--- 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: 5572666 num_examples: 117911 - name: test num_bytes: 472753 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: 136311591 num_examples: 127911 - config_name: ara-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 30402843 num_examples: 127911 - config_name: dan-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 5471013 num_examples: 115828 - name: test num_bytes: 472339 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: 43223104 num_examples: 125828 - config_name: dan-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 9792361 num_examples: 125828 - config_name: deu-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 37881032 num_examples: 777560 - name: test num_bytes: 487188 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: 319488602 num_examples: 787560 - config_name: deu-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 64439284 num_examples: 787560 - config_name: eng-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 254665724 num_examples: 5036931 - name: test num_bytes: 505537 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: 1647704737 num_examples: 5046931 - config_name: eng-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 372512689 num_examples: 5046931 - config_name: fas-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 9667341 num_examples: 201613 - name: test num_bytes: 479476 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: 218442091 num_examples: 211613 - config_name: fas-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 53916822 num_examples: 211613 - config_name: fra-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 23301349 num_examples: 479980 - name: test num_bytes: 485451 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: 213443967 num_examples: 489980 - config_name: fra-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 44403290 num_examples: 489980 - config_name: hin-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 4067150 num_examples: 86960 - name: test num_bytes: 451911 num_examples: 9663 - config_name: hin-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 82805164 num_examples: 96623 - config_name: hin-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 23711443 num_examples: 96623 - config_name: ind-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 4065572 num_examples: 86927 - name: test num_bytes: 451750 num_examples: 9659 - config_name: ind-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 26841405 num_examples: 96586 - config_name: ind-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 7488043 num_examples: 96586 - config_name: ita-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 9562584 num_examples: 199473 - name: test num_bytes: 479373 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: 74440906 num_examples: 209473 - config_name: ita-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 16452589 num_examples: 209473 - config_name: jpn-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 13015424 num_examples: 269994 - name: test num_bytes: 482062 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: 216075162 num_examples: 279994 - config_name: jpn-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 45851068 num_examples: 279994 - config_name: kor-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 3326414 num_examples: 71201 - name: test num_bytes: 369677 num_examples: 7912 - config_name: kor-corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 52807208 num_examples: 79113 - config_name: kor-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 11729767 num_examples: 79113 - config_name: nld-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 16378495 num_examples: 338659 - name: test num_bytes: 483576 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: 122713729 num_examples: 348659 - config_name: nld-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 25938043 num_examples: 348659 - config_name: pol-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 8092330 num_examples: 169430 - name: test num_bytes: 477520 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: 75311194 num_examples: 179430 - config_name: pol-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 15744703 num_examples: 179430 - config_name: por-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 8395879 num_examples: 175636 - name: test num_bytes: 478065 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: 68385326 num_examples: 185636 - config_name: por-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 14959035 num_examples: 185636 - config_name: rus-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 16265022 num_examples: 336342 - name: test num_bytes: 483516 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: 522818422 num_examples: 346342 - config_name: rus-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 100208739 num_examples: 346342 - config_name: spa-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 26619246 num_examples: 547706 - name: test num_bytes: 486128 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: 210715406 num_examples: 557706 - config_name: spa-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 49406268 num_examples: 557706 - config_name: swe-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 6353222 num_examples: 133876 - name: test num_bytes: 474482 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: 52307117 num_examples: 143876 - config_name: swe-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 11711417 num_examples: 143876 - config_name: tur-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 4710629 num_examples: 100259 - name: test num_bytes: 469842 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: 45037131 num_examples: 110259 - config_name: tur-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 9463426 num_examples: 110259 - config_name: vie-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 4476944 num_examples: 95470 - name: test num_bytes: 468866 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: 63033970 num_examples: 105470 - config_name: vie-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 13161075 num_examples: 105470 - config_name: zho-qrels features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 5451162 num_examples: 115421 - name: test num_bytes: 472247 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: 73030146 num_examples: 125421 - config_name: zho-queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 14796193 num_examples: 125421 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](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{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!