# Dataset Card for "squad-v2-fi"
### Dataset Summary
Machine translated and normalized Finnish version of the SQuAD-v2.0 dataset. Details about the translation and normalization processes can be found [here](https://helda.helsinki.fi/handle/10138/344973).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
## Dataset Structure
### Data Instances
Example data:
```
{
    "title": "Josefina (Ruotsin kuningatar)",
    "paragraphs": [
        {
            "qas": [
                {
                    "question": "Milloin Josefina Maximiliana Eugenia Napoleona av Leuchtenberg syntyi?",
                    "id": "2149392872931478957",
                    "answers": [
                        {
                            "answer_start": 59,
                            "text": "14. maaliskuuta 1807"
                        }
                    ],
                    "is_impossible": false
                }
            ],
            "context": "Josefina Maximiliana Eugenia Napoleona av Leuchtenberg (14. maaliskuuta 1807 − 7. kesäkuuta 1876, Tukholma) oli Ruotsi-Norjan kuningatar ja kuningas Oskar I:n puoliso."
        }
    ]
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
  - `text`: a `string` feature.
  - `answer_start`: a `int32` feature.
### Data Splits
|   name   |train|validation|
|----------|----:|---------:|
|plain_text|92383|      8737|

### Citation Information
```
@MastersThesis{3241c198b3f147faacbc6d8b64ed9419,
  author   = "Kylli{\"a}inen, {Ilmari}",
  title    = "Neural Factoid Question Answering and Question Generation for Finnish",
  language = "en",
  address  = "Helsinki, Finland",
  school   = "University of Helsinki",
  year     = "2022",
  month    = "jun",
  day      = "15",
  url      = "https://helda.helsinki.fi/handle/10138/344973"
}
```
task_categories:question-answering task_ids:extractive-qa annotations_creators:crowdsourced language_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:10K<n<100K language:fi license:gpl-3.0 question-generation region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced", "found"], "language": ["fi"], "license": ["gpl-3.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "SQuAD-v2-fi", "tags": ["question-generation"], "train-eval-index": [{"config": "plain_text", "task": "question-answering", "task_id": "extractive_question_answering", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"question": "question", "context": "context", "answers": {"text": "text", "answer_start": "answer_start"}}}]}
TAGS
#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-Finnish #license-gpl-3.0 #question-generation #region-us 

Dataset Card for "squad-v2-fi"
==============================


### Dataset Summary


Machine translated and normalized Finnish version of the SQuAD-v2.0 dataset. Details about the translation and normalization processes can be found here.


Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.


Dataset Structure
-----------------


### Data Instances


Example data:


### Data Fields


The data fields are the same among all splits.


#### plain\_text


* 'id': a 'string' feature.
* 'title': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature.


### Data Splits




### Dataset Summary


Machine translated and normalized Finnish version of the SQuAD-v2.0 dataset. Details about the translation and normalization processes can be found here.


Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.


Dataset Structure
----------------- ### Data Instances


Example data: ### Data Fields


The data fields are the same among all splits. #### plain\_text


* 'id': a 'string' feature.
* 'title': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature. ### Data Splits
TAGS
#task_categories-question-answering #task_ids-extractive-qa #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #language-Finnish #license-gpl-3.0 #question-generation #region-us 
 ### Dataset Summary


Machine translated and normalized Finnish version of the SQuAD-v2.0 dataset. Details about the translation and normalization processes can be found here.


Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.


Dataset Structure
----------------- ### Data Instances


Example data: ### Data Fields


The data fields are the same among all splits. #### plain\_text


* 'id': a 'string' feature.
* 'title': a 'string' feature.
* 'context': a 'string' feature.
* 'question': a 'string' feature.
* 'answers': a dictionary feature containing:
	+ 'text': a 'string' feature.
	+ 'answer\_start': a 'int32' feature. ### Data Splits