Datasets:

ctu-aic
/

csfever_v2

	---
	license: cc-by-sa-3.0
	task_categories:
	- text-classification
	- text-retrieval
	task_ids:
	- natural-language-inference
	- document-retrieval
	language:
	- cs
	tags:
	- Fact-checking
	pretty_name: CsFEVERv2
	multilinguality: monolingual
	source_datasets: fever
	size_categories:
	- 100K<n<1M
	---
	# Dataset Card for "CsFEVERv2"

	## Dataset Description

	CsFEVERv2 is a dataset for Czech fact-checking developed as part of a bachelor thesis at the Artificial Intelligence Center of the Faculty of Electrical Engineering of
	the Czech technical university in Prague. The dataset consists of an original subset, which is only an iteration of CsFEVER with new data and better processing and
	f1, precision, and 07 subsets filtered using an NLI model and optimized threshold values. The subset wiki_pages is a processed Wikipedia dump from
	August 2022 with correct revids. This subset should be used to map evidence from datasets to Wikipedia texts. Additionaly preprocessed datasets original_nli, f1_nli, precision_nli, 07_nli,
	for training of NLI models are included.

	The original subset can be used to generate other filtered datasets by filtering with other thresholds using predicted_label and predicted_score fields.

	### Languages

	Czech

	## Dataset Usage Example

	```python
	from datasets import load_dataset

	#load default (original) subset
	dataset = load_dataset("/home/mlynatom/csfever_v2")
	dataset = load_dataset("/home/mlynatom/csfever_v2", "original")

	#load f1, f1_nli, precision, precision_nli, 07, and 07_nli subsets
	dataset = load_dataset("/home/mlynatom/csfever_v2", "f1")

	#load wiki_pages subset
	dataset = load_dataset("/home/mlynatom/csfever_v2", "wiki_pages")

	```

	## Dataset Structure

	### Data Instances

	#### original
	An example of 'train' looks as follows.
	```json
	{'id': 75397,
	'label': 'SUPPORTS',
	'predicted_label': 'SUPPORTS',
	'predicted_score': 0.921731
	'claim': 'Nikolaj Coster-Waldau pracoval pro Fox Broadcasting Company.',
	'evidence': [ [ "Nikolaj Coster-Waldau", "Nikolaj Coster-Waldau" ], [ "Fox Broadcasting Company", "Fox Broadcasting Company" ] ]}
	```

	#### f1, precision, 07
	An example of 'train' looks as follows.
	```json
	{'id': 75397,
	'label': 'SUPPORTS',
	'claim': 'Nikolaj Coster-Waldau pracoval pro Fox Broadcasting Company.',
	'evidence': [ [ "Nikolaj Coster-Waldau", "Nikolaj Coster-Waldau" ], [ "Fox Broadcasting Company", "Fox Broadcasting Company" ] ]}
	```

	#### original_nli, f1_nli, precision_nli, 07_nli
	An example of 'train' looks as follows.
	```json
	{'id': 155439,
	'label': 2,
	'claim': 'Newcastle United FC vyhrál pět ligových titulů.',
	'evidence': "Ronnie Simpson. Ronnie Simpson (21. října 1930, Glasgow – 19. dubna 2004, Edinburgh) byl skotský fotbalový brankář..."}
	```

	#### wiki_pages
	An example of 'wiki_pages' looks as follows.
	```json
	{'id': 80916,
	'revid': 20561555,
	'url': "https://cs.wikipedia.org/wiki?curid=80916",
	'title': "Altruismus",
	'text': "Altruismus (z lat. "alter", druhý, 3. pád "altrui", druhému) je moderní ..."}
	```

	### Data Fields

	#### original

	- `id`: a `int32` feature.
	- `label`: a `string` feature.
	- `predicted_label`: a `string` feature. (label predicted by NLI model)
	- `predicted_score`: a `int32` feature. (confidence of predicted_label predicted by NLI model)
	- `claim`: a `string` feature.
	- `evidence`: a `sequence` feature.

	#### f1, precision, 07

	- `id`: a `int32` feature.
	- `label`: a `string` feature.
	- `claim`: a `string` feature.
	- `evidence`: a `sequence` feature.

	#### original_nli, f1_nli, precision_nli, 07_nli

	- `id`: a `int32` feature.
	- `label`: a `int32` feature.
	- `claim`: a `string` feature.
	- `evidence`: a `string` feature.

	#### wiki_pages
	- `id`: a `int32` feature.
	- `revid`: a `int32` feature.
	- `url`: a `string` feature.
	- `title`: a `string` feature.
	- `text`: a `string` feature.


	### Data Splits

	### Data Splits
	#### original
	\| \| train \| dev \| test \|
	\|----------\|-------:\|-----:\|------:\|
	\| original \| 118950 \| 7458 \| 7520 \|

	#### f1

	\| \| train \| dev \| test \|
	\|----\|------:\|-----:\|-----:\|
	\| f1 \| 83438 \| 5445 \| 5328 \|

	#### precision

	\| \| train \| dev \| test \|
	\|-----------\|-------:\|-----:\|------:\|
	\| precision \| 60828 \| 4288 \| 4236 \|

	#### 07

	\| \| train \| dev \| test \|
	\|----\|-------:\|-----:\|------:\|
	\| 07 \| 108607 \| 6685 \| 6623 \|

	#### wiki_pages
	\| \| wiki_pages \|
	\|------------\|-----------:\|
	\| wiki_pages \| 825078 \|

	# Citation

	```bibtex
	@article{Ullrich_2023,
	doi = {10.1007/s10579-023-09654-3},
	url = {https://doi.org/10.1007%2Fs10579-023-09654-3},
	year = 2023,
	month = {may},
	publisher = {Springer Science and Business Media {LLC}},
	author = {Herbert Ullrich and Jan Drchal and Martin Rýpar and Hana Vincourová and Václav Moravec},
	title = {{CsFEVER} and {CTKFacts}: acquiring Czech data for fact verification},
	journal = {Language Resources and Evaluation},
	archivePrefix={arXiv},
	eprint={2201.11115},
	}
	```

	```bibtex
	@thesis{Mlynar_2023,
	author = {Mlynář, Tomáš},
	type = {Bachelor's Thesis}
	title = {Automated Fact Checking Based on Czech Wikipedia},
	institution = {Czech Technical University in Prague, Faculty of Electrical Engineering},
	date = {2023},
	url = {http://hdl.handle.net/10467/109219}
	}
	```