README.md

metadata

annotations_creators:
  - machine-generated
language_creators:
  - machine-generated
language:
  - it
language_bcp47:
  - it-IT
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - unknown
source_datasets:
  - extended|squad
task_categories:
  - question-answering
task_ids:
  - open-domain-qa
  - extractive-qa
paperswithcode_id: squad-it
pretty_name: SQuAD-it

Dataset Card for "squad_it"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • 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
  • Contributions

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

More Information Needed

Languages

More Information Needed

Dataset Structure

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: a string feature.
• context: a string feature.
• question: a string feature.
• answers: a dictionary feature containing:
  • text: a string feature.
  • answer_start: a int32 feature.

Data Splits

name	train	test
default	54159	7609

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

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.