Datasets:
Delete loading script
Browse files- IndicQA.py +0 -113
IndicQA.py
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"""TODO(xquad): Add a description here."""
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import json
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import datasets
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_CITATION = """\
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"""
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_DESCRIPTION = """\
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"""
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_URL = "https://huggingface.co/datasets/ai4bharat/IndicQA/resolve/main/data/"
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_LANG = ["as", "bn", "gu", "hi", "kn", "ml", "mr", "or", "pa", "ta", "te"]
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class IndicqaConfig(datasets.BuilderConfig):
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"""BuilderConfig for Indicqa"""
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def __init__(self, lang, **kwargs):
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"""
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Args:
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lang: string, language for the input text
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**kwargs: keyword arguments forwarded to super.
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"""
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super(IndicqaConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
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self.lang = lang
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class Xquad(datasets.GeneratorBasedBuilder):
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"""TODO(indicqa): Short description of my dataset."""
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# TODO(indicqa): Set up version.
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [IndicqaConfig(name=f"indicqa.{lang}", description=_DESCRIPTION, lang=lang) for lang in _LANG]
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def _info(self):
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# TODO(indicqa): 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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"id": 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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# These are the features of your dataset like images, labels ...
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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="",
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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(indicqa): 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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urls_to_download = {lang: _URL + f"indicqa.{lang}.json" for lang in _LANG}
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downloaded_files = dl_manager.download_and_extract(urls_to_download)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": downloaded_files[self.config.lang]},
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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(indicqa): Yields (key, example) tuples from the dataset
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with open(filepath, encoding="utf-8") as f:
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indicqa = json.load(f)
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id_ = 0
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for article in indicqa["data"]:
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for paragraph in article["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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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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# Features currently used are "context", "question", and "answers".
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# Others are extracted here for the ease of future expansions.
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yield id_, {
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"context": context,
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"question": question,
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"id": qa["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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id_ += 1
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