README.md

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license: apache-2.0
---

# MIRACL Unified Dataset

A unified, standardized version of the [MIRACL dataset](https://huggingface.co/datasets/miracl/miracl) optimized for seamless integration with the Hugging Face ecosystem.

## Overview

While the original MIRACL dataset is excellent, loading it through the Hugging Face datasets library often requires running conversion scripts, which can take considerable time before the data becomes usable. To address this, we have pre-converted the entire MIRACL dataset into a format that's immediately ready for use with the Hugging Face datasets library.

MIRACL Unified provides multilingual information retrieval data in a clean, standardized format across 18 languages. This dataset transforms the original MIRACL format into a more accessible structure that's optimized for modern information retrieval research and development.

**Key improvements over the original MIRACL dataset:**
- 🔄 **Unified format** across all languages and splits
- 🚀 **Direct document indexing** with integer IDs for faster retrieval
- 📊 **Consistent schema** for seamless multi-language processing
- ⚡ **Optimized for Hugging Face** datasets library
- 🛠️ **Ready-to-use** for training and evaluation

## Supported Languages

The dataset covers 18 languages with comprehensive Wikipedia-based corpora:

- **Arabic (ar)**, **Bengali (bn)**, **English (en)**, **Spanish (es)**
- **Persian (fa)**, **Finnish (fi)**, **French (fr)**, **Hindi (hi)**
- **Indonesian (id)**, **Japanese (ja)**, **Korean (ko)**, **Russian (ru)**
- **Swahili (sw)**, **Telugu (te)**, **Thai (th)**, **Chinese (zh)**
- **German (de)**, **Yoruba (yo)**

## Dataset Structure

Each language provides two complementary datasets:

### Query Datasets (`{lang}_queries`)
Contains question-answer pairs with relevance annotations:
- `query_id`: Unique identifier for the query
- `query`: The search question or information need
- `positives`: List of relevant document row indices (integers)
- `negatives`: List of non-relevant document row indices (integers)

Available splits: `train`, `dev`, `testA`, `testB` (varies by language)

### Corpus Datasets (`{lang}_corpus`)
Contains the searchable document collection:
- `docid`: Original MIRACL document identifier
- `title`: Document title
- `text`: Full document content

## Quick Start

```python
from datasets import load_dataset

lang = "ja"  # Choose from: en, ar, bn, es, fa, fi, fr, hi, id, ko, ru, sw, te, th, zh, de, yo

# Load queries and corpus
miracl_queries = load_dataset("hotchpotch/miracl-hf-unified", f"{lang}_queries")
miracl_corpus = load_dataset("hotchpotch/miracl-hf-unified", f"{lang}_corpus", split="train")

# Check available splits
print(f"Available query splits: {list(miracl_queries.keys())}")  # e.g., ['train', 'dev', 'testA', 'testB']

# Get a query from any split (train, dev, testA, etc.)
query = miracl_queries["train"][0]
print(f"Query: {query['query']}")

# Extract positive and negative documents using row indices
positive_docs = [miracl_corpus[row_id] for row_id in query["positives"]]
negative_docs = [miracl_corpus[row_id] for row_id in query["negatives"]]

print(f"Found {len(positive_docs)} relevant and {len(negative_docs)} non-relevant docs")
```

## Dataset Statistics

| Language | Corpus Size | Train Queries | Dev Queries |
|----------|-------------|---------------|-------------|
| Arabic (ar) | 2.1M | 3,495 | 2,896 |
| Bengali (bn) | 297K | 1,631 | 411 |
| English (en) | 32.9M | 2,863 | 799 |
| Spanish (es) | 10.4M | 2,162 | 648 |
| Persian (fa) | 2.2M | 2,107 | 683 |
| Finnish (fi) | 1.9M | 2,897 | 1,271 |
| French (fr) | 14.6M | 1,143 | 343 |
| Hindi (hi) | 506K | 1,169 | 350 |
| Indonesian (id) | 1.4M | 4,071 | 960 |
| Japanese (ja) | 7.0M | 3,477 | 860 |
| Korean (ko) | 1.5M | 868 | 213 |
| Russian (ru) | 9.5M | 4,683 | 1,252 |
| Swahili (sw) | 132K | 1,901 | 482 |
| Telugu (te) | 518K | 3,452 | 646 |
| Thai (th) | 542K | 2,972 | 807 |
| Chinese (zh) | 4.9M | 1,312 | 393 |
| German (de) | 15.9M | - | 305 |
| Yoruba (yo) | 49K | - | 119 |

*Note: Some languages only have dev/test splits without training data. Be aware that in MIRACL evaluation practices, the "dev" split is often used as the actual test set for evaluation.*

## Key Features

### 🎯 **Direct Document Access**
Unlike the original MIRACL format, positive and negative documents are referenced by integer row indices that directly index into the corpus, enabling instant document retrieval without complex mapping.

### 🔄 **Consistent Schema**
All languages follow the same data structure, making it easy to build multi-lingual systems and conduct cross-lingual experiments.

### ⚡ **Optimized Performance**
- Pre-computed document mappings for faster access
- Efficient storage format compatible with Hugging Face datasets
- Memory-efficient loading options for large corpora

### 🛠️ **Research-Ready**
Perfect for:
- **Dense passage retrieval** training and evaluation
- **Cross-lingual information retrieval** research
- **Multi-lingual question answering** systems
- **Retrieval-augmented generation (RAG)** applications
- **Zero-shot cross-lingual transfer** studies

## Citation

If you use this dataset, please cite the original MIRACL paper:

```bibtex
@article{zhang2022miracl,
  title={MIRACL: A Multilingual Information Retrieval Across Languages Dataset},
  author={Zhang, Xinyu and Thakur, Nandan and Ogundepo, Odunayo and Kamalloo, Ehsan and Alfonso-Hermelo, David and Li, Xiaoguang and Liu, Qun and Rezagholizadeh, Mehdi and Lin, Jimmy},
  journal={arXiv preprint arXiv:2210.09984},
  year={2022}
}
```

## License

This dataset is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0), same as the original MIRACL dataset.

## Related Resources

- 📄 **Original MIRACL Dataset**: [https://huggingface.co/datasets/miracl/miracl](https://huggingface.co/datasets/miracl/miracl)
- 📝 **MIRACL Paper**: [https://arxiv.org/abs/2210.09984](https://arxiv.org/abs/2210.09984)

## Acknowledgments

This unified dataset is built upon the excellent work of the MIRACL team. We thank the original authors for creating such a valuable resource for the multilingual information retrieval community.

## Author

Yuichi Tateno (@hotchpotch)