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README.md

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+<h1 align="center">WebFAQ 2.0: Multilingual FAQ Q&A Dataset with Hard Negatives</h1>
+
+<h4 align="center">
+   <p>
+       <a href=#overview>Overview</a> |
+       <a href=#whats-new-in-v20>What's New in v2.0</a> |
+       <a href=#dataset-statistics>Dataset Statistics</a> |
+       <a href=#structure>Structure</a> |
+       <a href=#bilingual-alignments>Bitext Alignments</a> |
+       <a href=#hard-negatives-dataset>Hard Negatives</a> |
+       <a href=#training-strategies>Training Strategies</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>
+   <p>
+</h4>
+
+---
+
+## Overview
+
+**WebFAQ 2.0** is a large-scale multilingual dataset of **198 million natural question–answer pairs** across **104 languages**, mined from structured FAQ pages on the web.
+
+It is the successor of the original **WebFAQ (v1)** dataset (96M QAs, 75 languages):
+👉 [https://huggingface.co/datasets/PaDaS-Lab/webfaq](https://huggingface.co/datasets/PaDaS-Lab/webfaq)
+
+WebFAQ 2.0 significantly expands multilingual coverage, bilingual QA alignments, and introduces a new **hard negatives dataset** for training dense retrieval models .
+
+The dataset is designed for:
+
+* 🌍 Multilingual & cross-lingual dense retrieval
+* ❓ Open-domain Question Answering
+* 🔎 Hard negative mining research
+* 🌐 Bitext mining & multilingual embedding training
+
+---
+
+## What's New in v2.0
+
+Compared to WebFAQ v1, version 2.0 introduces:
+
+### 1️⃣ Massive Scale Increase
+
+* **198M QA pairs** (vs. 96M in v1)
+* **104 languages** (vs. 75)
+* English share reduced from ~51% to ~28% → stronger multilingual balance
+
+### 2️⃣ New Crawling Strategy
+
+Instead of relying solely on Web Data Commons dumps, WebFAQ 2.0:
+
+* Mines FAQPage markup directly from Common Crawl
+* Crawls pages using OWLer (OpenWebSearch project)
+* Extracts:
+
+  * Questions
+  * Answers
+  * Page titles
+  * Page descriptions
+  * hreflang links for multilingual alignment
+
+This yields richer contextual metadata and improved cross-lingual coverage .
+
+### 3️⃣ Large-Scale Bilingual Alignments
+
+* **13.8M aligned QA pairs**
+* **3,118 language combinations**
+* 1,282 language pairs contain ≥4,000 aligned samples
+
+This is a nearly 10× increase over v1.
+
+### 4️⃣ Hard Negatives Dataset
+
+A new dataset of:
+
+* **1.25M queries**
+* **20 languages**
+* 200 mined hard negatives per query
+* Cross-encoder scores included
+
+Designed specifically for dense retriever training .
+
+---
+
+## Dataset Statistics
+
+### Languages
+
+* 104 languages with ≥1,000 samples
+* More balanced distribution compared to v1
+* Significant growth in non-English languages (e.g., Hindi, Ukrainian, Dutch, Portuguese, Polish)
+
+### Topics
+
+Topic labels are inferred using a fine-tuned XLM-RoBERTa classifier trained on GPT-5-mini annotated data (F1 ≈ 88%).
+
+Major topic shift in v2.0:
+
+| Topic                             | %    |
+| --------------------------------- | ---- |
+| ✈️ Traveling & Hospitality        | 59.2 |
+| 🛒 Products & Commercial Services | 18.9 |
+| ❤️ Healthcare & Lifestyle         | 5.6  |
+| 🎵 Entertainment                  | 4.7  |
+| 🏦 Banking & Finance              | 4.4  |
+
+### Question Types
+
+Questions are categorized using a multilingual extension of the Bolotova et al. taxonomy:
+
+| Type           | %    |
+| -------------- | ---- |
+| Not-a-Question | 34.7 |
+| Factoid        | 33.6 |
+| Experience     | 11.6 |
+| Instruction    | 10.3 |
+| Evidence-Based | 4.3  |
+| Reason         | 3.3  |
+| Comparison     | 2.2  |
+| Debate         | 0.1  |
+
+Classifier F1 ≈ 88%.
+
+---
+
+## Structure
+
+Each language is provided as a separate configuration:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("michaeldinzinger/webfaq-v2", "eng")["default"]
+print(dataset[0])
+```
+
+### Fields
+
+* `id`: Unique identifier
+* `origin`: Website origin (scheme + host)
+* `url`: Source URL
+* `question`: Natural-language question
+* `answer`: Corresponding answer
+* `title`: Webpage title (new in v2.0)
+* `description`: Webpage description (new in v2.0)
+* `qa_similarity_score`: Semantic similarity between question and answer
+* `topic` (optional): Topic label
+* `question_type` (optional): Question category
+
+> Note: No official train/validation/test split is provided for the raw QA dataset.
+
+---
+
+## Bilingual Alignments
+
+The bitext dataset is released separately:
+
+👉 [https://huggingface.co/datasets/michaeldinzinger/webfaq-v2-bitexts](https://huggingface.co/datasets/michaeldinzinger/webfaq-v2-bitexts)
+
+Alignment method:
+
+* LaBSE embeddings
+* Nearest-neighbor retrieval
+* Similarity threshold ≥ 0.9
+
+Results:
+
+* 13.8M aligned QA pairs
+* Strong growth in non-English language pairs
+* Includes underrepresented combinations (e.g., Marathi–Telugu)
+
+---
+
+## Hard Negatives Dataset
+
+Released separately:
+
+👉 [https://huggingface.co/datasets/IrvinTopi/WebFAQHardNegatives](https://huggingface.co/datasets/IrvinTopi/WebFAQHardNegatives)
+
+### Mining Pipeline
+
+Two-stage approach :
+
+1. BM25 retrieval (top 200 candidates)
+2. Cross-encoder reranking using BGE-m3
+
+Each instance contains:
+
+```
+(query, positive, pos_score, negatives[], neg_scores[])
+```
+
+Total size:
+
+* 1.25M training samples
+* 20 languages
+* 200 negatives per query
+
+---
+
+## Training Strategies Enabled
+
+WebFAQ 2.0 supports two main dense retrieval training paradigms:
+
+### 🔹 Contrastive Learning
+
+* MultipleNegativesRankingLoss (MNR)
+* Random negatives
+* Hard negatives (Top-k or denoised)
+
+### 🔹 Knowledge Distillation
+
+* MarginMSE loss
+* Uses cross-encoder scores directly
+* Strong improvements in non-English retrieval
+
+Experiments show:
+
+* Random negatives remain surprisingly strong in contrastive setups.
+* Knowledge distillation improves multilingual robustness but may slightly reduce English performance .
+
+---
+
+## Examples
+
+```json
+{
+  "id": "example_id",
+  "origin": "https://example.com",
+  "url": "https://example.com/faq",
+  "question": "How old do I have to be to rent a car in Girona?",
+  "answer": "You must be at least 21 years old to rent a car in Girona.",
+  "title": "Car Rental FAQ – Girona",
+  "description": "Frequently asked questions about renting a car in Girona.",
+  "qa_similarity_score": 0.93,
+  "topic": "Traveling and Hospitality",
+  "question_type": "Experience"
+}
+```
+
+---
+
+## Considerations
+
+* ⚠️ No guarantee of factual correctness
+* ⚠️ Contains duplicates or near-duplicates
+* ⚠️ Hard negatives may contain false negatives
+* ⚠️ Language detection may be imperfect
+* 📜 Derived from public web pages – respect original website terms
+
+---
+
+## License
+
+[**Open Data Commons Attribution License (ODC-By)**](https://opendatacommons.org/licenses/by/)
+
+---
+
+## Citation
+
+If you use WebFAQ 2.0, please cite:
+
+```bibtex
+@inproceedings{dinzinger2025webfaqv2,
+  title={WebFAQ 2.0: A Multilingual QA Dataset with Mined Hard Negatives for Dense Retrieval},
+  author={Dinzinger, Michael and Caspari, Laura and Salman, Ali and Topi, Irvin and Mitrović, Jelena and Granitzer, Michael},
+  year={2025}
+}
+```
+
+You may also cite the original WebFAQ 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:
+
+* [michael.dinzinger@uni-passau.de](mailto:michael.dinzinger@uni-passau.de)
+* HuggingFace Discussions
+* GitHub: [https://github.com/padas-lab-de/webfaq](https://github.com/padas-lab-de/webfaq)
+
+---
+
+WebFAQ 2.0 is part of a long-term effort aligned with the [**Open Web Index**](https://openwebindex.eu/) initiative and will continue to grow as new structured FAQ data becomes available .
+
+Happy researching! 🚀