MCINext/persian-web-document-retrieval

Dataset Summary

Persian Web Document Retrieval is a Persian (Farsi) dataset designed for the Retrieval task. It is a component of the FaMTEB (Farsi Massive Text Embedding Benchmark). This dataset consists of real-world queries collected from the Zarrebin search engine and web documents labeled by humans for relevance. It is curated to evaluate model performance in web search scenarios.

• Language(s): Persian (Farsi)
• Task(s): Retrieval (Web Search)
• Source: Collected from Zarrebin search engine logs and human-labeled documents
• Part of FaMTEB: Yes

Supported Tasks and Leaderboards

The dataset benchmarks how well text embedding models can retrieve relevant web documents in response to real user queries in Persian. This is crucial for search engines and information access systems. Results can be explored on the Persian MTEB Leaderboard (filter by language: Persian).

Construction

1. Real search queries were sourced from Zarrebin, a major Persian-language search engine.
2. Relevant documents were retrieved and manually labeled for query-document relevance.
3. The final dataset reflects authentic web search behavior and document diversity in Persian.
4. The dataset is referenced in the FaMTEB paper as “Davani et al., 2023” ([Paper ID: 10553090]).

Data Splits

• Train: 245,692 samples
• Development (Dev): 0 samples
• Test: 175,472 samples