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webfaq_cleaned
A cleaned, multilingual question–document dataset across 44 languages,
~58.5M Q–D pairs total, intended as a contrastive training /
retrieval-eval corpus. Derived from
PaDaS-Lab/webfaq.
The source data is web-scraped FAQ pages. The raw input contained large amounts of SEO doorway pages, casino spam, e-commerce listing pages whose review snippet didn't match the product query, near-duplicate templates, and broken encodings. This dataset applies a multi-layer cleaning pipeline to filter that noise while preserving the substantive Q&A.
Schema
Each row is a single Q–D pair:
| field | type | description |
|---|---|---|
| query | string | the question |
| document | string | the answer / supporting passage |
One config per language; one train split per config.
Usage
from datasets import load_dataset
# Load one language
ds = load_dataset("bowang0911/webfaq_cleaned", "en", split="train")
print(ds[0])
# {'query': '...', 'document': '...'}
# Or stream it
ds = load_dataset("bowang0911/webfaq_cleaned", "ja", split="train", streaming=True)
for row in ds.take(5):
print(row)
Languages
44 languages, sorted by final row count. source = rows pulled from the
original PaDaS-Lab/webfaq shards; kept = rows remaining after cleaning.
| lang | source | kept | kept% | lang | source | kept | kept% |
|---|---|---|---|---|---|---|---|
| en | 49,275,346 | 40,817,655 | 82.8% | ro | 546,709 | 378,587 | 69.2% |
| fa | 969,748 | 841,712 | 86.8% | fi | 641,009 | 373,519 | 58.3% |
| ar | 1,000,000 | 829,000 | 82.9% | th | 502,215 | 355,047 | 70.7% |
| tr | 1,000,000 | 820,040 | 82.0% | el | 494,019 | 349,025 | 70.7% |
| fr | 1,000,000 | 805,753 | 80.6% | hu | 363,866 | 283,635 | 78.0% |
| nl | 1,000,000 | 802,485 | 80.2% | no | 316,320 | 248,062 | 78.4% |
| de | 1,000,000 | 798,319 | 79.8% | he | 391,084 | 188,530 | 48.2% |
| es | 1,000,000 | 797,885 | 79.8% | sk | 177,748 | 141,875 | 79.8% |
| it | 1,000,000 | 796,614 | 79.7% | bg | 164,015 | 134,795 | 82.2% |
| pt | 1,000,000 | 793,637 | 79.4% | lt | 119,192 | 89,490 | 75.1% |
| ru | 1,000,000 | 784,173 | 78.4% | is | 91,630 | 64,358 | 70.2% |
| pl | 1,000,000 | 782,624 | 78.3% | lv | 79,443 | 59,446 | 74.8% |
| vi | 1,000,000 | 723,073 | 72.3% | ca | 80,189 | 57,740 | 72.0% |
| zh | 1,000,000 | 682,722 | 68.3% | et | 69,643 | 56,385 | 81.0% |
| sv | 832,140 | 675,465 | 81.2% | bn | 57,943 | 50,329 | 86.9% |
| ja | 1,000,000 | 662,302 | 66.2% | sl | 49,867 | 42,019 | 84.3% |
| da | 768,688 | 609,387 | 79.3% | mr | 30,658 | 26,642 | 86.9% |
| ko | 646,996 | 592,344 | 91.6% | ms | 24,448 | 20,867 | 85.4% |
| uk | 805,505 | 580,032 | 72.0% | te | 23,949 | 19,578 | 81.7% |
| id | 652,660 | 482,649 | 74.0% | az | 19,738 | 18,357 | 93.0% |
| hi | 537,488 | 478,818 | 89.1% | tl | 21,467 | 16,743 | 78.0% |
| cs | 538,343 | 392,826 | 73.0% | ta | 19,815 | 16,305 | 82.3% |
Cleaning
Filters were developed iteratively: sample random rows per language, have
an LLM inspect them to identify recurring failure patterns (templated SEO
doorways, casino doorway pages, mismatched product-listing Q–Ds, encoding
glitches), then encode those patterns as cheap substring/regex filters and
re-sample to verify. Each row passes through four lightweight passes —
a substring blocklist (cross-language SEO brand markers like KAYAK /
momondo / Tripadvisor, plus a few per-language pattern sets added where
sampling revealed language-specific noise ecosystems), a yes/no echo +
query-template filter for the affected languages, near-duplicate dedup via
MinHash LSH (64 perms, 16 bands, char-5gram shingles, Jaccard ≥ 0.85 per
bucket), and encoding repair (HTML entities, JSON-unicode-escape leftovers,
and literal \r\n\t fixed in place; rows still containing \ufffd
after fix are dropped). The pipeline is deliberately conservative — it
targets templated noise and known encoding glitches rather than scoring
semantic relevance, so well-aligned Q–Ds are preserved even when the
surface form is somewhat templated.
Known limitations
- Languages where product-listing SEO is heavy still have a residual ~15–20% mismatched pair rate after cleaning. The dominant failure mode is product-title queries paired with unrelated user reviews — each pair is unique (so dedup doesn't catch it) and lacks distinctive substring markers, so removing it cleanly requires a semantic Q–D relevance classifier rather than substring filtering.
- Languages with large kept% drops (he, fi, ro, th, vi, el, cs, etc.) reflect heavy removal of templated travel-aggregator hotel-CTA pages, not loss of substantive QA. Spot checks on the kept rows for these languages show 90–100% substantive content.
- Other languages (en, ar, hi, ko, de, fr, es, it, pt, ru, etc.) spot-check at 90–100% substantive. The remaining ~5–10% is mostly benign templated single-source answers (price predictions, weather summaries, hotel facts) rather than truly unaligned pairs.
Source & citation
This dataset is a derived, filtered version of
PaDaS-Lab/webfaq.
If you use this dataset, please cite the original WebFAQ release as well as
this cleaning pass.
Intended for research use.