MCINext/msmarco-fa

Dataset Summary

MSMARCO-Fa is a Persian (Farsi) dataset created for the Retrieval task, particularly focusing on web search and document ranking. It is a translated version of the original English MS MARCO (Microsoft MAchine Reading COmprehension) dataset and is a key part of the FaMTEB (Farsi Massive Text Embedding Benchmark), under the BEIR-Fa collection.

• Language(s): Persian (Farsi)
• Task(s): Retrieval (Web Search, Document Ranking)
• Source: Translated from the English MS MARCO dataset
• Part of FaMTEB: Yes — under BEIR-Fa

Supported Tasks and Leaderboards

This dataset is used to evaluate the effectiveness of text embedding models in ranking web documents based on relevance to user queries, simulating real-world search engine applications. Benchmarking is available via the Persian MTEB Leaderboard (language: Persian).

Construction

The dataset was built by:

• Translating the original English MS MARCO dataset to Persian using the Google Translate API
• Preserving original relevance annotations, where some passages are human-judged as relevant to each query

As described in the FaMTEB paper:

• Translation quality was evaluated by BM25 retrieval score comparison with the English dataset
• Further validation was done using LLM-based assessments (GEMBA-DA framework)
• This dataset is similar in structure to mMARCO, but focused solely on the Persian language

Data Splits

Based on FaMTEB paper (Table 5):

• Train: 9,374,574 samples
• Dev: 0 samples
• Test: 8,845,925 samples

Total: ~9.9 million examples