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README.md

@@ -1,4 +1,3 @@
----
 annotations_creators:
 - expert-generated
 
@@ -37,80 +36,9 @@ license:
 
 task_ids:
 - document-retrieval
----
-
-# Dataset Card for MIRACL (Topics and Qrels)
-
-
-## Dataset Description
-* **Homepage:** http://miracl.ai
-* **Repository:** https://github.com/project-miracl/miracl
-* **Paper:** https://arxiv.org/abs/2210.09984
-
-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', '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.
+source_datasets
+- miracl/miracl
+---
 
-| 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 |
+A clone of the excellent [`miracl/miracl` dataset]() that doesn't require authentication. Refer to the original dataset for details.