PaDaS-Lab/webfaq-retrieval

WebFAQ Retrieval Dataset

Overview | Details | Structure | Examples | Considerations | License | Citation | Contact | Acknowledgement

Overview

The WebFAQ Retrieval Dataset is a carefully filtered and curated subset of the broader WebFAQ Q&A Dataset.
It is purpose-built for Information Retrieval (IR) tasks, such as training and evaluating dense or sparse retrieval models in multiple languages.

Each of the 49 largest languages from the WebFAQ corpus has been thoroughly cleaned and refined to ensure an unblurred notion of relevance between a query (question) and its corresponding document (answer). In particular, we applied:

• Deduplication of near-identical questions,
• Semantic consistency checks for question-answer alignment,
• Train/Test splits for retrieval experiments.

Details

Languages

The WebFAQ Retrieval Dataset covers 49 languages from the original WebFAQ corpus, both high-resource as well as low-resource. To ensure diversity, the 49 subsets originate from at least 100 different websites each. A single language comprises a few thousands to a few million of QA pairs after our rigorous filtering steps:

Top 20 Languages

Language	# QA pairs
ara	143k
dan	138k
deu	891k
eng	5.28M
fas	227k
fra	570k
hin	100k
ind	111k
ita	258k
jpn	309k
kor	102k
nld	371k
pol	193k
por	209k
rus	388k
spa	605k
swe	159k
tur	145k
vie	124k
zho	132k

Table of all 49 languages (lexicographical order)

Language	# QA pairs	# Test
ara	143k	10k
aze	4869	487
ben	14.3k	1432
bul	34.7k	3474
cat	12.7k	1270
ces	72.3k	7231
dan	138k	10k
deu	891k	10k
ell	38.5k	3852
eng	5.28M	10k
est	12.9k	1290
fas	227k	10k
fin	73.5k	7355
fra	570k	10k
heb	39.0k	3896
hin	100k	10k
hrv	5545	555
hun	45.3k	4530
ind	111k	10k
isl	4778	478
ita	258k	10k
jpn	309k	10k
kat	2405	241
kaz	2995	300
kor	102k	10k
lav	13.1k	1312
lit	18.4k	1837
mar	7404	741
msa	6429	643
nld	371k	10k
nor	63.2k	6324
pol	193k	10k
por	209k	10k
ron	59.9k	5990
rus	388k	10k
slk	31.5k	3153
slv	16.2k	1617
spa	605k	10k
sqi	2077	208
srp	5824	583
swe	159k	10k
tgl	3697	370
tha	47.4k	4743
tur	145k	10k
ukr	68.5k	6851
urd	2775	278
uzb	1263	127
vie	124k	10k
zho	132k	10k

Table of all 49 languages (ordered by size)

Language	# QA pairs	# Test
eng	5.28M	10k
deu	891k	10k
spa	605k	10k
fra	570k	10k
rus	388k	10k
nld	371k	10k
jpn	309k	10k
ita	258k	10k
fas	227k	10k
por	209k	10k
pol	193k	10k
swe	159k	10k
tur	145k	10k
ara	143k	10k
dan	138k	10k
zho	132k	10k
vie	124k	10k
ind	111k	10k
kor	102k	10k
hin	100k	10k
fin	73.5k	7355
ces	72.3k	7231
ukr	68.5k	6851
nor	63.2k	6324
ron	59.9k	5990
tha	47.4k	4743
hun	45.3k	4530
heb	39.0k	3896
ell	38.5k	3852
bul	34.7k	3474
slk	31.5k	3153
lit	18.4k	1837
slv	16.2k	1617
ben	14.3k	1432
lav	13.1k	1312
est	12.9k	1290
cat	12.7k	1270
mar	7404	741
msa	6429	643
srp	5824	583
hrv	5545	555
aze	4869	487
isl	4778	478
tgl	3697	370
kaz	2995	300
urd	2775	278
kat	2405	241
sqi	2077	208
uzb	1263	127

NDCG@10 in % for three examplary multilingual embedding models

Embedding_Model	#_Params	ara	aze	ben	bul	cat	ces	dan	deu	ell	eng	est	fas	fin	fra	heb	hin	hrv	hun	ind	isl	ita	jpn	kat	kaz	kor	lav	lit	mar	msa	nld	nor	pol	por	ron	rus	slk	slv	spa	sqi	srp	swe	tgl	tha	tur	ukr	urd	uzb	vie	zho
p-m-MiniLM-L12-v2	118M	40.89	37.90	12.82	53.62	71.06	51.63	59.31	36.34	58.14	46.86	59.81	36.30	48.04	40.35	47.71	49.73	74.91	55.53	57.92	43.15	46.64	37.43	61.09	39.35	43.36	56.48	56.04	53.56	81.57	45.85	60.91	43.59	51.48	61.22	32.97	59.42	57.91	45.17	77.38	68.31	52.39	35.78	49.84	35.96	44.77	70.47	38.61	51.42	56.43
m-E5-base	278M	75.66	63.51	80.90	78.65	87.34	78.66	84.14	67.91	80.34	64.29	81.33	70.01	76.14	68.66	78.82	75.26	87.22	79.47	76.65	86.22	75.52	70.02	66.22	67.90	82.52	75.78	80.28	81.16	90.75	74.60	84.46	73.67	75.61	83.08	63.21	83.62	79.27	72.72	86.77	83.17	79.03	80.79	75.24	65.02	76.17	82.17	72.52	81.93	84.18
jina-v3	572M	84.64	84.31	91.85	85.25	94.74	85.99	87.53	74.22	87.01	66.70	89.72	79.49	83.52	76.41	86.80	86.86	94.67	85.44	84.02	93.11	82.29	75.29	94.49	90.17	85.41	87.00	89.65	94.46	95.10	79.85	88.59	81.27	83.13	88.43	72.02	89.34	87.33	78.63	96.36	88.85	83.68	93.60	79.23	70.96	83.88	93.27	88.91	87.40	88.85

Structure

Unlike the raw Q&A dataset, WebFAQ Retrieval provides explicit train/test splits for each of the 49 languages. The general structure for each language is:

• Corpus: A set of unique documents (answers) with IDs and text fields.
• Queries: A set of question strings, each tied to a document ID for relevance.
• Qrels: Relevance labels, mapping each question to its relevant document (corresponding answer).

Folder Layout (e.g., for eng)

eng/
  ├── corpus.jsonl        # all unique documents (answers)
  ├── queries.jsonl       # all queries for train/test
  ├── train.jsonl         # relevance annotations for train
  └── test.jsonl          # relevance annotations for test

Examples

Below is a small snippet showing how to load English train/test sets with 🤗 Datasets:

import json
from datasets import load_dataset
from tqdm import tqdm

# Load train qrels
train_qrels = load_dataset(
    "PaDaS-Lab/webfaq-retrieval",
    "eng-qrels",
    split="train"
)

# Inspect first qrel
print(json.dumps(train_qrels[0], indent=4))

# Load the corpus (answers)
data_corpus = load_dataset(
    "PaDaS-Lab/webfaq-retrieval",
    "eng-corpus",
    split="corpus"
)
corpus = {
    d["_id"]: {"title": d["title"], "text": d["text"]} for d in tqdm(data_corpus)
}

# Inspect first document
print("Document:")
print(json.dumps(corpus[train_qrels[0]["corpus-id"]], indent=4))

# Load all queries
data_queries = load_dataset(
    "PaDaS-Lab/webfaq-retrieval",
    "eng-queries",
    split="queries"
)
queries = {
    q["_id"]: q["text"] for q in tqdm(data_queries)
}

# Inspect first query
print("Query:")
print(json.dumps(queries[train_qrels[0]["query-id"]], indent=4))

# Keep only those queries with relevance annotations
query_ids = set([q["query-id"] for q in train_qrels])
queries = {
    qid: query for qid, query in queries.items() if qid in query_ids
}
print(f"Number of queries: {len(queries)}")

Below is a code snippet showing how to evaluate retrieval performance using the mteb library:

from mteb import MTEB
from mteb.tasks.Retrieval.multilingual.WebFAQRetrieval import WebFAQRetrieval

# ... Load model ...

# Load the WebFAQ task
task = WebFAQRetrieval()
eval_split = "test"

evaluation = MTEB(tasks=[task])
evaluation.run(
    model,
    eval_splits=[eval_split],
    output_folder="output",
    overwrite_results=True
)

Considerations

Please note the following considerations when using the collected QAs:

• [Q&A Dataset] Risk of Duplicate or Near-Duplicate Content: The raw Q&A dataset is large and includes minor paraphrases.
• [Retrieval Dataset] Sparse Relevance: As raw FAQ data, each question typically has one “best” (on-page) answer. Additional valid answers may exist on other websites but are not labeled as relevant.
• Language Detection Limitations: Some QA pairs mix languages, or contain brand names, which can confuse automatic language classification.
• No Guarantee of Factual Accuracy: Answers reflect the content of the source websites. They may include outdated, biased, or incorrect information.
• Copyright and Privacy: Please ensure compliance with any applicable laws and the source website’s terms.

License

The Collection of WebFAQ Datasets is shared under Creative Commons Attribution 4.0 (CC BY 4.0) license.

Note: The dataset is derived from public webpages in Common Crawl snapshots (2022–2024) and intended for research purposes. Each FAQ’s text is published by the original website under their terms. Downstream users should verify any usage constraints from the original websites as well as Common Crawl’s Terms of Use.

Citation

If you use this dataset in your research, please consider citing the associated paper:

@misc{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 Mitrović and Michael Granitzer},
    year={2025},
    eprint={2502.20936},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Contact

For inquiries and feedback, please feel free to contact us via E-Mail (michael.dinzinger@uni-passau.de) or start a discussion on HuggingFace or GitHub.

Acknowledgement

We thank the Common Crawl and Web Data Commons teams for providing the underlying data, and all contributors who helped shape the WebFAQ project.

Thank you

We hope the Collection of WebFAQ Datasets serves as a valuable resource for your research. Please consider citing it in any publications or projects that use it. If you encounter issues or want to contribute improvements, feel free to get in touch with us on HuggingFace or GitHub.

Happy researching!