paulomouraj Paulo Roberto Moura Junior commited on
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Co-authored-by: Paulo Roberto Moura Junior <Paulo Roberto Moura Junior@users.noreply.huggingface.co>

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+ data_files:
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+ - config_name: bitbake
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+ data_files:
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+ - split: train
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+ - config_name: c
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+ data_files:
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+ - split: train
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+ path: c/train-*
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+ - config_name: clojure
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+ data_files:
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+ data_files:
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+ path: csharp/train-*
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+ data_files:
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+ - split: train
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+ path: css/train-*
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+ - config_name: elixir
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+ data_files:
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+ - split: train
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+ - config_name: emacs-lisp
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+ data_files:
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+ path: php/train-*
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+ path: rust/train-*
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+ - config_name: scala
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+ path: scala/train-*
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+ - split: train
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+ path: scss/train-*
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+ - split: train
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+ path: shell/train-*
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+ data_files:
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+ - split: train
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+ path: slim/train-*
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+ - config_name: sql
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+ data_files:
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+ - split: train
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+ - config_name: swift
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+ data_files:
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+ - split: train
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+ path: swift/train-*
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+ - config_name: text
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+ data_files:
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+ - split: train
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+ path: text/train-*
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+ - config_name: toml
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+ data_files:
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+ - split: train
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+ path: toml/train-*
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+ - config_name: twig
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+ data_files:
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+ - split: train
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+ path: twig/train-*
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+ - config_name: typescript
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+ data_files:
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+ - split: train
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+ path: typescript/train-*
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+ - config_name: unknown
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+ data_files:
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+ - split: train
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+ path: unknown/train-*
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+ - config_name: viml
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+ data_files:
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+ - split: train
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+ path: viml/train-*
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+ - config_name: xml
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+ data_files:
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+ - split: train
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+ path: xml/train-*
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+ - config_name: yaml
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+ data_files:
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+ - split: train
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+ path: yaml/train-*
1038
+ ---
1039
+
1040
+ # CodeEditSearchTrain
1041
+
1042
+ A code-edit retrieval training set, derived from [`bigcode/commitpackft`](https://huggingface.co/datasets/bigcode/commitpackft)
1043
+ and decontaminated against the [`cassanof/CodeEditSearch`](https://huggingface.co/datasets/cassanof/CodeEditSearch) eval.
1044
+
1045
+ Each example contains an `instruction` (the commit message), a unified `diff`, and the `before`/`after` file contents, plus
1046
+ the source `commit` SHA.
1047
+
1048
+ ## How the dataset is built
1049
+
1050
+ We start from `bigcode/commitpackft`, a large collection of code-edit commits across many programming languages, and
1051
+ keep every language with at least 1,000 commits — 47 languages in total. For each commit we keep the message as the
1052
+ natural-language instruction, render a short unified diff between the file before and after the change, and store the
1053
+ full file contents on both sides. Commits that don't have both a before and an after file, or that have an empty
1054
+ message, are discarded.
1055
+
1056
+ The harder problem is making sure none of the kept commits leak into the
1057
+ [`cassanof/CodeEditSearch`](https://huggingface.co/datasets/cassanof/CodeEditSearch) evaluation set. We apply three
1058
+ filters, each catching something the previous one would miss:
1059
+
1060
+ 1. **Same commit.** If a commit's SHA already appears in the eval, we drop it, because same git commit means same code change.
1061
+ 2. **Same text after normalization.** Two commits can describe the same change with different SHAs (cherry-picks,
1062
+ rebases, mirrored repos). We lowercase, normalize Unicode and collapse whitespace, then hash every text field with
1063
+ xxHash-64 and compare against the same hashes computed over the eval. Anything that collides is dropped.
1064
+ 3. **Near-duplicate text.** Even after normalization, paraphrased or partially edited versions slip through. We extract
1065
+ every 13-word sequence from each field and compute what fraction of them also appear in the eval (containment).
1066
+ If any field crosses 50% overlap, the commit is dropped.
1067
+
1068
+ Steps 2 and 3 follow the decontamination methodology described in our
1069
+ [English models blog post](https://huggingface.co/blog/lightonai/denseon-lateon) ("Decontaminated BEIR" section) and our
1070
+ [multilingual models blog post](https://huggingface.co/blog/lightonai/mdenseon-mlateon).
1071
+
1072
+ The eval only covers 13 languages, but the contamination index pools everything from those 13 into one big set, and we
1073
+ check every commit in all 47 source languages against it, so a Python snippet that also appears verbatim in a YAML
1074
+ commit message would still be caught.
1075
+
1076
+ **End result:** 678,295 source commits → 599,698 with both file contents → 578,203 after dropping shared SHAs →
1077
+ 573,593 after the exact-text filter → **560,922 kept** after the near-duplicate filter. Per-language numbers are in
1078
+ the report below.
1079
+
1080
+ ## Generation code
1081
+
1082
+ <details>
1083
+ <summary>
1084
+ Python code to build and decontaminate the dataset
1085
+ </summary>
1086
+
1087
+ ```python
1088
+ """
1089
+ Build a code-edit retrieval training set from `bigcode/commitpackft`,
1090
+ deduped against `cassanof/CodeEditSearch` via (A) commit-SHA exclusion,
1091
+ (B) normalized xxHash-64, and (C) 13-gram containment.
1092
+ """
1093
+
1094
+ from __future__ import annotations
1095
+
1096
+ import argparse
1097
+ import difflib
1098
+ import json
1099
+ import logging
1100
+ import os
1101
+ import re
1102
+ import sys
1103
+ import unicodedata
1104
+ import urllib.request
1105
+ from pathlib import Path
1106
+
1107
+ import xxhash
1108
+ from datasets import load_dataset, Dataset
1109
+ from dotenv import load_dotenv
1110
+ from tqdm import tqdm
1111
+
1112
+ logger = logging.getLogger(__name__)
1113
+
1114
+
1115
+ # 13 languages covered by the cassanof/CodeEditSearch eval:
1116
+ EVAL_LANGUAGES = [
1117
+ "c",
1118
+ "c++",
1119
+ "go",
1120
+ "java",
1121
+ "javascript",
1122
+ "php",
1123
+ "python",
1124
+ "ruby",
1125
+ "rust",
1126
+ "scala",
1127
+ "shell",
1128
+ "swift",
1129
+ "typescript",
1130
+ ]
1131
+
1132
+ SHUFFLE_SEED = 42
1133
+ EVAL_DATASET = "cassanof/CodeEditSearch"
1134
+ SOURCE_DATASET = "bigcode/commitpackft"
1135
+ SIZE_API = "https://datasets-server.huggingface.co/size?dataset={dataset}"
1136
+
1137
+ REPORT_DIR = Path(__file__).resolve().parent / "output"
1138
+
1139
+ WHITESPACE_RE = re.compile(r"\s+")
1140
+
1141
+
1142
+ def discover_languages(dataset: str, min_rows: int) -> list[str]:
1143
+ """Fetch all configs from HF datasets-server with at least `min_rows` rows."""
1144
+ url = SIZE_API.format(dataset=dataset)
1145
+ with urllib.request.urlopen(url) as resp:
1146
+ data = json.load(resp)
1147
+ configs = data["size"]["configs"]
1148
+ kept = [c["config"] for c in configs if c["num_rows"] >= min_rows]
1149
+ logger.info(
1150
+ "discovered %d/%d configs in %s with >= %d rows",
1151
+ len(kept), len(configs), dataset, min_rows,
1152
+ )
1153
+ return sorted(kept)
1154
+
1155
+
1156
+ def collect_eval_shas(languages: list[str]) -> set[str]:
1157
+ """Pool every commit SHA appearing in the eval dataset across the given languages."""
1158
+ shas: set[str] = set()
1159
+ for lang in languages:
1160
+ try:
1161
+ ds = load_dataset(EVAL_DATASET, lang, split="train")
1162
+ except Exception as e:
1163
+ logger.warning("could not load eval for lang=%s: %s", lang, e)
1164
+ continue
1165
+ for row in ds:
1166
+ sha = row.get("commit")
1167
+ if sha:
1168
+ shas.add(sha)
1169
+ logger.info("collected %d eval commit SHAs across %d languages", len(shas), len(languages))
1170
+ return shas
1171
+
1172
+
1173
+ def has_nonempty_contents(row: dict) -> bool:
1174
+ """True if both `old_contents` and `new_contents` are present and non-empty."""
1175
+ return bool(row.get("old_contents")) and bool(row.get("new_contents"))
1176
+
1177
+
1178
+ def build_unified_diff(old: str, new: str, old_file: str = "a", new_file: str = "b") -> str:
1179
+ """Render a unified diff (3 lines of context) between two file contents."""
1180
+ return "".join(difflib.unified_diff(
1181
+ old.splitlines(keepends=True),
1182
+ new.splitlines(keepends=True),
1183
+ fromfile=old_file,
1184
+ tofile=new_file,
1185
+ n=3,
1186
+ ))
1187
+
1188
+
1189
+ def normalize(text: str) -> str:
1190
+ """NFKD-fold, lowercase, and collapse whitespace runs to single spaces."""
1191
+ text = unicodedata.normalize("NFKD", text).lower()
1192
+ return WHITESPACE_RE.sub(" ", text).strip()
1193
+
1194
+
1195
+ def hash64(text: str) -> int:
1196
+ """64-bit xxh64 of text bytes."""
1197
+ return xxhash.xxh64(text.encode("utf-8", errors="replace")).intdigest()
1198
+
1199
+
1200
+ def word_ngrams(text: str, n: int = 13) -> set[str]:
1201
+ """Set of contiguous n-word phrases from whitespace-split text."""
1202
+ words = text.split()
1203
+ if len(words) < n:
1204
+ return set()
1205
+ return {" ".join(words[i:i + n]) for i in range(len(words) - n + 1)}
1206
+
1207
+
1208
+ def field_hash(text: str) -> int | None:
1209
+ """Normalized hash of one field; None if the field is empty after normalization."""
1210
+ norm = normalize(text or "")
1211
+ if not norm:
1212
+ return None
1213
+ return hash64(norm)
1214
+
1215
+
1216
+ def build_eval_overlap_index(languages: list[str], n: int = 13) -> tuple[set[int], set[str]]:
1217
+ """Index per-field normalized hashes and pooled per-field n-grams across all eval rows."""
1218
+ field_hashes: set[int] = set()
1219
+ ngram_set: set[str] = set()
1220
+ logger.info("building per-field contamination index over eval rows...")
1221
+ total = 0
1222
+ for lang in languages:
1223
+ try:
1224
+ ds = load_dataset(EVAL_DATASET, lang, split="train")
1225
+ except Exception:
1226
+ raise RuntimeError(f"failed to load eval dataset for lang={lang}; check that EVAL_LANGUAGES is correct and that the dataset is accessible")
1227
+ for row in tqdm(ds, desc=f"overlap-index[{lang}]"):
1228
+ fields = (
1229
+ (row.get("instruction") or "").strip(),
1230
+ row.get("diff") or "",
1231
+ row.get("before") or "",
1232
+ row.get("after") or "",
1233
+ )
1234
+ if not any(fields):
1235
+ continue
1236
+ for f in fields:
1237
+ h = field_hash(f)
1238
+ if h is not None:
1239
+ field_hashes.add(h)
1240
+ ngram_set.update(word_ngrams(normalize(f), n=n))
1241
+ total += 1
1242
+ logger.info(
1243
+ "indexed %d eval rows: %d field-hashes, %d %d-grams",
1244
+ total, len(field_hashes), len(ngram_set), n,
1245
+ )
1246
+ return field_hashes, ngram_set
1247
+
1248
+
1249
+ def contamination_reason(
1250
+ instruction: str,
1251
+ diff: str,
1252
+ old_contents: str,
1253
+ new_contents: str,
1254
+ eval_field_hashes: set[int],
1255
+ eval_ngrams: set[str],
1256
+ n: int = 13,
1257
+ threshold: float = 0.5,
1258
+ ) -> str:
1259
+ """Return "hash"/"ngram" if any of the 4 fields matches eval, else "" (clean)."""
1260
+ fields = (instruction, diff, old_contents, new_contents)
1261
+ for f in fields:
1262
+ h = field_hash(f)
1263
+ if h is not None and h in eval_field_hashes:
1264
+ return "hash"
1265
+ for f in fields:
1266
+ sample_ngrams = word_ngrams(normalize(f), n=n)
1267
+ if not sample_ngrams:
1268
+ continue
1269
+ if len(sample_ngrams & eval_ngrams) / len(sample_ngrams) >= threshold:
1270
+ return "ngram"
1271
+ return ""
1272
+
1273
+
1274
+ def build_language(
1275
+ lang: str,
1276
+ eval_shas: set[str],
1277
+ eval_field_hashes: set[int],
1278
+ eval_ngrams: set[str],
1279
+ output_repo: str,
1280
+ max_per_lang: int | None,
1281
+ ngram_n: int = 13,
1282
+ overlap_threshold: float = 0.5,
1283
+ ) -> dict:
1284
+ """Filter one language of the source dataset against the eval, push to the Hub, and return its funnel."""
1285
+ logger.info("lang=%s loading source from %s", lang, SOURCE_DATASET)
1286
+ ds = load_dataset(
1287
+ "json",
1288
+ data_files=f"hf://datasets/{SOURCE_DATASET}/data/{lang}/data.jsonl",
1289
+ split="train",
1290
+ )
1291
+ source_rows = len(ds)
1292
+
1293
+ ds = ds.shuffle(seed=SHUFFLE_SEED)
1294
+ ds = ds.filter(has_nonempty_contents)
1295
+ passed_nonempty = len(ds)
1296
+ logger.info(
1297
+ "lang=%s %d rows have both old+new contents (from %d)",
1298
+ lang, passed_nonempty, source_rows,
1299
+ )
1300
+
1301
+ kept_rows: list[dict] = []
1302
+ passed_sha = passed_msg = passed_hash = passed_ngram = 0
1303
+
1304
+ for row in tqdm(ds, desc=f"filter[{lang}]"):
1305
+ if row.get("commit") in eval_shas:
1306
+ continue
1307
+ passed_sha += 1
1308
+
1309
+ instruction = (row.get("message") or "").strip()
1310
+ if not instruction:
1311
+ continue
1312
+ passed_msg += 1
1313
+
1314
+ old_contents = row.get("old_contents") or ""
1315
+ new_contents = row.get("new_contents") or ""
1316
+ diff = build_unified_diff(
1317
+ old_contents,
1318
+ new_contents,
1319
+ old_file=row.get("old_file", "a"),
1320
+ new_file=row.get("new_file", "b"),
1321
+ )
1322
+
1323
+ reason = contamination_reason(
1324
+ instruction, diff, old_contents, new_contents,
1325
+ eval_field_hashes, eval_ngrams,
1326
+ n=ngram_n, threshold=overlap_threshold,
1327
+ )
1328
+ if reason == "hash":
1329
+ continue
1330
+ passed_hash += 1
1331
+ if reason == "ngram":
1332
+ continue
1333
+ passed_ngram += 1
1334
+
1335
+ if max_per_lang and len(kept_rows) >= max_per_lang:
1336
+ continue
1337
+ kept_rows.append({
1338
+ "instruction": instruction,
1339
+ "diff": diff,
1340
+ "before": old_contents,
1341
+ "after": new_contents,
1342
+ "commit": row.get("commit"),
1343
+ })
1344
+
1345
+ logger.info(
1346
+ "lang=%s passed_nonempty=%d passed_sha=%d passed_msg=%d "
1347
+ "passed_hash=%d passed_ngram=%d kept=%d",
1348
+ lang, passed_nonempty, passed_sha, passed_msg,
1349
+ passed_hash, passed_ngram, len(kept_rows),
1350
+ )
1351
+
1352
+ config_name = lang.replace("+", "p").replace("#", "sharp")
1353
+ logger.info("lang=%s pushing to %s (config=%s)", lang, output_repo, config_name)
1354
+ Dataset.from_list(kept_rows).push_to_hub(
1355
+ repo_id=output_repo,
1356
+ config_name=config_name,
1357
+ private=True,
1358
+ )
1359
+
1360
+ return {
1361
+ "config": config_name,
1362
+ "funnel": {
1363
+ "source_rows": source_rows,
1364
+ "passed_nonempty": passed_nonempty,
1365
+ "passed_sha": passed_sha,
1366
+ "passed_nonempty_msg": passed_msg,
1367
+ "passed_hash": passed_hash,
1368
+ "passed_ngram": passed_ngram,
1369
+ "kept": len(kept_rows),
1370
+ },
1371
+ }
1372
+
1373
+
1374
+ def main(argv: list[str] | None = None) -> int:
1375
+ """CLI entrypoint: build the decontaminated training set for each language and write the run report."""
1376
+ parser = argparse.ArgumentParser(description="Build CodeEditSearch training set.")
1377
+ parser.add_argument("--output-repo", default="lightonai/CodeEditSearchTrain",
1378
+ help="HF Hub dataset repo to push to")
1379
+ parser.add_argument("--languages", nargs="+", default=None,
1380
+ help="languages to build (default: auto-discover from source via --min-rows)")
1381
+ parser.add_argument("--min-rows", type=int, default=1000,
1382
+ help="when auto-discovering, only include configs with >= this many rows (default: 1000)")
1383
+ parser.add_argument("--max-per-lang", type=int, default=None,
1384
+ help="cap rows per language (default: no cap)")
1385
+ parser.add_argument("--overlap-threshold", type=float, default=0.5,
1386
+ help="13-gram containment threshold (default 0.5)")
1387
+ parser.add_argument("--ngram-n", type=int, default=13, help="n-gram size (default 13)")
1388
+ args = parser.parse_args(argv)
1389
+
1390
+ logging.basicConfig(
1391
+ level=logging.INFO,
1392
+ format="%(asctime)s %(levelname)s %(name)s: %(message)s",
1393
+ stream=sys.stderr,
1394
+ )
1395
+
1396
+ load_dotenv()
1397
+ assert os.environ.get("HF_TOKEN"), "HF_TOKEN not set after load_dotenv(); add it to .env"
1398
+
1399
+ if args.languages is None:
1400
+ args.languages = discover_languages(SOURCE_DATASET, args.min_rows)
1401
+ logger.info("building %d languages: %s", len(args.languages), args.languages)
1402
+
1403
+ # The eval (cassanof/CodeEditSearch) only has the 13 EVAL_LANGUAGES configs,
1404
+ # so only query those — intersected with what the user requested.
1405
+ eval_overlap_langs = [l for l in args.languages if l in EVAL_LANGUAGES]
1406
+ eval_shas = collect_eval_shas(eval_overlap_langs)
1407
+ eval_field_hashes, eval_ngrams = build_eval_overlap_index(
1408
+ eval_overlap_langs, n=args.ngram_n,
1409
+ )
1410
+
1411
+ report = {
1412
+ "source_dataset": SOURCE_DATASET,
1413
+ "eval_dataset": EVAL_DATASET,
1414
+ "shuffle_seed": SHUFFLE_SEED,
1415
+ "overlap_threshold": args.overlap_threshold,
1416
+ "ngram_n": args.ngram_n,
1417
+ "eval_shas_count": len(eval_shas),
1418
+ "eval_field_hashes_count": len(eval_field_hashes),
1419
+ "eval_ngrams_count": len(eval_ngrams),
1420
+ "languages": [],
1421
+ }
1422
+ for lang in args.languages:
1423
+ report["languages"].append(build_language(
1424
+ lang=lang,
1425
+ eval_shas=eval_shas,
1426
+ eval_field_hashes=eval_field_hashes,
1427
+ eval_ngrams=eval_ngrams,
1428
+ output_repo=args.output_repo,
1429
+ max_per_lang=args.max_per_lang,
1430
+ ngram_n=args.ngram_n,
1431
+ overlap_threshold=args.overlap_threshold,
1432
+ ))
1433
+
1434
+ totals: dict[str, int] = {}
1435
+ for entry in report["languages"]:
1436
+ for k, v in entry["funnel"].items():
1437
+ totals[k] = totals.get(k, 0) + v
1438
+ report["totals"] = totals
1439
+
1440
+ REPORT_DIR.mkdir(parents=True, exist_ok=True)
1441
+ report_path = REPORT_DIR / "report.json"
1442
+ report_path.write_text(json.dumps(report, indent=2))
1443
+ logger.info("wrote report to %s", report_path)
1444
+ logger.info("totals: %s", json.dumps(totals))
1445
+ logger.info("pushed to https://huggingface.co/datasets/%s", args.output_repo)
1446
+ return 0
1447
+
1448
+
1449
+ if __name__ == "__main__":
1450
+ sys.exit(main())
1451
+ ```
1452
+ </details>
1453
+
1454
+ ## Filtering report
1455
+
1456
+ <details>
1457
+ <summary>
1458
+ Per-language decontamination funnel
1459
+ </summary>
1460
+
1461
+ ```json
1462
+ {
1463
+ "source_dataset": "bigcode/commitpackft",
1464
+ "eval_dataset": "cassanof/CodeEditSearch",
1465
+ "shuffle_seed": 42,
1466
+ "overlap_threshold": 0.5,
1467
+ "ngram_n": 13,
1468
+ "eval_shas_count": 21495,
1469
+ "eval_field_hashes_count": 84365,
1470
+ "eval_ngrams_count": 2887107,
1471
+ "languages": [
1472
+ {
1473
+ "config": "batchfile",
1474
+ "funnel": {
1475
+ "source_rows": 1466,
1476
+ "passed_nonempty": 1234,
1477
+ "passed_sha": 1234,
1478
+ "passed_nonempty_msg": 1234,
1479
+ "passed_hash": 1234,
1480
+ "passed_ngram": 1232,
1481
+ "kept": 1232
1482
+ }
1483
+ },
1484
+ {
1485
+ "config": "bitbake",
1486
+ "funnel": {
1487
+ "source_rows": 1308,
1488
+ "passed_nonempty": 924,
1489
+ "passed_sha": 924,
1490
+ "passed_nonempty_msg": 924,
1491
+ "passed_hash": 924,
1492
+ "passed_ngram": 924,
1493
+ "kept": 924
1494
+ }
1495
+ },
1496
+ {
1497
+ "config": "c",
1498
+ "funnel": {
1499
+ "source_rows": 8506,
1500
+ "passed_nonempty": 6925,
1501
+ "passed_sha": 5335,
1502
+ "passed_nonempty_msg": 5335,
1503
+ "passed_hash": 5027,
1504
+ "passed_ngram": 4415,
1505
+ "kept": 4415
1506
+ }
1507
+ },
1508
+ {
1509
+ "config": "csharp",
1510
+ "funnel": {
1511
+ "source_rows": 9346,
1512
+ "passed_nonempty": 8472,
1513
+ "passed_sha": 8472,
1514
+ "passed_nonempty_msg": 8472,
1515
+ "passed_hash": 8467,
1516
+ "passed_ngram": 8358,
1517
+ "kept": 8358
1518
+ }
1519
+ },
1520
+ {
1521
+ "config": "cpp",
1522
+ "funnel": {
1523
+ "source_rows": 4992,
1524
+ "passed_nonempty": 3855,
1525
+ "passed_sha": 2165,
1526
+ "passed_nonempty_msg": 2165,
1527
+ "passed_hash": 1967,
1528
+ "passed_ngram": 1748,
1529
+ "kept": 1748
1530
+ }
1531
+ },
1532
+ {
1533
+ "config": "clojure",
1534
+ "funnel": {
1535
+ "source_rows": 2403,
1536
+ "passed_nonempty": 2194,
1537
+ "passed_sha": 2194,
1538
+ "passed_nonempty_msg": 2194,
1539
+ "passed_hash": 2191,
1540
+ "passed_ngram": 2191,
1541
+ "kept": 2191
1542
+ }
1543
+ },
1544
+ {
1545
+ "config": "coffeescript",
1546
+ "funnel": {
1547
+ "source_rows": 5513,
1548
+ "passed_nonempty": 5131,
1549
+ "passed_sha": 5131,
1550
+ "passed_nonempty_msg": 5131,
1551
+ "passed_hash": 5129,
1552
+ "passed_ngram": 5116,
1553
+ "kept": 5116
1554
+ }
1555
+ },
1556
+ {
1557
+ "config": "css",
1558
+ "funnel": {
1559
+ "source_rows": 5049,
1560
+ "passed_nonempty": 4762,
1561
+ "passed_sha": 4762,
1562
+ "passed_nonempty_msg": 4762,
1563
+ "passed_hash": 4762,
1564
+ "passed_ngram": 4705,
1565
+ "kept": 4705
1566
+ }
1567
+ },
1568
+ {
1569
+ "config": "elixir",
1570
+ "funnel": {
1571
+ "source_rows": 1150,
1572
+ "passed_nonempty": 1016,
1573
+ "passed_sha": 1016,
1574
+ "passed_nonempty_msg": 1016,
1575
+ "passed_hash": 1015,
1576
+ "passed_ngram": 1015,
1577
+ "kept": 1015
1578
+ }
1579
+ },
1580
+ {
1581
+ "config": "emacs-lisp",
1582
+ "funnel": {
1583
+ "source_rows": 1015,
1584
+ "passed_nonempty": 950,
1585
+ "passed_sha": 950,
1586
+ "passed_nonempty_msg": 950,
1587
+ "passed_hash": 950,
1588
+ "passed_ngram": 950,
1589
+ "kept": 950
1590
+ }
1591
+ },
1592
+ {
1593
+ "config": "go",
1594
+ "funnel": {
1595
+ "source_rows": 5004,
1596
+ "passed_nonempty": 5001,
1597
+ "passed_sha": 3249,
1598
+ "passed_nonempty_msg": 3249,
1599
+ "passed_hash": 3097,
1600
+ "passed_ngram": 2925,
1601
+ "kept": 2925
1602
+ }
1603
+ },
1604
+ {
1605
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+ }
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+ },
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+ {
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+ }
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+ ],
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+ }
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+ ```
2048
+ </details>
2049
+
2050
+ ## Licensing
2051
+
2052
+ This dataset is derived from `bigcode/commitpackft` and we keep all of the original licenses of the source data.
2053
+
2054
+ ## Citation
2055
+
2056
+ If you use this dataset, please consider citing our work
2057
+
2058
+ ```bibtex
2059
+ @misc{sourty2026mdenseonmlateon,
2060
+ title = {{mDenseOn with the mLateOn}: Open Multilingual, Long-Context, and Code Retrieval Models},
2061
+ author = {Sourty, Raphael and Chaffin, Antoine and Moura Junior, Paulo Roberto and Chatelain, Amelie},
2062
+ year = {2026},
2063
+ howpublished = {\url{https://huggingface.co/blog/lightonai/mDenseOn-mLateOn}}
2064
+ }
2065
+ ```
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