Created README

README.md

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+---
+annotations_creators:
+- crowdsourced
+language:
+- en
+- ar
+- bn
+- fi
+- id
+- ja
+- sw
+- ko
+- ru
+- te
+- th
+language_creators:
+- crowdsourced
+license:
+- apache-2.0
+multilinguality:
+- multilingual
+pretty_name: Answerable TyDi QA
+size_categories:
+- ['100K<n<1M']
+source_datasets:
+- extended|wikipedia
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+---
+
+# Dataset Card for "answerable-tydiqa"
+
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/google-research-datasets/tydiqa](https://github.com/google-research-datasets/tydiqa)
+- **Paper:** [Paper](https://aclanthology.org/2020.tacl-1.30/)
+- **Size of downloaded dataset files:** 75.43 MB
+- **Size of the generated dataset:** 131.78 MB
+- **Total amount of disk used:** 207.21 MB
+
+### Dataset Summary
+
+[TyDi QA](https://huggingface.co/datasets/tydiqa) is a question answering dataset covering 11 typologically diverse languages. 
+Answerable TyDi QA is an extension of the GoldP subtask of the original TyDi QA dataset to also include unanswertable questions.
+
+## Dataset Structure
+
+The dataset contains a train and a validation set, with 116067 and 13325 examples, respectively. Access them with
+
+```py
+from datasets import load_dataset
+dataset = load_dataset("copenlu/answerable_tydiqa")
+train_set = dataset["train"]
+validation_set = dataset["validation"]
+```
+
+### Data Instances
+
+Here is an example of an instance of the dataset:
+
+```
+{'question_text': 'dimanakah  Dr. Ernest François Eugène Douwes Dekker meninggal?',
+ 'document_title': 'Ernest Douwes Dekker',
+ 'language': 'indonesian',
+ 'annotations': 
+             {'answer_start': [45],
+              'answer_text': ['28 Agustus 1950']
+              },
+ 'document_plaintext': 'Ernest Douwes Dekker wafat dini hari tanggal 28 Agustus 1950 (tertulis di batu nisannya; 29 Agustus 1950 versi van der Veur, 2006) dan dimakamkan di TMP Cikutra, Bandung.',
+ 'document_url': 'https://id.wikipedia.org/wiki/Ernest%20Douwes%20Dekker'}
+```
+
+Description of the dataset columns:
+
+| Column name                  | type        |  Description                                                                                                     |
+| -----------                  | ----------- | -----------                                                                                                      |
+| document_title               | str         | The title of the Wikipedia article from which the data instance was generated                                    |
+| document_url                 | str         | The URL of said article                                                                                          |
+| language                     | str         | The language of the data instance                                                                                |
+| question_text                | str         | The question to answer                                                                                           |
+| document_plaintext           | str         | The context, a Wikipedia paragraph that might or might not contain the answer to the question                    | 
+| annotations["answer_start"]  | list[int]   | The char index in 'document_plaintext' where the answer starts. If the question is unanswerable - an empty list  |
+| annotations["answer_text"]   | list[str]   | The answer, a span of text from 'document_plaintext'. If the question is unanswerable - an empty list            |
+
+
+**Notice:** If the question is *answerable*, annotations["answer_start"] and annotations["answer_text"] contain a list of length 1  
+(In some variations of the dataset the lists might be longer, e.g. if more than one person annotated the instance, but not in our case). 
+If the question is *unanswerable*, annotations["answer_start"] and annotations["answer_text"] contain an empty list of length 0.
+
+
+## Useful stuff
+
+Check out the [datasets ducumentations](https://huggingface.co/docs/datasets/quickstart) to learn how to manipulate and use the dataset. Specifically, you might find the following functions useful:
+
+`dataset.filter`, for filtering out data (useful for keeping instances of specific languages, for example).
+
+`dataset.map`, for manipulating the dataset.
+
+`dataset.to_pandas`, to convert the dataset into a pandas.DataFrame format.
+
+
+```
+@article{tydiqa,
+title   = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
+author  = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
+year    = {2020},
+journal = {Transactions of the Association for Computational Linguistics}
+}
+
+```
+
+
+### Contributions
+
+Thanks to [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.