Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Size:
100K - 1M
License:
| 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 - [-1] | | |
| | annotations["answer_text"] | list[str] | The answer, a span of text from 'document_plaintext'. If the question is unanswerable - [''] | | |
| **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"] will have "-1", while annotations["answer_text"] contain a list with an empty string. | |
| ## 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. |