--- 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: 6293949 num_examples: 132664 - name: test num_bytes: 474367 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: train num_bytes: 32958434 num_examples: 132664 - name: test num_bytes: 2500510 num_examples: 10000 - config_name: dan-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 6050475 num_examples: 127686 - name: test num_bytes: 473919 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: train num_bytes: 9957312 num_examples: 127686 - name: test num_bytes: 775965 num_examples: 10000 - config_name: deu-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 42959068 num_examples: 881201 - name: test num_bytes: 487561 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: train num_bytes: 71911852 num_examples: 881201 - name: test num_bytes: 819131 num_examples: 10000 - config_name: eng-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 266487011 num_examples: 5268725 - name: test num_bytes: 505744 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: train num_bytes: 393277327 num_examples: 5268725 - name: test num_bytes: 744279 num_examples: 10000 - config_name: fas-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 10417722 num_examples: 216940 - name: test num_bytes: 480118 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: train num_bytes: 55308380 num_examples: 216940 - name: test num_bytes: 2559588 num_examples: 10000 - config_name: fra-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 27197413 num_examples: 559505 - name: test num_bytes: 486112 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: train num_bytes: 50838219 num_examples: 559505 - name: test num_bytes: 912921 num_examples: 10000 - config_name: hin-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 4211562 num_examples: 90031 - name: test num_bytes: 467737 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: train num_bytes: 22108215 num_examples: 90031 - name: test num_bytes: 2449171 num_examples: 10000 - config_name: ind-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 4762169 num_examples: 101315 - name: test num_bytes: 470046 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: train num_bytes: 8003845 num_examples: 101315 - name: test num_bytes: 787656 num_examples: 10000 - config_name: ita-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 11928821 num_examples: 247803 - name: test num_bytes: 481306 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: train num_bytes: 21034519 num_examples: 247803 - name: test num_bytes: 852818 num_examples: 10000 - config_name: jpn-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 14443765 num_examples: 299157 - name: test num_bytes: 482708 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: train num_bytes: 50949827 num_examples: 299157 - name: test num_bytes: 1696476 num_examples: 10000 - config_name: kor-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 4307545 num_examples: 92000 - name: test num_bytes: 468235 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: train num_bytes: 13051430 num_examples: 92000 - name: test num_bytes: 1411285 num_examples: 10000 - config_name: nld-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 17456124 num_examples: 360662 - name: test num_bytes: 484094 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: train num_bytes: 26845652 num_examples: 360662 - name: test num_bytes: 747128 num_examples: 10000 - config_name: pol-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 8732406 num_examples: 182515 - name: test num_bytes: 478609 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: train num_bytes: 16387616 num_examples: 182515 - name: test num_bytes: 891561 num_examples: 10000 - config_name: por-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 9556659 num_examples: 199353 - name: test num_bytes: 479418 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: train num_bytes: 16301318 num_examples: 199353 - name: test num_bytes: 816501 num_examples: 10000 - config_name: rus-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 18281176 num_examples: 377504 - name: test num_bytes: 484300 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: train num_bytes: 114319660 num_examples: 377504 - name: test num_bytes: 3036674 num_examples: 10000 - config_name: spa-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 28919803 num_examples: 594661 - name: test num_bytes: 486366 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: train num_bytes: 53980948 num_examples: 594661 - name: test num_bytes: 913713 num_examples: 10000 - config_name: swe-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 7079762 num_examples: 148738 - name: test num_bytes: 476180 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: train num_bytes: 11970053 num_examples: 148738 - name: test num_bytes: 803251 num_examples: 10000 - config_name: tur-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 6400507 num_examples: 134846 - name: test num_bytes: 474727 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: train num_bytes: 11003660 num_examples: 134846 - name: test num_bytes: 812383 num_examples: 10000 - config_name: vie-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 5380320 num_examples: 113972 - name: test num_bytes: 472088 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: train num_bytes: 14776653 num_examples: 113972 - name: test num_bytes: 1299967 num_examples: 10000 - config_name: zho-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 5796595 num_examples: 122491 - name: test num_bytes: 473244 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: train num_bytes: 14554815 num_examples: 122491 - name: test num_bytes: 1183199 num_examples: 10000 configs: - config_name: ara-qrels data_files: - split: train path: ara/qrels-train.jsonl - split: test path: ara/qrels-test.jsonl - config_name: ara-corpus data_files: - split: corpus path: ara/corpus.jsonl - config_name: ara-queries data_files: - split: train path: ara/queries-train.jsonl - split: test path: ara/queries-test.jsonl - config_name: dan-qrels data_files: - split: train path: dan/qrels-train.jsonl - split: test path: dan/qrels-test.jsonl - config_name: dan-corpus data_files: - split: corpus path: dan/corpus.jsonl - config_name: dan-queries data_files: - split: train path: dan/queries-train.jsonl - split: test path: dan/queries-test.jsonl - config_name: deu-qrels data_files: - split: train path: deu/qrels-train.jsonl - split: test path: deu/qrels-test.jsonl - config_name: deu-corpus data_files: - split: corpus path: deu/corpus.jsonl - config_name: deu-queries data_files: - split: train path: deu/queries-train.jsonl - split: test path: deu/queries-test.jsonl - config_name: eng-qrels data_files: - split: train path: eng/qrels-train.jsonl - split: test path: eng/qrels-test.jsonl - config_name: eng-corpus data_files: - split: corpus path: eng/corpus.jsonl - config_name: eng-queries data_files: - split: train path: eng/queries-train.jsonl - split: test path: eng/queries-test.jsonl - config_name: fas-qrels data_files: - split: train path: fas/qrels-train.jsonl - split: test path: fas/qrels-test.jsonl - config_name: fas-corpus data_files: - split: corpus path: fas/corpus.jsonl - config_name: fas-queries data_files: - split: train path: fas/queries-train.jsonl - split: test path: fas/queries-test.jsonl - config_name: fra-qrels data_files: - split: train path: fra/qrels-train.jsonl - split: test path: fra/qrels-test.jsonl - config_name: fra-corpus data_files: - split: corpus path: fra/corpus.jsonl - config_name: fra-queries data_files: - split: train path: fra/queries-train.jsonl - split: test path: fra/queries-test.jsonl - config_name: hin-qrels data_files: - split: train path: hin/qrels-train.jsonl - split: test path: hin/qrels-test.jsonl - config_name: hin-corpus data_files: - split: corpus path: hin/corpus.jsonl - config_name: hin-queries data_files: - split: train path: hin/queries-train.jsonl - split: test path: hin/queries-test.jsonl - config_name: ind-qrels data_files: - split: train path: ind/qrels-train.jsonl - split: test path: ind/qrels-test.jsonl - config_name: ind-corpus data_files: - split: corpus path: ind/corpus.jsonl - config_name: ind-queries data_files: - split: train path: ind/queries-train.jsonl - split: test path: ind/queries-test.jsonl - config_name: ita-qrels data_files: - split: train path: ita/qrels-train.jsonl - split: test path: ita/qrels-test.jsonl - config_name: ita-corpus data_files: - split: corpus path: ita/corpus.jsonl - config_name: ita-queries data_files: - split: train path: ita/queries-train.jsonl - split: test path: ita/queries-test.jsonl - config_name: jpn-qrels data_files: - split: train path: jpn/qrels-train.jsonl - split: test path: jpn/qrels-test.jsonl - config_name: jpn-corpus data_files: - split: corpus path: jpn/corpus.jsonl - config_name: jpn-queries data_files: - split: train path: jpn/queries-train.jsonl - split: test path: jpn/queries-test.jsonl - config_name: kor-qrels data_files: - split: train path: kor/qrels-train.jsonl - split: test path: kor/qrels-test.jsonl - config_name: kor-corpus data_files: - split: corpus path: kor/corpus.jsonl - config_name: kor-queries data_files: - split: train path: kor/queries-train.jsonl - split: test path: kor/queries-test.jsonl - config_name: nld-qrels data_files: - split: train path: nld/qrels-train.jsonl - split: test path: nld/qrels-test.jsonl - config_name: nld-corpus data_files: - split: corpus path: nld/corpus.jsonl - config_name: nld-queries data_files: - split: train path: nld/queries-train.jsonl - split: test path: nld/queries-test.jsonl - config_name: pol-qrels data_files: - split: train path: pol/qrels-train.jsonl - split: test path: pol/qrels-test.jsonl - config_name: pol-corpus data_files: - split: corpus path: pol/corpus.jsonl - config_name: pol-queries data_files: - split: train path: pol/queries-train.jsonl - split: test path: pol/queries-test.jsonl - config_name: por-qrels data_files: - split: train path: por/qrels-train.jsonl - split: test path: por/qrels-test.jsonl - config_name: por-corpus data_files: - split: corpus path: por/corpus.jsonl - config_name: por-queries data_files: - split: train path: por/queries-train.jsonl - split: test path: por/queries-test.jsonl - config_name: rus-qrels data_files: - split: train path: rus/qrels-train.jsonl - split: test path: rus/qrels-test.jsonl - config_name: rus-corpus data_files: - split: corpus path: rus/corpus.jsonl - config_name: rus-queries data_files: - split: train path: rus/queries-train.jsonl - split: test path: rus/queries-test.jsonl - config_name: spa-qrels data_files: - split: train path: spa/qrels-train.jsonl - split: test path: spa/qrels-test.jsonl - config_name: spa-corpus data_files: - split: corpus path: spa/corpus.jsonl - config_name: spa-queries data_files: - split: train path: spa/queries-train.jsonl - split: test path: spa/queries-test.jsonl - config_name: swe-qrels data_files: - split: train path: swe/qrels-train.jsonl - split: test path: swe/qrels-test.jsonl - config_name: swe-corpus data_files: - split: corpus path: swe/corpus.jsonl - config_name: swe-queries data_files: - split: train path: swe/queries-train.jsonl - split: test path: swe/queries-test.jsonl - config_name: tur-qrels data_files: - split: train path: tur/qrels-train.jsonl - split: test path: tur/qrels-test.jsonl - config_name: tur-corpus data_files: - split: corpus path: tur/corpus.jsonl - config_name: tur-queries data_files: - split: train path: tur/queries-train.jsonl - split: test path: tur/queries-test.jsonl - config_name: vie-qrels data_files: - split: train path: vie/qrels-train.jsonl - split: test path: vie/qrels-test.jsonl - config_name: vie-corpus data_files: - split: corpus path: vie/corpus.jsonl - config_name: vie-queries data_files: - split: train path: vie/queries-train.jsonl - split: test path: vie/queries-test.jsonl - config_name: zho-qrels data_files: - split: train path: zho/qrels-train.jsonl - split: test path: zho/qrels-test.jsonl - config_name: zho-corpus data_files: - split: corpus path: zho/corpus.jsonl - config_name: zho-queries data_files: - split: train path: zho/queries-train.jsonl - split: test path: zho/queries-test.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](https://huggingface.co/datasets/anonymous202501/webfaq). 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](https://github.com/huggingface/datasets): ```python 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 `mteb` library. The code snippet below is a placeholder for when it becomes available. ```python 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)**](https://creativecommons.org/licenses/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](https://commoncrawl.org/terms-of-use/). ## Citation If you use this dataset in your research, please consider citing the associated paper: ```bibtex @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](mailto: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!