Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
Spanish
Size:
10K<n<100K
ArXiv:
License:
Upload data
Browse filesUpload the data from local to the hub
- .gitattributes +2 -0
- dev-v2.0-es.json +0 -0
- dev-v2.0-es_small.json +0 -0
- squad_es.py +151 -0
- train-v2.0-es.json +3 -0
- train-v2.0-es_small.json +3 -0
.gitattributes
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@@ -53,3 +53,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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train-v2.0-es_small.json filter=lfs diff=lfs merge=lfs -text
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train-v2.0-es.json filter=lfs diff=lfs merge=lfs -text
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dev-v2.0-es.json
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dev-v2.0-es_small.json
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squad_es.py
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"""TODO(squad_es): Add a description here."""
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import json
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import datasets
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# TODO(squad_es): BibTeX citation
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_CITATION = """\
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@article{2016arXiv160605250R,
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author = {Casimiro Pio , Carrino and Marta R. , Costa-jussa and Jose A. R. , Fonollosa},
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title = "{Automatic Spanish Translation of the SQuAD Dataset for Multilingual
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Question Answering}",
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journal = {arXiv e-prints},
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year = 2019,
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eid = {arXiv:1912.05200v1},
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pages = {arXiv:1912.05200v1},
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archivePrefix = {arXiv},
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eprint = {1912.05200v2},
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}
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"""
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# TODO(squad_es_v1):
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_DESCRIPTION = """\
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automatic translation of the Stanford Question Answering Dataset (SQuAD) v2 into Spanish
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"""
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_URL = "https://raw.githubusercontent.com/ccasimiro88/TranslateAlignRetrieve/master/"
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_URLS_V1 = {
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"train": _URL + "SQuAD-es-v1.1/train-v1.1-es.json",
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"dev": _URL + "SQuAD-es-v1.1/dev-v1.1-es.json",
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}
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_URLS_V2 = {
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"train": _URL + "SQuAD-es-v2.0/train-v2.0-es.json",
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"dev": _URL + "SQuAD-es-v2.0/dev-v2.0-es.json",
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}
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class SquadEsConfig(datasets.BuilderConfig):
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"""BuilderConfig for SQUADEsV2."""
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def __init__(self, **kwargs):
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"""BuilderConfig for SQUADEsV2.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(SquadEsConfig, self).__init__(**kwargs)
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class SquadEs(datasets.GeneratorBasedBuilder):
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"""TODO(squad_es): Short description of my dataset."""
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# TODO(squad_es): Set up version.
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VERSION = datasets.Version("0.1.0")
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BUILDER_CONFIGS = [
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SquadEsConfig(
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name="v1.1.0",
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version=datasets.Version("1.1.0", ""),
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description="Plain text Spanish squad version 1",
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),
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SquadEsConfig(
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name="v2.0.0",
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version=datasets.Version("2.0.0", ""),
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description="Plain text Spanish squad version 2",
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),
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]
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def _info(self):
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# TODO(squad_es): Specifies the datasets.DatasetInfo object
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# datasets.features.FeatureConnectors
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features=datasets.Features(
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{
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# These are the features of your dataset like images, labels ...
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"id": datasets.Value("string"),
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"title": datasets.Value("string"),
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"context": datasets.Value("string"),
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"question": datasets.Value("string"),
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"answers": datasets.features.Sequence(
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{
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"text": datasets.Value("string"),
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"answer_start": datasets.Value("int32"),
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}
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),
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}
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),
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# If there's a common (input, target) tuple from the features,
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# specify them here. They'll be used if as_supervised=True in
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# builder.as_dataset.
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage="https://github.com/ccasimiro88/TranslateAlignRetrieve",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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# TODO(squad_es): Downloads the data and defines the splits
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# dl_manager is a datasets.download.DownloadManager that can be used to
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# download and extract URLs
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if self.config.name == "v1.1.0":
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dl_dir = dl_manager.download_and_extract(_URLS_V1)
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elif self.config.name == "v2.0.0":
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dl_dir = dl_manager.download_and_extract(_URLS_V2)
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else:
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raise Exception("version does not match any existing one")
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": dl_dir["train"]},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": dl_dir["dev"]},
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),
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]
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def _generate_examples(self, filepath):
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"""Yields examples."""
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# TODO(squad_es): Yields (key, example) tuples from the dataset
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with open(filepath, encoding="utf-8") as f:
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data = json.load(f)
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for example in data["data"]:
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title = example.get("title", "").strip()
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for paragraph in example["paragraphs"]:
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context = paragraph["context"].strip()
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for qa in paragraph["qas"]:
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question = qa["question"].strip()
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id_ = qa["id"]
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answer_starts = [answer["answer_start"] for answer in qa["answers"]]
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answers = [answer["text"].strip() for answer in qa["answers"]]
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yield id_, {
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"title": title,
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"context": context,
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"question": question,
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"id": id_,
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"answers": {
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"answer_start": answer_starts,
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"text": answers,
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},
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}
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train-v2.0-es.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:6d5a2001fc9508449284114017c4fadce6bbe460dcb1656e89b780c125370c85
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size 46750744
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train-v2.0-es_small.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:7e5d1c6869482f17af936703adc77e7c9f6f417507e19ab5ee79d4d1dd55dc07
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size 31532249
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