Update README.md

README.md

@@ -14,4 +14,87 @@ configs:
       - split: queries
         path: queries.jsonl
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+# 📚 Translated LONG2RAG (MTEB-Style Retrieval Dataset)
+
+## Dataset Summary
+
+This dataset is a **translated version** of the [LONG2RAG benchmark](https://github.com/QZH-777/longrag) (Qi et al., EMNLP Findings 2024), adapted into **MTEB-style retrieval format** for evaluating multilingual **retrieval-augmented generation (RAG)** and **long-context retrieval** systems.
+
+LONG2RAG was originally designed to evaluate how well large language models (LLMs) incorporate key points from retrieved long documents into long-form answers. It includes **280 complex, practical questions** across **10 domains** and **8 question categories**, each paired with **5 retrieved documents** (avg. length ~2,444 words).
+
+This translated version preserves the structure but reformats it into **query–document relevance pairs** suitable for **retrieval evaluation** under the [Massive Text Embedding Benchmark (MTEB)](https://huggingface.co/collections/mteb/mteb-benchmark-63f5f98f79c33120b8f94d1d).
+
 ---
+
+## Supported Tasks and Leaderboards
+
+* **Task Category:** Retrieval  
+* **Task:** Given a natural language query, rank candidate documents by relevance.  
+* **MTEB Integration:** Compatible with `mteb` evaluation framework.  
+
+---
+
+## Languages
+
+* **Original:** English  
+* **This release:** Translated into Persian  
+
+---
+
+
+## Dataset Details
+
+### Queries
+- **280** complex, uncontaminated, long-form questions.  
+
+### Corpus
+- Retrieved real-world documents (**5 per query**).  
+
+### Relevance Labels
+- Binary (**relevant / not relevant**).  
+
+---
+
+## Domains and Question Categories
+
+### Domains (10)
+- AI  
+- Biology  
+- Economics  
+- Film  
+- History  
+- Music  
+- Religion  
+- Sports  
+- Technology  
+- Others  
+
+### Question Categories (8)
+- Factual  
+- Explanatory  
+- Comparative  
+- Subjective  
+- Methodological  
+- Causal  
+- Hypothetical  
+- Predictive  
+
+---
+
+## Data Splits
+
+- **test**: 280 queries  
+
+Each query has **5 candidate documents**, aligned with **MTEB retrieval style**.  
+
+---
+
+## Citation
+
+```bibtex
+@inproceedings{qi2024long2rag,
+  title = {LONG2RAG: Evaluating Long-Context \& Long-Form Retrieval-Augmented Generation with Key Point Recall},
+  author = {Qi, Zehan and Xu, Rongwu and Guo, Zhijiang and Wang, Cunxiang and Zhang, Hao and Xu, Wei},
+  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
+  year = {2024}
+}