---
license: cc-by-4.0
language:
- ara
- dan
- deu
- eng
- fas
- fra
- hin
- ind
- ita
- jpn
- kor
- nld
- pol
- por
- rus
- spa
- swe
- tur
- vie
- zho
multilinguality:
- multilingual
task_categories:
- text-retrieval
task_ids:
- document-retrieval
tags:
- ir_datasets
- text-retrieval
pretty_name: WebFAQ for ir_datasets
viewer: false
---

# WebFAQ for ir_datasets

Archives used by the `webfaq/*` datasets in [ir_datasets](https://ir-datasets.com/). They are downloaded
automatically by `ir_datasets.load("webfaq/<lang>/train")` and similar; you normally do not need to fetch them by hand.

Each `<lang>.tar.gz` contains:

```
webfaq/<lang>/documents.jsonl        # {"doc_id", "text"}: answers, deduplicated by exact text
webfaq/<lang>/training/queries.jsonl # {"query_id", "text"}
webfaq/<lang>/training/qrels         # TREC qrels, one confirmed answer per question
webfaq/<lang>/training/BGE-M3.res    # TREC run: BGE-M3 teacher scores for positives + hard negatives
webfaq/<lang>/test/queries.jsonl
webfaq/<lang>/test/qrels
```

They are built from [PaDaS-Lab/webfaq-retrieval](https://huggingface.co/datasets/PaDaS-Lab/webfaq-retrieval)
(queries, answers, train/test splits) and [IrvinTopi/WebFAQHardNegatives](https://huggingface.co/datasets/IrvinTopi/WebFAQHardNegatives)
(BM25-mined hard negatives scored with BGE-M3). Answers with identical text are merged, keeping the first
occurrence, and qrels and scored documents are remapped to the retained document IDs. The BGE-M3 scores are
training signals for distillation, not relevance judgments.

| Language | Documents | Train queries | Test queries | Train scoreddocs | Archive | MD5 |
|---|--:|--:|--:|--:|--:|---|
| `ara` | 141,089 | 132,664 | 10,000 | 6,336,393 | 98 MB | `fb1c59ccaba2221b1670b846aec77d59` |
| `dan` | 136,939 | 127,686 | 10,000 | 6,123,062 | 91 MB | `218d6b3e8b4fbdfb759f4f79ec580319` |
| `deu` | 886,794 | 881,201 | 10,000 | 18,702,436 | 550 MB | `a0d76a212bdbff0839013f8375c7a562` |
| `eng` | 5,252,530 | 5,268,725 | 10,000 | 25,632,193 | 1,957 MB | `14611295d1c8eedd80db4ca15f5f90e1` |
| `fas` | 226,446 | 216,940 | 10,000 | 12,654,983 | 219 MB | `1921d193ef4cfd90ae5fba5974b4ca7a` |
| `fra` | 565,142 | 559,505 | 10,000 | 19,140,183 | 434 MB | `77f5bce88f2c84485e168146bda3a553` |
| `hin` | 99,897 | 90,031 | 10,000 | 6,374,233 | 101 MB | `738f7ba66240833dc799f6ef946d6956` |
| `ind` | 109,774 | 101,315 | 10,000 | 6,134,393 | 99 MB | `6618692cb8bc84f22c091cba4eb8d2a1` |
| `ita` | 255,534 | 247,803 | 10,000 | 18,887,103 | 310 MB | `4f89ed3a74af5d19353f8764de5260ff` |
| `jpn` | 307,368 | 299,157 | 10,000 | 19,111,766 | 344 MB | `fe4f4d8d1b2df9a681273c59a37ec2ef` |
| `kor` | 101,036 | 92,000 | 10,000 | 6,347,985 | 103 MB | `10368db40e0f83eb6a51409aa8dc4d14` |
| `nld` | 368,382 | 360,662 | 10,000 | 18,627,158 | 343 MB | `645cee39c4ed725fb178f1a1d7060a82` |
| `pol` | 191,578 | 182,515 | 10,000 | 12,775,737 | 213 MB | `a922476ed3754e26ee818031a2f1f131` |
| `por` | 208,209 | 199,353 | 10,000 | 12,731,380 | 215 MB | `6dddcf33d28117cdfcdd33117b7f95af` |
| `rus` | 383,269 | 377,504 | 10,000 | 19,040,146 | 389 MB | `0527dba5764be2aada5be8fa7cd1febe` |
| `spa` | 600,855 | 594,661 | 10,000 | 19,015,957 | 433 MB | `2cbbc80199bdfd82578c7b94a97ae0dd` |
| `swe` | 157,849 | 148,738 | 10,000 | 6,155,553 | 116 MB | `c5a6c5195a746908b34fde4c73de8935` |
| `tur` | 144,115 | 134,846 | 10,000 | 6,327,043 | 112 MB | `53f013906712957365d875ff60c63bbc` |
| `vie` | 123,283 | 113,972 | 10,000 | 6,401,341 | 116 MB | `70a4f08bd733d89eb799cf8fcbbe3f39` |
| `zho` | 131,606 | 122,491 | 10,000 | 6,379,527 | 120 MB | `55477ad01d5dd79fee38ef56ed4e0e2c` |

## Citation

```bibtex
@inproceedings{Dinzinger2025WebFAQ,
  title = {{WebFAQ}: A Multilingual Collection of Natural {Q\&A} Datasets for Dense Retrieval},
  author = {Michael Dinzinger and Laura Caspari and Kanishka Ghosh Dastidar and Jelena Mitrovi{\'c} and Michael Granitzer},
  booktitle = {Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  series = {SIGIR '25},
  year = {2025},
  doi = {10.1145/3726302.3731934}
}

@inproceedings{Dinzinger2026WebFAQ2,
  title = {{WebFAQ} 2.0: A Multilingual {QA} Dataset with Mined Hard Negatives for Dense Retrieval},
  author = {Michael Dinzinger and Laura Caspari and Ali Salman and Irvin Topi and Jelena Mitrovi{\'c} and Michael Granitzer},
  booktitle = {Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  series = {SIGIR '26},
  year = {2026},
  doi = {10.1145/3805712.3808621}
}
```