Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Languages:
Italian
Size:
10K - 100K
License:
metadata
paperswithcode_id: squad-it
Dataset Card for "squad_it"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/crux82/squad-it
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 8.37 MB
- Size of the generated dataset: 56.07 MB
- Total amount of disk used: 64.44 MB
Dataset Summary
SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian. The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is split into training and test sets to support the replicability of the benchmarking of QA systems:
Supported Tasks and Leaderboards
Languages
Dataset Structure
We show detailed information for up to 5 configurations of the dataset.
Data Instances
default
- Size of downloaded dataset files: 8.37 MB
- Size of the generated dataset: 56.07 MB
- Total amount of disk used: 64.44 MB
An example of 'train' looks as follows.
This example was too long and was cropped:
{
"answers": "{\"answer_start\": [243, 243, 243, 243, 243], \"text\": [\"evitare di essere presi di mira dal boicottaggio\", \"evitare di essere pres...",
"context": "\"La crisi ha avuto un forte impatto sulle relazioni internazionali e ha creato una frattura all' interno della NATO. Alcune nazi...",
"id": "5725b5a689a1e219009abd28",
"question": "Perchè le nazioni europee e il Giappone si sono separati dagli Stati Uniti durante la crisi?"
}
Data Fields
The data fields are the same among all splits.
default
id
: astring
feature.context
: astring
feature.question
: astring
feature.answers
: a dictionary feature containing:text
: astring
feature.answer_start
: aint32
feature.
Data Splits
name | train | test |
---|---|---|
default | 54159 | 7609 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@InProceedings{10.1007/978-3-030-03840-3_29,
author="Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto",
editor="Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
title="Neural Learning for Question Answering in Italian",
booktitle="AI*IA 2018 -- Advances in Artificial Intelligence",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="389--402",
isbn="978-3-030-03840-3"
}
Contributions
Thanks to @thomwolf, @lewtun, @albertvillanova, @mariamabarham, @patrickvonplaten for adding this dataset.