---
language:
- id
license: apache-2.0
task_categories:
- question-answering
pretty_name: TyDi QA Indonesian
size_categories:
- 100K<n<1M
source_datasets:
- google-research-datasets/tydiqa
configs:
- config_name: primary_task
  data_files:
  - split: train
    path: "train.parquet"
  - split: validation
    path: "valid.parquet"
---

# TyDi QA - Indonesian

This is a dataset card for TyDi QA specifically for indonesian subset.

This work only copy from the original dataset https://huggingface.co/datasets/google-research-datasets/tydiqa

## Dataset Description

### Dataset Summary

TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic
information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but
don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without
the use of translation (unlike MLQA and XQuAD).

## Dataset Structure

### Data Instances

#### primary_task

An example of 'validation' looks as follows.
```
This example was too long and was cropped:

{
    'annotations': {'minimal_answers_end_byte': array([-1], dtype=int32),
                 'minimal_answers_start_byte': array([-1], dtype=int32),
                 'passage_answer_candidate_index': array([-1], dtype=int32),
                 'yes_no_answer': array(['NONE'], dtype=object)},
    'document_plaintext': '\n transl.\n\n Ras (dari bahasa Prancis race, yang sendirinya dari '
    'document_title': 'Ras manusia',
    'document_url': 'https://id.wikipedia.org/wiki/Ras%20manusia',
    'language': 'indonesian',
    'passage_answer_candidates': {'plaintext_end_byte': array([  659,   843,  1195,  1353,  2125,  3607,  4161,  4508,  4740,
        6530,  7665,  7999,  8561,  9209,  9615, 10690, 11126, 11979,
       12746, 13304, 13486, 15385, 17455, 17505], dtype=int32),
                               'plaintext_start_byte': array([    1,   660,   844,  1196,  1354,  2126,  3608,  4198,  4509,
        4741,  6531,  7666,  8000,  8562,  9249,  9616, 10774, 11127,
       11980, 12747, 13325, 13503, 15400, 17459], dtype=int32)},
    'question_text': 'berapakah jenis ras yang ada didunia?'}
```


### Data Fields

The data fields are the same among all splits.

#### primary_task
- `passage_answer_candidates`: a dictionary feature containing:
  - `plaintext_start_byte`: a `int32` feature.
  - `plaintext_end_byte`: a `int32` feature.
- `question_text`: a `string` feature.
- `document_title`: a `string` feature.
- `language`: a `string` feature.
- `annotations`: a dictionary feature containing:
  - `passage_answer_candidate_index`: a `int32` feature.
  - `minimal_answers_start_byte`: a `int32` feature.
  - `minimal_answers_end_byte`: a `int32` feature.
  - `yes_no_answer`: a `string` feature.
- `document_plaintext`: a `string` feature.
- `document_url`: a `string` feature.


```
@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}
}

```