", help="Do not remove residual HTML tags."
)
parser.add_argument(
"--keep-pii",
action="store_true",
help="Do not replace email, phone, and resident-registration patterns.",
)
return parser.parse_args()
def process_document(
document: dict[str, Any],
filter_config: FilterConfig,
) -> tuple[dict[str, Any], str, str | None, str | None, str | None, float]:
text = normalize_text(
document["text"],
strip_html=filter_config.strip_html,
mask_pii=filter_config.mask_pii,
)
metrics = measure_text(text)
reason = rejection_reason(text, filter_config, metrics=metrics)
document_hash = canonical_hash(text) if reason is None else None
near_signature = (
near_duplicate_signature(text) if reason is None else None
)
score = quality_score(metrics, filter_config)
return document, text, reason, document_hash, near_signature, score
def process_batch(
documents: list[dict[str, Any]],
filter_config: FilterConfig,
) -> list[
tuple[dict[str, Any], str, str | None, str | None, str | None, float]
]:
return [process_document(document, filter_config) for document in documents]
def document_batches(
documents: Iterable[dict[str, Any]],
*,
batch_size: int,
limit: int | None,
) -> Iterator[list[dict[str, Any]]]:
batch: list[dict[str, Any]] = []
seen = 0
for document in documents:
if limit is not None and seen >= limit:
break
batch.append(document)
seen += 1
if len(batch) >= batch_size:
yield batch
batch = []
if batch:
yield batch
def processed_documents(
documents: Iterable[dict[str, Any]],
*,
filter_config: FilterConfig,
workers: int,
batch_size: int,
limit: int | None,
) -> Iterator[
tuple[dict[str, Any], str, str | None, str | None, str | None, float]
]:
batches = document_batches(documents, batch_size=batch_size, limit=limit)
if workers == 1:
for batch in batches:
yield from process_batch(batch, filter_config)
return
max_pending = workers * 2
pending: deque[Any] = deque()
with ProcessPoolExecutor(max_workers=workers) as executor:
for batch in batches:
pending.append(executor.submit(process_batch, batch, filter_config))
if len(pending) >= max_pending:
yield from pending.popleft().result()
while pending:
yield from pending.popleft().result()
def ensure_safe_output(output_dir: Path) -> None:
existing = [
*output_dir.glob("train-*.jsonl"),
*output_dir.glob("validation-*.jsonl"),
*output_dir.glob("test-*.jsonl"),
]
if existing:
names = ", ".join(path.name for path in existing[:3])
raise FileExistsError(
f"{output_dir} already contains generated shards ({names}). "
"Choose a new output directory to avoid overwriting data."
)
def main() -> None:
configure_utf8_stdio()
args = parse_args()
paths = discover_paths(args.input)
if not paths:
raise FileNotFoundError("No supported input files were found")
if args.documents_per_shard <= 0:
raise ValueError("--documents-per-shard must be positive")
if args.workers <= 0:
raise ValueError("--workers must be positive")
if args.worker_batch_size <= 0:
raise ValueError("--worker-batch-size must be positive")
filter_config = FilterConfig(
min_chars=args.min_chars,
max_chars=args.max_chars,
min_hangul_chars=args.min_hangul_chars,
min_hangul_letter_ratio=args.min_hangul_ratio,
min_hangul_visible_ratio=args.min_hangul_visible_ratio,
max_digit_ratio=args.max_digit_ratio,
max_symbol_ratio=args.max_symbol_ratio,
max_url_per_word=args.max_url_per_word,
min_unique_line_ratio=args.min_unique_line_ratio,
min_unique_word_ratio=args.min_unique_word_ratio,
max_short_line_ratio=args.max_short_line_ratio,
max_list_line_ratio=args.max_list_line_ratio,
max_table_line_ratio=args.max_table_line_ratio,
max_boilerplate_hits_per_1k=args.max_boilerplate_hits_per_1k,
max_repeated_character_run=args.max_repeated_character_run,
max_code_line_ratio=args.max_code_line_ratio,
max_code_math_char_ratio=args.max_code_math_char_ratio,
min_quality_score=args.min_quality_score,
strip_html=not args.keep_html,
mask_pii=not args.keep_pii,
)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
ensure_safe_output(output_dir)
dedupe_path = output_dir / "exact_dedupe.sqlite3"
if dedupe_path.exists():
raise FileExistsError(
f"{dedupe_path} already exists. Choose a new output directory."
)
counters: Counter[str] = Counter()
writers = {
split: ShardedJsonlWriter(
output_dir, split, documents_per_shard=args.documents_per_shard
)
for split in ("train", "validation", "test")
}
connection = sqlite3.connect(dedupe_path)
connection.execute("PRAGMA journal_mode=WAL")
connection.execute("PRAGMA synchronous=NORMAL")
connection.execute("CREATE TABLE hashes (hash TEXT PRIMARY KEY)")
if not args.disable_near_dedupe:
connection.execute(
"CREATE TABLE near_signatures (signature TEXT PRIMARY KEY)"
)
try:
documents = iter_documents(
paths, text_field=args.text_field, txt_mode=args.txt_mode
)
processed = processed_documents(
documents,
filter_config=filter_config,
workers=args.workers,
batch_size=args.worker_batch_size,
limit=args.limit,
)
for index, (
document,
text,
reason,
document_hash,
near_signature,
score,
) in enumerate(
tqdm(processed, total=args.limit, unit="docs"),
start=1,
):
counters["seen"] += 1
if reason is not None:
counters[f"rejected_{reason}"] += 1
else:
assert document_hash is not None
cursor = connection.execute(
"INSERT OR IGNORE INTO hashes(hash) VALUES (?)", (document_hash,)
)
if cursor.rowcount == 0:
counters["rejected_exact_duplicate"] += 1
else:
if not args.disable_near_dedupe:
assert near_signature is not None
near_cursor = connection.execute(
"INSERT OR IGNORE INTO near_signatures(signature) "
"VALUES (?)",
(near_signature,),
)
if near_cursor.rowcount == 0:
counters["rejected_near_duplicate"] += 1
continue
split = split_for_hash(
document_hash,
validation_ratio=args.validation_ratio,
test_ratio=args.test_ratio,
seed=args.seed,
)
output_row = {
"id": document["id"],
"text": text,
"source": document["source"],
"url": document["url"],
"license": document["license"],
"sha256": document_hash,
"quality_score": round(score, 6),
}
writers[split].write(output_row)
counters[f"accepted_{split}"] += 1
counters["accepted_characters"] += len(text)
counters["accepted_quality_score_micros"] += round(
score * 1_000_000
)
if index % 10_000 == 0:
connection.commit()
connection.commit()
finally:
connection.close()
for writer in writers.values():
writer.close()
stats = {
"inputs": [str(path) for path in paths],
"filter": asdict(filter_config),
"near_dedupe": {
"enabled": not args.disable_near_dedupe,
"method": "bottom-k paragraph/chunk sketch",
},
"split": {
"train": 1.0 - args.validation_ratio - args.test_ratio,
"validation": args.validation_ratio,
"test": args.test_ratio,
"seed": args.seed,
},
"counts": dict(sorted(counters.items())),
}
stats_path = output_dir / "prepare_stats.json"
with stats_path.open("w", encoding="utf-8") as handle:
json.dump(stats, handle, ensure_ascii=False, indent=2)
handle.write("\n")
print(json.dumps(stats["counts"], ensure_ascii=False, indent=2))
print(f"Stats: {stats_path}")
if __name__ == "__main__":
main()
|