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Update files from the datasets library (from 1.0.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

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  1. .gitattributes +27 -0
  2. dataset_infos.json +1 -0
  3. dummy/0.1.0/dummy_data.zip +3 -0
  4. squad_it.py +109 -0
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bin.* filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zstandard filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
dataset_infos.json ADDED
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+ {"default": {"description": "SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset \ninto Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.\n The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is \n split into training and test sets to support the replicability of the benchmarking of QA systems:\n", "citation": "@InProceedings{10.1007/978-3-030-03840-3_29,\n\tauthor=\"Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto\",\n\teditor=\"Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo\",\n\ttitle=\"Neural Learning for Question Answering in Italian\",\n\tbooktitle=\"AI*IA 2018 -- Advances in Artificial Intelligence\",\n\tyear=\"2018\",\n\tpublisher=\"Springer International Publishing\",\n\taddress=\"Cham\",\n\tpages=\"389--402\",\n\tisbn=\"978-3-030-03840-3\"\n}\n", "homepage": "https://github.com/crux82/squad-it", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "squad_it", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 7870374, "num_examples": 7609, "dataset_name": "squad_it"}, "train": {"name": "train", "num_bytes": 50925634, "num_examples": 54159, "dataset_name": "squad_it"}}, "download_checksums": {"https://github.com/crux82/squad-it/raw/master/SQuAD_it-train.json.gz": {"num_bytes": 7725286, "checksum": "75d4d2832961f7a0f76a43d7e919e56a880ccc55de434ec90ae82cd67bec5d25"}, "https://github.com/crux82/squad-it/raw/master/SQuAD_it-test.json.gz": {"num_bytes": 1051245, "checksum": "25986c617cc7d58e82e916755b8a5684e5efae69835332858a6534a304cd293c"}}, "download_size": 8776531, "dataset_size": 58796008, "size_in_bytes": 67572539}}
dummy/0.1.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:163fe17eb7f55a290dbf9623cc8c85ecd9c877828c261c9de88435319cc4b00f
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+ size 2215
squad_it.py ADDED
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+ """TODO(squad_it): Add a description here."""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import json
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+ import os
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+
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+ import datasets
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+
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+
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+ # TODO(squad_it): BibTeX citation
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+ _CITATION = """\
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+ @InProceedings{10.1007/978-3-030-03840-3_29,
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+ author={Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto},
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+ editor={Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
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+ title={Neural Learning for Question Answering in Italian},
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+ booktitle={AI*IA 2018 -- Advances in Artificial Intelligence},
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+ year={2018},
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+ publisher={Springer International Publishing},
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+ address={Cham},
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+ pages={389--402},
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+ isbn={978-3-030-03840-3}
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+ }
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+ """
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+
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+ # TODO(squad_it):
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+ _DESCRIPTION = """\
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+ SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
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+ into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
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+ The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is
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+ split into training and test sets to support the replicability of the benchmarking of QA systems:
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+ """
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+ _URL = "https://github.com/crux82/squad-it/raw/master"
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+ _TRAIN_FILE = "SQuAD_it-train.json.gz"
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+ _TEST_FILE = "SQuAD_it-test.json.gz"
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+
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+
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+ class SquadIt(datasets.GeneratorBasedBuilder):
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+ """TODO(squad_it): Short description of my dataset."""
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+
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+ # TODO(squad_it): Set up version.
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+ VERSION = datasets.Version("0.1.0")
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+
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+ def _info(self):
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+ # TODO(squad_it): 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="https://github.com/crux82/squad-it",
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+ citation=_CITATION,
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+ )
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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_it): 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 = {"train": os.path.join(_URL, _TRAIN_FILE), "test": os.path.join(_URL, _TEST_FILE)}
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+ downloaded_files = dl_manager.download_and_extract(urls_to_download)
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+
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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+ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
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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_it): 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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+ 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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+
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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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+
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+ yield id_, {
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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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+ }