metadata

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 | Repository: | Paper | ArXiv

MIRACL 🌍🙌🌏 (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.

Dataset Structure

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

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