---
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)](https://huggingface.co/spaces/mteb/leaderboard), 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