Create README.md

README.md

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+---
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: qrels/train.jsonl
+      - split: validation
+        path: qrels/validation.jsonl
+      - split: test
+        path: qrels/test.jsonl
+  - config_name: corpus
+    data_files:
+      - split: corpus
+        path: corpus.jsonl
+  - config_name: queries
+    data_files:
+      - split: queries
+        path: queries.jsonl
+---
+## Dataset Summary
+
+**NQ-Fa** is a Persian (Farsi) dataset created for the **Retrieval** task, specifically targeting **open-domain question answering**. It is a **translated version** of the original English **Natural Questions (NQ)** dataset and a central component of the [FaMTEB (Farsi Massive Text Embedding Benchmark)](https://huggingface.co/spaces/mteb/leaderboard), as part of the **BEIR-Fa** collection.
+
+- **Language(s):** Persian (Farsi)  
+- **Task(s):** Retrieval (Question Answering)  
+- **Source:** Translated from English NQ using Google Translate  
+- **Part of FaMTEB:** Yes — under BEIR-Fa
+
+## Supported Tasks and Leaderboards
+
+This dataset evaluates how well **text embedding models** can retrieve relevant answer passages from Persian Wikipedia in response to **natural language questions**, originally issued to Google Search. Results are benchmarked on the **Persian MTEB Leaderboard** on Hugging Face Spaces (language filter: Persian).
+
+## Construction
+
+The construction process included:
+
+- Starting with the **Natural Questions (NQ)** English dataset, containing real user search queries  
+- Using the **Google Translate API** to translate both questions and annotated Wikipedia passages into Persian  
+- Retaining original query-passage mapping structure for retrieval evaluation
+
+As described in the *FaMTEB* paper, all BEIR-Fa datasets (including NQ-Fa) underwent:
+
+- **BM25 retrieval comparison** between English and Persian  
+- **LLM-based translation quality check** using the GEMBA-DA framework  
+
+These evaluations confirmed a **high level of translation quality**.
+
+## Data Splits
+
+Defined in the FaMTEB paper (Table 5):
+
+- **Train:** 0 samples  
+- **Dev:** 0 samples  
+- **Test:** 2,685,669 samples
+
+**Total:** ~2.69 million examples (according to metadata)