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), 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)