| --- |
| annotations_creators: |
| - expert-generated |
|
|
| language: |
| - ar |
| - bn |
| - en |
| - es |
| - fa |
| - fi |
| - fr |
| - hi |
| - id |
| - ja |
| - ko |
| - ru |
| - sw |
| - te |
| - th |
| - zh |
|
|
|
|
| multilinguality: |
| - multilingual |
|
|
| pretty_name: MIRACL-corpus |
| size_categories: [] |
| source_datasets: [] |
| tags: [] |
|
|
| task_categories: |
| - text-retrieval |
|
|
|
|
| task_ids: |
| - document-retrieval |
| --- |
| |
| # Dataset Card for MIRACL Corpus |
|
|
|
|
| ## Dataset Description |
| * **Homepage:** http://miracl.ai |
| * **Repository:** https://github.com/project-miracl/miracl |
| * **Paper:** Coming Soon! |
|
|
| 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](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' |
| 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` and `dev` set. |
| 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 | |
