---
license: mit
task_categories:
  - text-retrieval
  - question-answering
language:
  - as
  - bn
  - gu
  - hi
  - kn
  - ml
  - mr
  - ne
  - or
  - pa
  - ta
  - te
  - ur
multilinguality: multilingual
size_categories:
  - 10K<n<100K
source_datasets:
  - ms_marco
tags:
  - indian-languages
  - multilingual
  - indic
  - retrieval
  - msmarco
  - benchmark
pretty_name: "IndicMSMARCO: Multilingual Information Retrieval Benchmark"
configs:
  - config_name: as
    data_files:
      - as/*.parquet
    default: false
    description: "Assamese language subset"
  - config_name: bn
    data_files:
      - bn/*.parquet
    default: false
    description: "Bengali language subset"
  - config_name: gu
    data_files:
      - gu/*.parquet
    default: false
    description: "Gujarati language subset"
  - config_name: hi
    data_files:
      - hi/*.parquet
    default: false
    description: "Hindi language subset"
  - config_name: kn
    data_files:
      - kn/*.parquet
    default: false
    description: "Kannada language subset"
  - config_name: ml
    data_files:
      - ml/*.parquet
    default: false
    description: "Malayalam language subset"
  - config_name: mr
    data_files:
      - mr/*.parquet
    default: false
    description: "Marathi language subset"
  - config_name: ne
    data_files:
      - ne/*.parquet
    default: false
    description: "Nepali language subset"
  - config_name: or
    data_files:
      - or/*.parquet
    default: false
    description: "Odia language subset"
  - config_name: pa
    data_files:
      - pa/*.parquet
    default: false
    description: "Punjabi language subset"
  - config_name: ta
    data_files:
      - ta/*.parquet
    default: false
    description: "Tamil language subset"
  - config_name: te
    data_files:
      - te/*.parquet
    default: false
    description: "Telugu language subset"
  - config_name: ur
    data_files:
      - ur/*.parquet
    default: false
    description: "Urdu language subset"
---

# 🔍 IndicMSMARCO: Multilingual Information Retrieval Benchmark

A comprehensive multilingual variant of MS MARCO specifically tailored for Indian languages, featuring carefully selected queries and corresponding passages with high-quality translations.

## 🚀 Quick Start - Load Individual Languages

```python
from datasets import load_dataset

# Load ONLY Hindi data (fast and efficient!)
hindi_data = load_dataset("ai4bharat/IndicMSMARCO", "hi")
print(f"Hindi queries: {len(hindi_data['train'])} samples")

# Load ONLY Bengali data  
bengali_data = load_dataset("ai4bharat/IndicMSMARCO", "bn")
print(f"Bengali queries: {len(bengali_data['train'])} samples")

# Access query-passage pairs
for example in hindi_data['train'][:3]:
    print(f"Query: {example['query']}")
    print(f"Passage: {example['passage'][:200]}...")
    print("---")
```

## 📊 Dataset Overview

- **Total Samples**: 12,999
- **Languages**: 13 languages
- **Source**: MS MARCO development set
- **Quality**: Human-verified translations
- **Task**: Information Retrieval / Passage Ranking

### 🎯 Key Features

- **Topic Diversity**: Science, history, politics, health, technology
- **Query Complexity**: Simple factual, descriptive, and complex entity-based queries  
- **Balanced Representation**: Short, medium, and long-form queries
- **High-Quality Translations**: Professional translation and verification
- **Consistent Structure**: Normalized schema across all languages

## 📋 Available Languages (13 total)

| Code | Language | Load Command | Sample Count |
|------|----------|--------------|--------------|
| `as` | Assamese | `load_dataset('ai4bharat/IndicMSMARCO', 'as')` | ~999 |
| `bn` | Bengali | `load_dataset('ai4bharat/IndicMSMARCO', 'bn')` | ~999 |
| `gu` | Gujarati | `load_dataset('ai4bharat/IndicMSMARCO', 'gu')` | ~999 |
| `hi` | Hindi | `load_dataset('ai4bharat/IndicMSMARCO', 'hi')` | ~999 |
| `kn` | Kannada | `load_dataset('ai4bharat/IndicMSMARCO', 'kn')` | ~999 |
| `ml` | Malayalam | `load_dataset('ai4bharat/IndicMSMARCO', 'ml')` | ~999 |
| `mr` | Marathi | `load_dataset('ai4bharat/IndicMSMARCO', 'mr')` | ~999 |
| `ne` | Nepali | `load_dataset('ai4bharat/IndicMSMARCO', 'ne')` | ~999 |
| `or` | Odia | `load_dataset('ai4bharat/IndicMSMARCO', 'or')` | ~999 |
| `pa` | Punjabi | `load_dataset('ai4bharat/IndicMSMARCO', 'pa')` | ~999 |
| `ta` | Tamil | `load_dataset('ai4bharat/IndicMSMARCO', 'ta')` | ~999 |
| `te` | Telugu | `load_dataset('ai4bharat/IndicMSMARCO', 'te')` | ~999 |
| `ur` | Urdu | `load_dataset('ai4bharat/IndicMSMARCO', 'ur')` | ~999 |

## 💡 Usage Examples

### Information Retrieval Evaluation
```python
from datasets import load_dataset

# Load Hindi benchmark
dataset = load_dataset("ai4bharat/IndicMSMARCO", "hi")
queries = dataset['train']

# Extract queries and passages for retrieval evaluation
for item in queries:
    query_id = item['query_id']
    query_text = item['query']
    passage_text = item['passage']
    relevance = item['relevance_score']
    
    # Use for your retrieval model evaluation
    print(f"Query {query_id}: {query_text}")
    print(f"Relevant passage: {passage_text[:100]}...")
```

### Cross-lingual Retrieval Benchmark
```python
# Compare retrieval across languages
languages = ['as', 'bn', 'gu', 'hi']
results = {}

for lang in languages:
    dataset = load_dataset("ai4bharat/IndicMSMARCO", lang)
    results[lang] = dataset['train']
    print(f"{lang}: {len(results[lang])} query-passage pairs")

# Evaluate your multilingual retrieval model
for lang_code, queries in results.items():
    # Run your retrieval evaluation here
    pass
```



## 📋 Dataset Structure

```python
{
    "query_id": "1234567",
    "query": "भारत की राजधानी क्या है?",
    "passage": "भारत की राजधानी नई दिल्ली है। यह देश के उत्तरी भाग में स्थित है...",
    "passage_id": "7654321",
    "language": "hi",
    "answer": "नई दिल्ली",
    "title": "भारत की राजधानी",
    "query_type": "factual",
    "relevance_score": 1.0,
    "is_selected": true,
    "text": "Query: भारत की राजधानी क्या है? | Passage: भारत की राजधानी नई दिल्ली है...",
    "dataset": "IndicMSMARCO",
    "source": "MS MARCO translated to Indian languages",
    "meta": "{\"model\": \"translation_model\", \"verified\": true}"
}
```

## ⚡ Performance & Loading Tips

- **Single Language Loading**: Always use config name for fastest loading
- **Streaming**: Use `streaming=True` for memory-efficient processing
- **Batch Evaluation**: Load full train split for comprehensive benchmarking
- **Cross-lingual**: Compare same query_id across languages

## 🎯 Use Cases

- 🔍 **Information Retrieval**: Benchmark multilingual retrieval systems
- 🤖 **RAG Evaluation**: Test retrieval-augmented generation systems
- 📊 **Cross-lingual IR**: Evaluate cross-language information retrieval
- 🧪 **Model Comparison**: Compare multilingual embedding models
- 📚 **Academic Research**: Multilingual IR and NLP research



## 📖 Citation

If you use IndicMSMARCO in your research, please cite:

```bibtex
@dataset{indic_msmarco_2024,
  title={IndicRAGSuite: LargeScale Datasets and a Benchmark for Indian Language RAG Systems},
  author={Pasunuti Prasanjith,Prathmesh B More,Anoop Kunchukuttan, Raj Dabre},
  year={2025},
  {journal = {arXiv preprint arXiv:2506.01615},
  url={https://huggingface.co/datasets/ai4bharat/IndicMSMARCO}
}
```


## 🔧 Technical Details

- **Format**: JSONL files per language
- **Encoding**: UTF-8
- **Schema**: Normalized MS MARCO structure
- **Quality Control**: Multi-stage validation process

---
*Built for multilingual information retrieval • Human-verified quality • Ready for benchmarking*