Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
csv
Sub-tasks:
open-domain-qa
Languages:
Polish
Size:
1K - 10K
License:
Create README.md
#1
by
asawczyn
- opened
README.md
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---
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annotations_creators:
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- expert-generated
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language_creators:
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- other
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language:
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- pl
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license:
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- cc-by-sa-3.0
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multilinguality:
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- monolingual
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pretty_name: 'Did you know?'
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size_categories:
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- 1K<n<10K
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source_datasets:
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- original
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task_categories:
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- question-answering
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task_ids:
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- open-domain-question-answering
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---
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# klej-dyk
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## Description
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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.
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## Tasks (input, output, and metrics)
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The task is to predict if the answer to the given question is correct or not.
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**Input** ('question sentence', 'answer' columns): question and answer sentences
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**Output** ('target' column): 1 if the answer is correct, 0 otherwise. Note that the test split doesn't have target values so -1 is used instead
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**Domain**: Wikipedia
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**Measurements**: F1-Score
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**Example**:
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*Czym zajmowali się świątnicy? vs. Świątnik – osoba, która dawniej zajmowała się
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obsługą kościoła (świątyni).* → **1 (the answer is correct)**
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## Data splits
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| Subset | Cardinality |
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| ----------- | ----------: |
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| train | 4154 |
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| val | 0 |
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| test | 1029 |
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## Class distribution
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| Class | train | validation | test |
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|:----------|--------:|-------------:|-------:|
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| incorrect | 0.831 | - | 0.831 |
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| correct | 0.169 | - | 0.169 |
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## Citation
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```
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@misc{11321/39,
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title = {Pytania i odpowiedzi z serwisu wikipedyjnego "Czy wiesz", wersja 1.1},
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author = {Marci{\'n}czuk, Micha{\l} and Piasecki, Dominik and Piasecki, Maciej and Radziszewski, Adam},
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url = {http://hdl.handle.net/11321/39},
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note = {{CLARIN}-{PL} digital repository},
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year = {2013}
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}
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```
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## License
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```
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Creative Commons Attribution ShareAlike 3.0 licence (CC-BY-SA 3.0)
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```
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## Links
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[HuggingFace](https://huggingface.co/datasets/dyk)
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[Source](http://nlp.pwr.wroc.pl/en/tools-and-resources/resources/czy-wiesz-question-answering-dataset)
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[Source #2](https://clarin-pl.eu/dspace/handle/11321/39)
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[Paper](https://www.researchgate.net/publication/272685895_Open_dataset_for_development_of_Polish_Question_Answering_systems)
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## Examples
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### Loading
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```python
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from pprint import pprint
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from datasets import load_dataset
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dataset = load_dataset("allegro/klej-dyk")
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pprint(dataset['train'][100])
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#{'answer': '"W wyborach prezydenckich w 2004 roku, Moroz przekazał swoje '
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# 'poparcie Wiktorowi Juszczence. Po wyborach w 2006 socjaliści '
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# 'początkowo tworzyli ""pomarańczową koalicję"" z Naszą Ukrainą i '
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# 'Blokiem Julii Tymoszenko."',
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# 'q_id': 'czywiesz4362',
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# 'question': 'ile partii tworzy powołaną przez Wiktora Juszczenkę koalicję '
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# 'Blok Nasza Ukraina?',
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# 'target': 0}
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```
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### Evaluation
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```python
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import random
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from pprint import pprint
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from datasets import load_dataset, load_metric
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dataset = load_dataset("allegro/klej-dyk")
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dataset = dataset.class_encode_column("target")
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references = dataset["test"]["target"]
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# generate random predictions
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predictions = [random.randrange(max(references) + 1) for _ in range(len(references))]
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acc = load_metric("accuracy")
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f1 = load_metric("f1")
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acc_score = acc.compute(predictions=predictions, references=references)
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f1_score = f1.compute(predictions=predictions, references=references, average="macro")
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pprint(acc_score)
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pprint(f1_score)
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# {'accuracy': 0.5286686103012633}
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# {'f1': 0.46700507614213194}
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```
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