Datasets:
Tasks:
Text Retrieval
Sub-tasks:
document-retrieval
Multilinguality:
multilingual
Annotations Creators:
expert-generated
ArXiv:
License:
annotations_creators: | |
- expert-generated | |
language: | |
- ar | |
- bn | |
- en | |
- es | |
- fa | |
- fi | |
- fr | |
- hi | |
- id | |
- ja | |
- ko | |
- ru | |
- sw | |
- te | |
- th | |
- zh | |
- de | |
- yo | |
multilinguality: | |
- multilingual | |
pretty_name: MIRACL-corpus | |
source_datasets: [] | |
task_categories: | |
- text-retrieval | |
license: | |
- apache-2.0 | |
task_ids: | |
- document-retrieval | |
# Dataset Card for MIRACL (Topics and Qrels) | |
## Dataset Description | |
[Homepage](ttp://miracl.ai) | | |
[Repository:](https://github.com/project-miracl/miracl) | | |
[Paper](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00595/117438) | | |
[ArXiv](https://arxiv.org/abs/2210.09984) | |
MIRACL πππ (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world. | |
This dataset contains the collection data of the 16 "known languages". The remaining 2 "surprise languages" will not be released until later. | |
The topics are generated by native speakers of each language, who also label the relevance between the topics and a given document list. | |
This repository only contains the topics and qrels of MIRACL. The collection can be found [here](https://huggingface.co/datasets/miracl/miracl-corpus). | |
## Dataset Structure | |
1. To download the files: | |
Under folders `miracl-v1.0-{lang}/topics`, | |
the topics are saved in `.tsv` format, with each line to be: | |
``` | |
qid\tquery | |
``` | |
Under folders `miracl-v1.0-{lang}/qrels`, | |
the qrels are saved in standard TREC format, with each line to be: | |
``` | |
qid Q0 docid relevance | |
``` | |
2. To access the data using HuggingFace `datasets`: | |
``` | |
lang='ar' # or any of the 16 languages | |
miracl = datasets.load_dataset('miracl/miracl', lang, use_auth_token=True) | |
# training set: | |
for data in miracl['train']: # or 'dev', 'testA' | |
query_id = data['query_id'] | |
query = data['query'] | |
positive_passages = data['positive_passages'] | |
negative_passages = data['negative_passages'] | |
for entry in positive_passages: # OR 'negative_passages' | |
docid = entry['docid'] | |
title = entry['title'] | |
text = entry['text'] | |
``` | |
The structure is the same for `train`, `dev`, and `testA` set, where `testA` only exists for languages in Mr. TyDi (i.e., Arabic, Bengali, English, Finnish, Indonesian, Japanese, Korean, Russian, Swahili, Telugu, Thai). | |
Note that `negative_passages` are annotated by native speakers as well, instead of the non-positive passages from top-`k` retrieval results. | |
## Dataset Statistics | |
The following table contains the number of queries (`#Q`) and the number of judgments (`#J`) in each language, for the training and development set, | |
where the judgments include both positive and negative samples. | |
| Lang | Train | | Dev | | | |
|:----:|:-----:|:------:|:-----:|:------:| | |
| | **#Q**| **#J** |**#Q** |**#J** | | |
| ar | 3,495 | 25,382 | 2,896 | 29,197 | | |
| bn | 1,631 | 16,754 | 411 | 4,206 | | |
| en | 2,863 | 29,416 | 799 | 8,350 | | |
| es | 2,162 | 21,531 | 648 | 6,443 | | |
| fa | 2,107 | 21,844 | 632 | 6,571 | | |
| fi | 2,897 | 20,350 | 1,271 | 12,008 | | |
| fr | 1,143 | 11,426 | 343 | 3,429 | | |
| hi | 1,169 | 11,668 | 350 | 3,494 | | |
| id | 4,071 | 41,358 | 960 | 9,668 | | |
| ja | 3,477 | 34,387 | 860 | 8,354 | | |
| ko | 868 | 12,767 | 213 | 3,057 | | |
| ru | 4,683 | 33,921 | 1,252 | 13,100 | | |
| sw | 1,901 | 9,359 | 482 | 5,092 | | |
| te | 3,452 | 18,608 | 828 | 1,606 | | |
| th | 2,972 | 21,293 | 733 | 7,573 | | |
| zh | 1,312 | 13,113 | 393 | 3,928 | |