Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
extractive-qa
Languages:
Catalan
Size:
10K - 100K
ArXiv:
License:
gonzalez-agirre
commited on
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Parent(s):
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Update README.md
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README.md
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This dataset can be used to build extractive-QA and Language Models.
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### Supported Tasks and Leaderboards
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Extractive-QA, Language Model.
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```
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### Data Fields
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Follows [Rajpurkar, Pranav et al., 2016](http://arxiv.org/abs/1606.05250) for
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- `id` (str): Unique ID assigned to the question.
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- `title` (str): Title of the Wikipedia article.
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- `context` (str): Wikipedia section text.
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- test.json: 2135 question/answer pairs
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## Dataset Creation
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### Methodology
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Aggregation
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### Curation Rationale
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For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
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### Source Data
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[More Information Needed]
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### Annotations
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#### Annotation process
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We
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#### Who are the annotators?
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Annotation was commissioned to
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### Personal and Sensitive Information
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No personal or sensitive information included.
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed]
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This dataset can be used to build extractive-QA and Language Models.
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Splits have been balanced by kind of question, and unlike other datasets like SQUAD, it only contains, per record, one question and one answer for each context, although the contexts can repeat multiple times.
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### Supported Tasks and Leaderboards
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Extractive-QA, Language Model.
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},
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```
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### Data Fields
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Follows [Rajpurkar, Pranav et al., 2016](http://arxiv.org/abs/1606.05250) for SQUAD v1 datasets.
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- `id` (str): Unique ID assigned to the question.
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- `title` (str): Title of the Wikipedia article.
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- `context` (str): Wikipedia section text.
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- test.json: 2135 question/answer pairs
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## Dataset Creation
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### Methodology
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Aggregation and balancing from ViquiQUAD and VilaQUAD datasets.
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### Curation Rationale
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For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
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### Source Data
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[More Information Needed]
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### Annotations
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#### Annotation process
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We commissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQUAD 1.0 ([Rajpurkar, Pranav et al. “SQuAD: 100, 000+ Questions for Machine Comprehension of Text.” EMNLP (2016)](http://arxiv.org/abs/1606.05250)).
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#### Who are the annotators?
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Annotation was commissioned to a specialized company that hired a team of native language speakers.
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### Personal and Sensitive Information
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No personal or sensitive information is included.
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed]
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