Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Languages:
Italian
Size:
10K - 100K
License:
File size: 4,735 Bytes
9a221b5 876ab27 9a221b5 f26eba5 9a221b5 876ab27 9a221b5 f26eba5 9a221b5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
"""TODO(squad_it): Add a description here."""
import json
import datasets
from datasets.tasks import QuestionAnsweringExtractive
# TODO(squad_it): BibTeX citation
_CITATION = """\
@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}
}
"""
# TODO(squad_it):
_DESCRIPTION = """\
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:
"""
_URL = "https://github.com/crux82/squad-it/raw/master/"
_URLS = {
"train": _URL + "SQuAD_it-train.json.gz",
"test": _URL + "SQuAD_it-test.json.gz",
}
class SquadIt(datasets.GeneratorBasedBuilder):
"""TODO(squad_it): Short description of my dataset."""
# TODO(squad_it): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(squad_it): Specifies the datasets.DatasetInfo object
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
"id": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
}
),
# These are the features of your dataset like images, labels ...
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://github.com/crux82/squad-it",
citation=_CITATION,
task_templates=[
QuestionAnsweringExtractive(
question_column="question", context_column="context", answers_column="answers"
)
],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(squad_it): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
urls_to_download = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(squad_it): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for example in data["data"]:
for paragraph in example["paragraphs"]:
context = paragraph["context"].strip()
for qa in paragraph["qas"]:
question = qa["question"].strip()
id_ = qa["id"]
answer_starts = [answer["answer_start"] for answer in qa["answers"]]
answers = [answer["text"].strip() for answer in qa["answers"]]
yield id_, {
"context": context,
"question": question,
"id": id_,
"answers": {
"answer_start": answer_starts,
"text": answers,
},
}
|