Datasets:

allegro
/

klej-dyk

	---
	annotations_creators:
	- expert-generated
	language_creators:
	- other
	language:
	- pl
	license:
	- cc-by-sa-3.0
	multilinguality:
	- monolingual
	size_categories:
	- 1K<n<10K
	source_datasets:
	- original
	task_categories:
	- question-answering
	task_ids:
	- open-domain-qa
	pretty_name: Did you know?
	---

	# klej-dyk

	## Description

	The Czy wiesz? (eng. Did you know?) the dataset consists of almost 5k question-answer pairs obtained from Czy wiesz... section of Polish Wikipedia. Each question is written by a Wikipedia collaborator and is answered with a link to a relevant Wikipedia article. In huggingface version of this dataset, they chose the negatives which have the largest token overlap with a question.

	## Tasks (input, output, and metrics)

	The task is to predict if the answer to the given question is correct or not.

	Input ('question sentence', 'answer' columns): question and answer sentences

	Output ('target' column): 1 if the answer is correct, 0 otherwise.

	Domain: Wikipedia

	Measurements: F1-Score

	Example:

	Input: `Czym zajmowali się świątnicy?` ; `Świątnik – osoba, która dawniej zajmowała się
	obsługą kościoła (świątyni).`

	Input (translated by DeepL): `What did the sacristans do?` ; `A sacristan - a person who used to be in charge of the handling the church (temple).`

	Output: `1` (the answer is correct)

	## Data splits

	\| Subset \| Cardinality \|
	\| ----------- \| ----------: \|
	\| train \| 4154 \|
	\| val \| 0 \|
	\| test \| 1029 \|

	## Class distribution

	\| Class \| train \| validation \| test \|
	\|:----------\|--------:\|-------------:\|-------:\|
	\| incorrect \| 0.831 \| - \| 0.831 \|
	\| correct \| 0.169 \| - \| 0.169 \|

	## Citation

	```
	@misc{11321/39,
	title = {Pytania i odpowiedzi z serwisu wikipedyjnego "Czy wiesz", wersja 1.1},
	author = {Marci{\'n}czuk, Micha{\l} and Piasecki, Dominik and Piasecki, Maciej and Radziszewski, Adam},
	url = {http://hdl.handle.net/11321/39},
	note = {{CLARIN}-{PL} digital repository},
	year = {2013}
	}
	```

	## License

	```
	Creative Commons Attribution ShareAlike 3.0 licence (CC-BY-SA 3.0)
	```

	## Links

	[HuggingFace](https://huggingface.co/datasets/dyk)

	[Source](http://nlp.pwr.wroc.pl/en/tools-and-resources/resources/czy-wiesz-question-answering-dataset)
	[Source #2](https://clarin-pl.eu/dspace/handle/11321/39)

	[Paper](https://www.researchgate.net/publication/272685895_Open_dataset_for_development_of_Polish_Question_Answering_systems)

	## Examples

	### Loading

	```python
	from pprint import pprint

	from datasets import load_dataset

	dataset = load_dataset("allegro/klej-dyk")
	pprint(dataset['train'][100])

	#{'answer': '"W wyborach prezydenckich w 2004 roku, Moroz przekazał swoje '
	# 'poparcie Wiktorowi Juszczence. Po wyborach w 2006 socjaliści '
	# 'początkowo tworzyli ""pomarańczową koalicję"" z Naszą Ukrainą i '
	# 'Blokiem Julii Tymoszenko."',
	# 'q_id': 'czywiesz4362',
	# 'question': 'ile partii tworzy powołaną przez Wiktora Juszczenkę koalicję '
	# 'Blok Nasza Ukraina?',
	# 'target': 0}
	```

	### Evaluation

	```python
	import random
	from pprint import pprint

	from datasets import load_dataset, load_metric

	dataset = load_dataset("allegro/klej-dyk")
	dataset = dataset.class_encode_column("target")
	references = dataset["test"]["target"]

	# generate random predictions
	predictions = [random.randrange(max(references) + 1) for _ in range(len(references))]

	acc = load_metric("accuracy")
	f1 = load_metric("f1")

	acc_score = acc.compute(predictions=predictions, references=references)
	f1_score = f1.compute(predictions=predictions, references=references, average="macro")

	pprint(acc_score)
	pprint(f1_score)

	# {'accuracy': 0.5286686103012633}
	# {'f1': 0.46700507614213194}

	```