Update README.md

README.md

@@ -1052,4 +1052,180 @@ configs:
     data_files:
       - split: queries
         path: zho/queries.jsonl
----
+---
+<h1 align="center">WebFAQ Retrieval Dataset</h1>
+<h4 align="center">
+   <p>
+       <a href=#overview>Overview</a> |
+       <a href=#details>Details</a>  |
+       <a href=#structure>Structure</a>  |
+       <a href=#examples>Examples</a> |
+       <a href=#considerations>Considerations</a> |
+       <a href=#license>License</a> |
+       <a href=#citation>Citation</a> |
+       <a href=#contact>Contact</a> |
+       <a href=#acknowledgement>Acknowledgement</a>
+   <p>
+</h4>
+
+## 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     |
+|-------------|----------------|
+| TBD         |                |
+
+## 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!