Update files from the datasets library (from 1.0.0)

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

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

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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

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+version https://git-lfs.github.com/spec/v1
+oid sha256:163fe17eb7f55a290dbf9623cc8c85ecd9c877828c261c9de88435319cc4b00f
+size 2215

squad_it.py

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+"""TODO(squad_it): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+# 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"
+_TRAIN_FILE = "SQuAD_it-train.json.gz"
+_TEST_FILE = "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,
+        )
+
+    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 = {"train": os.path.join(_URL, _TRAIN_FILE), "test": os.path.join(_URL, _TEST_FILE)}
+        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,
+                            },
+                        }