---
license: cc-by-nc-sa-4.0
task_categories:
- translation
- text-retrieval
language:
- fi
- gn
- ht
- id
- ja
- ka
- ro
- so
- sw
- ta
- th
- tr
- vi
- zh
tags:
- news
- multilingual
- machine-translated
- nllb
pretty_name: xMINDlarge
size_categories:
- 10K<n<100K
multilinguality:
  - translation
  - multilingual
  - multi-parallel
source_datasets:
  - MIND
configs:
  - config_name: fin
    data_files:
      - split: train
        path: data/fin/train.parquet.gzip
      - split: dev
        path: data/fin/dev.parquet.gzip
      - split: test
        path: data/fin/test.parquet.gzip
  - config_name: grn
    data_files:
      - split: train
        path: data/grn/train.parquet.gzip
      - split: dev
        path: data/grn/dev.parquet.gzip
      - split: test
        path: data/grn/test.parquet.gzip
  - config_name: hat
    data_files:
      - split: train
        path: data/hat/train.parquet.gzip
      - split: dev
        path: data/hat/dev.parquet.gzip
      - split: test
        path: data/hat/test.parquet.gzip
  - config_name: ind
    data_files:
      - split: train
        path: data/ind/train.parquet.gzip
      - split: dev
        path: data/ind/dev.parquet.gzip
      - split: test
        path: data/ind/test.parquet.gzip
  - config_name: jpn
    data_files:
      - split: train
        path: data/jpn/train.parquet.gzip
      - split: dev
        path: data/jpn/dev.parquet.gzip
      - split: test
        path: data/jpn/test.parquet.gzip
  - config_name: kat
    data_files:
      - split: train
        path: data/kat/train.parquet.gzip
      - split: dev
        path: data/kat/dev.parquet.gzip
      - split: test
        path: data/kat/test.parquet.gzip
  - config_name: ron
    data_files:
      - split: train
        path: data/ron/train.parquet.gzip
      - split: dev
        path: data/ron/dev.parquet.gzip
      - split: test
        path: data/ron/test.parquet.gzip
  - config_name: som
    data_files:
      - split: train
        path: data/som/train.parquet.gzip
      - split: dev
        path: data/som/dev.parquet.gzip
      - split: test
        path: data/som/test.parquet.gzip
  - config_name: swh
    data_files:
      - split: train
        path: data/swh/train.parquet.gzip
      - split: dev
        path: data/swh/dev.parquet.gzip
      - split: test
        path: data/swh/test.parquet.gzip
  - config_name: tam
    data_files:
      - split: train
        path: data/tam/train.parquet.gzip
      - split: dev
        path: data/tam/dev.parquet.gzip
      - split: test
        path: data/tam/test.parquet.gzip
  - config_name: tha
    data_files:
      - split: train
        path: data/tha/train.parquet.gzip
      - split: dev
        path: data/tha/dev.parquet.gzip
      - split: test
        path: data/tha/test.parquet.gzip
  - config_name: tur
    data_files:
      - split: train
        path: data/tur/train.parquet.gzip
      - split: dev
        path: data/tur/dev.parquet.gzip
      - split: test
        path: data/tur/test.parquet.gzip
  - config_name: vie
    data_files:
      - split: train
        path: data/vie/train.parquet.gzip
      - split: dev
        path: data/vie/dev.parquet.gzip
      - split: test
        path: data/vie/test.parquet.gzip
  - config_name: zho
    data_files:
      - split: train
        path: data/zho/train.parquet.gzip
      - split: dev
        path: data/zho/dev.parquet.gzip
      - split: test
        path: data/zho/test.parquet.gzip
---

# Dataset Card for xMINDlarge

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Uses](#uses)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Source Data](#source-data)
  - [Data Collection and Processing](#data-collection-and-processing)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)

## Dataset Description

- **Homepage:** https://huggingface.co/datasets/aiana94/xMINDlarge
- **Repository:** https://github.com/andreeaiana/xMIND 
- **Paper:**  [MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation](https://arxiv.org/abs/2403.17876)
- **Point of Contact:** [Andreea Iana](https://andreeaiana.github.io/)
- **License:** [CC-BY-4.0-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/)


### Dataset Summary

xMINDlarge is an open, large-scale multi-parallel news dataset for multi- and cross-lingual news recommendation. 
It is derived from the English [MINDlarge](https://msnews.github.io/) dataset using open-source neural machine translation (i.e., [NLLB 3.3B](https://huggingface.co/facebook/nllb-200-3.3B)).

For the *small version* of the dataset, see [xMINDsmall](https://huggingface.co/datasets/aiana94/xMINDsmall).

### Uses 

This dataset can be used for machine translation, text retrieval, or as a benchmark dataset for news recommendation.


### Languages

xMIND contains news translated into 14 linguistically and geographically diverse languages, with digital footprints of varying sizes.

| **Code** 	| **Language**     	| **Script** 	| **Macro-area** 	| **Family**     	| **Genus**             	|
|:----------|:------------------|:--------------|:------------------|:------------------|:--------------------------|
| FIN      	| Finnish          	| Latin      	| Eurasia        	| Uralic         	| Finnic                	| 
| GRN      	| Guarani          	| Latin      	| South-America  	| Tupian         	| Maweti-Guarani        	| 
| HAT      	| Haitian Creole   	| Latin      	| North-America  	| Indo-European  	| Creoles and Pidgins   	| 
| IND      	| Indonesian       	| Latin      	| Papunesia      	| Austronesian   	| Malayo-Sumbawan       	| 
| JPN     	| Japanese         	| Japanese   	| Eurasia        	| Japonic        	| Japanesic             	| 
| KAT      	| Georgian         	| Georgian   	| Eurasia        	| Kartvelic      	| Georgian-Zan          	| 
| RON      	| Romanian         	| Latin      	| Eurasia        	| Indo-European  	| Romance               	| 
| SOM      	| Somali           	| Latin      	| Africa         	| Afro-Asiatic   	| Lowland East Cushitic 	| 
| SWH      	| Swahili          	| Latin      	| Africa         	| Niger-Congo    	| Bantu                 	| 
| TAM      	| Tamil            	| Tamil      	| Eurasia        	| Dravidian      	| Dravidian             	| 
| THA      	| Thai             	| Thai       	| Eurasia        	| Tai-Kadai      	| Kam-Tai               	| 
| TUR      	| Turkish          	| Latin      	| Eurasia        	| Altaic         	| Turkic                	| 
| VIE      	| Vietnamese       	| Latin      	| Eurasia        	| Austro-Asiatic 	| Vietic                	| 
| ZHO      	| Mandarin Chinese 	| Han        	| Eurasia        	| Sino-Tibetan   	| Sinitic               	| 


## Dataset Structure

### Data Instances
```
>>> from datasets import load_dataset
>>> data = load_dataset('aiana94/xMINDlarge', 'ron')

# Please, specify the language code.

# A data point example is below:

{
"nid": "N49265"
"title": "Aceste reţete cu sos de afine sunt perfecte pentru cina de Ziua Recunoştinţei.",
"abstract": "Nu vei mai vrea niciodată versiunea cumpărată din magazin."
}

```

### 


### Data Fields

- nid (string): news ID (same as in the [MIND dataset](https://msnews.github.io/))
- title (string): news title
- abstract (string) : news abstract (optional)

### Data Splits

For all languages, there are three split: `train`, `dev`, `test`.

## Dataset Creation


### Source Data

The news were machine-translated from the [MINDlarge dataset](https://msnews.github.io/).

#### Data Collection and Processing

We translated the news articles using the open-source model [NLLB 3.3B](https://huggingface.co/facebook/nllb-200-3.3B). 
For more details regarding the translation setup and data quality, we refer to the corresponding [paper](https://arxiv.org/abs/2403.17876).

#### Personal and Sensitive Information

The data is sourced from newspaper sources and contains mentions of public figures and individuals.


## Considerations for Using the Data

### Social Impact of Dataset
[More Information Needed]


### Discussion of Biases
[More Information Needed]


### Other Known Limitations

Users should keep in mind that the dataset contains short news texts (e.g., news titles and abstracts), which might limit the applicability of the developed systems to other domains.

## Additional Information 

### Licensing Information
The dataset is released under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
If you intend to use, adapt, or share xMINDlarge, particularly together with additional news and click behavior information from the original MIND dataset, please read and reference the [Microsoft Research License Terms](https://github.com/msnews/MIND/blob/master/MSR%20License_Data.pdf) of MIND.

### Citation Infomation

**BibTeX:**

```bibtex
@inproceedings{iana2024mind,
  title={Mind your language: a multilingual dataset for cross-lingual news recommendation},
  author={Iana, Andreea and Glava{\v{s}}, Goran and Paulheim, Heiko},
  booktitle={Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={553--563},
  year={2024}
}

```

Also consider citing the following:

```bibtex
@inproceedings{wu2020mind,
  title={Mind: A large-scale dataset for news recommendation},
  author={Wu, Fangzhao and Qiao, Ying and Chen, Jiun-Hung and Wu, Chuhan and Qi, Tao and Lian, Jianxun and Liu, Danyang and Xie, Xing and Gao, Jianfeng and Wu, Winnie and others},
  booktitle={Proceedings of the 58th annual meeting of the association for computational linguistics},
  pages={3597--3606},
  year={2020}
}
```