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| path: az/train-* |
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| path: bg/train-* |
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| path: bn/train-* |
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| path: ca/train-* |
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| path: el/train-* |
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| data_files: |
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| path: fi/train-* |
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| data_files: |
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| path: fr/train-* |
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| data_files: |
| - split: train |
| path: he/train-* |
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| data_files: |
| - split: train |
| path: hi/train-* |
| - config_name: hu |
| data_files: |
| - split: train |
| path: hu/train-* |
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| data_files: |
| - split: train |
| path: id/train-* |
| - config_name: is |
| data_files: |
| - split: train |
| path: is/train-* |
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| data_files: |
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| data_files: |
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| path: ja/train-* |
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| path: ko/train-* |
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| path: lt/train-* |
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| path: lv/train-* |
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| data_files: |
| - split: train |
| path: mr/train-* |
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| data_files: |
| - split: train |
| path: ms/train-* |
| - config_name: nl |
| data_files: |
| - split: train |
| path: nl/train-* |
| - config_name: 'no' |
| data_files: |
| - split: train |
| path: no/train-* |
| - config_name: pl |
| data_files: |
| - split: train |
| path: pl/train-* |
| - config_name: pt |
| data_files: |
| - split: train |
| path: pt/train-* |
| - config_name: ro |
| data_files: |
| - split: train |
| path: ro/train-* |
| - config_name: ru |
| data_files: |
| - split: train |
| path: ru/train-* |
| - config_name: sk |
| data_files: |
| - split: train |
| path: sk/train-* |
| - config_name: sl |
| data_files: |
| - split: train |
| path: sl/train-* |
| - config_name: sv |
| data_files: |
| - split: train |
| path: sv/train-* |
| - config_name: ta |
| data_files: |
| - split: train |
| path: ta/train-* |
| - config_name: te |
| data_files: |
| - split: train |
| path: te/train-* |
| - config_name: th |
| data_files: |
| - split: train |
| path: th/train-* |
| - config_name: tl |
| data_files: |
| - split: train |
| path: tl/train-* |
| - config_name: tr |
| data_files: |
| - split: train |
| path: tr/train-* |
| - config_name: uk |
| data_files: |
| - split: train |
| path: uk/train-* |
| - config_name: vi |
| data_files: |
| - split: train |
| path: vi/train-* |
| - config_name: zh |
| data_files: |
| - split: train |
| path: zh/train-* |
| --- |
| |
| # 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`](https://huggingface.co/datasets/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 |
| |
| ```python |
| 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`](https://huggingface.co/datasets/PaDaS-Lab/webfaq). |
| If you use this dataset, please cite the original WebFAQ release as well as |
| this cleaning pass. |
|
|
| Intended for research use. |
|
|
