Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
json
Sub-tasks:
document-retrieval
Size:
10M - 100M
ArXiv:
Tags:
text-retrieval
metadata
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
config_names:
- corpus
tags:
- text-retrieval
dataset_info:
- config_name: ara-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 6293965
num_examples: 132664
- name: test
num_bytes: 474351
num_examples: 10000
- config_name: ara-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 162827578
num_examples: 142664
- config_name: ara-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 35458944
num_examples: 142664
- config_name: dan-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 6050436
num_examples: 127686
- name: test
num_bytes: 473958
num_examples: 10000
- config_name: dan-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 49171909
num_examples: 137686
- config_name: dan-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 10733277
num_examples: 137686
- config_name: deu-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 42959189
num_examples: 881201
- name: test
num_bytes: 487440
num_examples: 10000
- config_name: deu-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 377457585
num_examples: 891201
- config_name: deu-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 72730983
num_examples: 891201
- config_name: eng-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 266487037
num_examples: 5268725
- name: test
num_bytes: 505718
num_examples: 10000
- config_name: eng-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 1772481467
num_examples: 5278725
- config_name: eng-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 394021606
num_examples: 5278725
- config_name: fas-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 10417693
num_examples: 216940
- name: test
num_bytes: 480147
num_examples: 10000
- config_name: fas-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 240471393
num_examples: 226940
- config_name: fas-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 57867968
num_examples: 226940
- config_name: fra-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 27197426
num_examples: 559505
- name: test
num_bytes: 486099
num_examples: 10000
- config_name: fra-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 256564231
num_examples: 569505
- config_name: fra-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 51751140
num_examples: 569505
- config_name: hin-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 4211543
num_examples: 90031
- name: test
num_bytes: 467756
num_examples: 10000
- config_name: hin-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 87202578
num_examples: 100031
- config_name: hin-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 24557386
num_examples: 100031
- config_name: ind-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 4762307
num_examples: 101315
- name: test
num_bytes: 469908
num_examples: 10000
- config_name: ind-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 32240964
num_examples: 111315
- config_name: ind-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 8791501
num_examples: 111315
- config_name: ita-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 11928808
num_examples: 247803
- name: test
num_bytes: 481319
num_examples: 10000
- config_name: ita-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 96693889
num_examples: 257803
- config_name: ita-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 21887337
num_examples: 257803
- config_name: jpn-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 14443770
num_examples: 299157
- name: test
num_bytes: 482703
num_examples: 10000
- config_name: jpn-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 254914767
num_examples: 309157
- config_name: jpn-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 52646303
num_examples: 309157
- config_name: kor-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 4307606
num_examples: 92000
- name: test
num_bytes: 468174
num_examples: 10000
- config_name: kor-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 65463396
num_examples: 102000
- config_name: kor-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 14462715
num_examples: 102000
- config_name: nld-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 17456195
num_examples: 360662
- name: test
num_bytes: 484023
num_examples: 10000
- config_name: nld-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 134247494
num_examples: 370662
- config_name: nld-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 27592780
num_examples: 370662
- config_name: pol-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 8732582
num_examples: 182515
- name: test
num_bytes: 478433
num_examples: 10000
- config_name: pol-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 83829979
num_examples: 192515
- config_name: pol-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 17279177
num_examples: 192515
- config_name: por-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 9556791
num_examples: 199353
- name: test
num_bytes: 479286
num_examples: 10000
- config_name: por-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 80179713
num_examples: 209353
- config_name: por-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 17117819
num_examples: 209353
- config_name: rus-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 18281224
num_examples: 377504
- name: test
num_bytes: 484252
num_examples: 10000
- config_name: rus-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 612916055
num_examples: 387504
- config_name: rus-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 117356334
num_examples: 387504
- config_name: spa-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 28919818
num_examples: 594661
- name: test
num_bytes: 486351
num_examples: 10000
- config_name: spa-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 240959272
num_examples: 604661
- config_name: spa-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 54894661
num_examples: 604661
- config_name: swe-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 7079817
num_examples: 148738
- name: test
num_bytes: 476125
num_examples: 10000
- config_name: swe-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 59133680
num_examples: 158738
- config_name: swe-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 12773304
num_examples: 158738
- config_name: tur-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 6400585
num_examples: 134846
- name: test
num_bytes: 474649
num_examples: 10000
- config_name: tur-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 57145253
num_examples: 144846
- config_name: tur-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 11816043
num_examples: 144846
- config_name: vie-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 5380433
num_examples: 113972
- name: test
num_bytes: 471975
num_examples: 10000
- config_name: vie-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 76390471
num_examples: 123972
- config_name: vie-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 16076620
num_examples: 123972
- config_name: zho-qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 5796592
num_examples: 122491
- name: test
num_bytes: 473247
num_examples: 10000
- config_name: zho-corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 79790293
num_examples: 132491
- config_name: zho-queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 15738014
num_examples: 132491
configs:
- config_name: ara-qrels
data_files:
- split: train
path: ara/train.jsonl
- split: test
path: ara/test.jsonl
- config_name: ara-corpus
data_files:
- split: corpus
path: ara/corpus.jsonl
- config_name: ara-queries
data_files:
- split: queries
path: ara/queries.jsonl
- config_name: dan-qrels
data_files:
- split: train
path: dan/train.jsonl
- split: test
path: dan/test.jsonl
- config_name: dan-corpus
data_files:
- split: corpus
path: dan/corpus.jsonl
- config_name: dan-queries
data_files:
- split: queries
path: dan/queries.jsonl
- config_name: deu-qrels
data_files:
- split: train
path: deu/train.jsonl
- split: test
path: deu/test.jsonl
- config_name: deu-corpus
data_files:
- split: corpus
path: deu/corpus.jsonl
- config_name: deu-queries
data_files:
- split: queries
path: deu/queries.jsonl
- config_name: eng-qrels
data_files:
- split: train
path: eng/train.jsonl
- split: test
path: eng/test.jsonl
- config_name: eng-corpus
data_files:
- split: corpus
path: eng/corpus.jsonl
- config_name: eng-queries
data_files:
- split: queries
path: eng/queries.jsonl
- config_name: fas-qrels
data_files:
- split: train
path: fas/train.jsonl
- split: test
path: fas/test.jsonl
- config_name: fas-corpus
data_files:
- split: corpus
path: fas/corpus.jsonl
- config_name: fas-queries
data_files:
- split: queries
path: fas/queries.jsonl
- config_name: fra-qrels
data_files:
- split: train
path: fra/train.jsonl
- split: test
path: fra/test.jsonl
- config_name: fra-corpus
data_files:
- split: corpus
path: fra/corpus.jsonl
- config_name: fra-queries
data_files:
- split: queries
path: fra/queries.jsonl
- config_name: hin-qrels
data_files:
- split: train
path: hin/train.jsonl
- split: test
path: hin/test.jsonl
- config_name: hin-corpus
data_files:
- split: corpus
path: hin/corpus.jsonl
- config_name: hin-queries
data_files:
- split: queries
path: hin/queries.jsonl
- config_name: ind-qrels
data_files:
- split: train
path: ind/train.jsonl
- split: test
path: ind/test.jsonl
- config_name: ind-corpus
data_files:
- split: corpus
path: ind/corpus.jsonl
- config_name: ind-queries
data_files:
- split: queries
path: ind/queries.jsonl
- config_name: ita-qrels
data_files:
- split: train
path: ita/train.jsonl
- split: test
path: ita/test.jsonl
- config_name: ita-corpus
data_files:
- split: corpus
path: ita/corpus.jsonl
- config_name: ita-queries
data_files:
- split: queries
path: ita/queries.jsonl
- config_name: jpn-qrels
data_files:
- split: train
path: jpn/train.jsonl
- split: test
path: jpn/test.jsonl
- config_name: jpn-corpus
data_files:
- split: corpus
path: jpn/corpus.jsonl
- config_name: jpn-queries
data_files:
- split: queries
path: jpn/queries.jsonl
- config_name: kor-qrels
data_files:
- split: train
path: kor/train.jsonl
- split: test
path: kor/test.jsonl
- config_name: kor-corpus
data_files:
- split: corpus
path: kor/corpus.jsonl
- config_name: kor-queries
data_files:
- split: queries
path: kor/queries.jsonl
- config_name: nld-qrels
data_files:
- split: train
path: nld/train.jsonl
- split: test
path: nld/test.jsonl
- config_name: nld-corpus
data_files:
- split: corpus
path: nld/corpus.jsonl
- config_name: nld-queries
data_files:
- split: queries
path: nld/queries.jsonl
- config_name: pol-qrels
data_files:
- split: train
path: pol/train.jsonl
- split: test
path: pol/test.jsonl
- config_name: pol-corpus
data_files:
- split: corpus
path: pol/corpus.jsonl
- config_name: pol-queries
data_files:
- split: queries
path: pol/queries.jsonl
- config_name: por-qrels
data_files:
- split: train
path: por/train.jsonl
- split: test
path: por/test.jsonl
- config_name: por-corpus
data_files:
- split: corpus
path: por/corpus.jsonl
- config_name: por-queries
data_files:
- split: queries
path: por/queries.jsonl
- config_name: rus-qrels
data_files:
- split: train
path: rus/train.jsonl
- split: test
path: rus/test.jsonl
- config_name: rus-corpus
data_files:
- split: corpus
path: rus/corpus.jsonl
- config_name: rus-queries
data_files:
- split: queries
path: rus/queries.jsonl
- config_name: spa-qrels
data_files:
- split: train
path: spa/train.jsonl
- split: test
path: spa/test.jsonl
- config_name: spa-corpus
data_files:
- split: corpus
path: spa/corpus.jsonl
- config_name: spa-queries
data_files:
- split: queries
path: spa/queries.jsonl
- config_name: swe-qrels
data_files:
- split: train
path: swe/train.jsonl
- split: test
path: swe/test.jsonl
- config_name: swe-corpus
data_files:
- split: corpus
path: swe/corpus.jsonl
- config_name: swe-queries
data_files:
- split: queries
path: swe/queries.jsonl
- config_name: tur-qrels
data_files:
- split: train
path: tur/train.jsonl
- split: test
path: tur/test.jsonl
- config_name: tur-corpus
data_files:
- split: corpus
path: tur/corpus.jsonl
- config_name: tur-queries
data_files:
- split: queries
path: tur/queries.jsonl
- config_name: vie-qrels
data_files:
- split: train
path: vie/train.jsonl
- split: test
path: vie/test.jsonl
- config_name: vie-corpus
data_files:
- split: corpus
path: vie/corpus.jsonl
- config_name: vie-queries
data_files:
- split: queries
path: vie/queries.jsonl
- config_name: zho-qrels
data_files:
- split: train
path: zho/train.jsonl
- split: test
path: zho/test.jsonl
- config_name: zho-corpus
data_files:
- split: corpus
path: zho/corpus.jsonl
- config_name: zho-queries
data_files:
- split: queries
path: zho/queries.jsonl
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 20 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 20 high-resource languages from the original WebFAQ corpus, each comprising tens of thousands to hundreds of thousands of QA pairs after our rigorous filtering steps:
| Language | # QA pairs |
|---|---|
| ara | 143k |
| dan | 138k |
| deu | 891k |
| eng | 5.28M |
| fas | 227k |
| fra | 570k |
| hin | 96.6k |
| ind | 96.6k |
| ita | 209k |
| jpn | 280k |
| kor | 79.1k |
| nld | 349k |
| pol | 179k |
| por | 186k |
| rus | 346k |
| spa | 558k |
| swe | 144k |
| tur | 110k |
| vie | 105k |
| zho | 125k |
Structure
Unlike the raw Q&A dataset, WebFAQ Retrieval provides explicit train/test splits for each of the 20 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(
"anonymous202501/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(
"anonymous202501/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(
"anonymous202501/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:
Note: WebFAQ is not yet available as multilingual task in the
mteblibrary. The code snippet below is a placeholder for when it becomes available.
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{webfaq2025,
title = {WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval},
author = {Anonymous Author(s)},
year = {2025},
howpublished = {...},
note = {Under review}
}
Contact
TBD
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!