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--- |
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annotations_creators: |
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- expert-generated |
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language: |
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- ar |
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- bn |
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- en |
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- es |
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- fa |
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- fi |
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- fr |
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- hi |
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- id |
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- ja |
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- ko |
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- ru |
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- sw |
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- te |
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- th |
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- zh |
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multilinguality: |
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- multilingual |
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pretty_name: MIRACL-corpus |
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size_categories: [] |
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source_datasets: [] |
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tags: [] |
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task_categories: |
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- text-retrieval |
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task_ids: |
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- document-retrieval |
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--- |
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# Dataset Card for MIRACL Corpus |
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## Dataset Description |
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* **Homepage:** http://miracl.ai |
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* **Repository:** https://github.com/project-miracl/miracl |
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* **Paper:** Coming Soon! |
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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. |
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This dataset contains the collection data of the 16 "known languages". The remaining 2 "surprise languages" will not be released until later. |
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The topics are generated by native speakers of each language, who also label the relevance between the topics and a given document list. |
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This repository only contains the topics and qrels of MIRACL. The collection can be found [here](https://huggingface.co/datasets/miracl/miracl-corpus). |
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## Dataset Structure |
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1. To download the files: |
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Under folders `miracl-v1.0-{lang}/topics`, |
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the topics are saved in `.tsv` format, with each line to be: |
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``` |
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qid\tquery |
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``` |
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Under folders `miracl-v1.0-{lang}/qrels`, |
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the qrels are saved in standard TREC format, with each line to be: |
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``` |
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qid Q0 docid relevance |
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``` |
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2. To access the data using HuggingFace `datasets`: |
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``` |
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lang='ar' # or any of the 16 languages |
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miracl = datasets.load_dataset('miracl/miracl', lang, use_auth_token=True) |
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# training set: |
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for data in miracl['train']: # or 'dev' |
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query_id = data['query_id'] |
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query = data['query'] |
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positive_passages = data['positive_passages'] |
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negative_passages = data['negative_passages'] |
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for entry in positive_passages: # OR 'negative_passages' |
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docid = entry['docid'] |
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title = entry['title'] |
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text = entry['text'] |
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``` |
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The structure is the same for `train` and `dev` set. |
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Note that `negative_passages` are annotated by native speakers as well, instead of the non-positive passages from top-`k` retrieval results. |
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## Dataset Statistics |
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The following table contains the number of queries (`#Q`) and the number of judgments (`#J`) in each language, for the training and development set, |
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where the judgments include both positive and negative samples. |
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| Lang | Train | | Dev | | |
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|:----:|:-----:|:------:|:-----:|:------:| |
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| | **#Q**| **#J** |**#Q** |**#J** | |
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| ar | 3,495 | 25,382 | 2,896 | 29,197 | |
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| bn | 1,631 | 16,754 | 411 | 4,206 | |
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| en | 2,863 | 29,416 | 799 | 8,350 | |
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| es | 2,162 | 21,531 | 648 | 6,443 | |
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| fa | 2,107 | 21,844 | 632 | 6,571 | |
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| fi | 2,897 | 20,350 | 1,271 | 12,008 | |
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| fr | 1,143 | 11,426 | 343 | 3,429 | |
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| hi | 1,169 | 11,668 | 350 | 3,494 | |
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| id | 3,998 | 39,885 | 939 | 9,344 | |
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| ja | 3,477 | 34,387 | 860 | 8,354 | |
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| ko | 868 | 12,767 | 213 | 3,057 | |
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| ru | 4,683 | 33,921 | 1,252 | 13,100 | |
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| sw | 1,901 | 9,359 | 482 | 5,092 | |
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| te | 3,452 | 18,608 | 828 | 1,606 | |
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| th | 2,993 | 22,057 | 747 | 7,861 | |
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| zh | 1,312 | 13,113 | 393 | 3,928 | |