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configs:
- config_name: default
data_files:
- 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)
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