metadata
configs:
- config_name: default
data_files:
- split: train
path: qrels/train.jsonl
- split: dev
path: qrels/dev.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
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