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": "Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this \nspan-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard \ncoreferences before selecting the appropriate span(s) in the paragraphs for answering questions.\n", "citation": "@article{allenai:quoref,\n      author    = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and  Matt Gardner},\n      title     = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning},\n      journal   = {arXiv:1908.05803v2 },\n      year      = {2019},\n}\n", "homepage": "https://leaderboard.allenai.org/quoref/submissions/get-started", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"answer_start": {"dtype": "int32", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "quoref", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 44377729, "num_examples": 19399, "dataset_name": "quoref"}, "validation": {"name": "validation", "num_bytes": 5442031, "num_examples": 2418, "dataset_name": "quoref"}}, "download_checksums": {"https://quoref-dataset.s3-us-west-2.amazonaws.com/train_and_dev/quoref-train-dev-v0.1.zip": {"num_bytes": 5078438, "checksum": "aacde0863c04ba6e9ab46995ea844a5b0c6cea58a77ab6fd86a128e33a3ad8fb"}}, "download_size": 5078438, "dataset_size": 49819760, "size_in_bytes": 54898198}}

dummy/0.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:35eec734b230b1170f1ceb3eeea5d7bb789f6213004082c2e19f3e75fb898ba9
+size 3274

quoref.py

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+"""TODO(quoref): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+# TODO(quoref): BibTeX citation
+_CITATION = """\
+@article{allenai:quoref,
+      author    = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and  Matt Gardner},
+      title     = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning},
+      journal   = {arXiv:1908.05803v2 },
+      year      = {2019},
+}
+"""
+
+# TODO(quoref):
+_DESCRIPTION = """\
+Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this
+span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard
+coreferences before selecting the appropriate span(s) in the paragraphs for answering questions.
+"""
+
+_URL = "https://quoref-dataset.s3-us-west-2.amazonaws.com/train_and_dev/quoref-train-dev-v0.1.zip"
+
+
+class Quoref(datasets.GeneratorBasedBuilder):
+    """TODO(quoref): Short description of my dataset."""
+
+    # TODO(quoref): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        # TODO(quoref): 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"),
+                    "question": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                    "answers": datasets.features.Sequence(
+                        {
+                            "answer_start": datasets.Value("int32"),
+                            "text": datasets.Value("string"),
+                        }
+                    )
+                    # 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://leaderboard.allenai.org/quoref/submissions/get-started",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(quoref): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        dl_dir = dl_manager.download_and_extract(_URL)
+        data_dir = os.path.join(dl_dir, "quoref-train-dev-v0.1")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "quoref-train-v0.1.json")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "quoref-dev-v0.1.json")},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(quoref): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for article in data["data"]:
+                title = article.get("title", "").strip()
+                url = article.get("url", "").strip()
+                for paragraph in article["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"]]
+
+                        # Features currently used are "context", "question", and "answers".
+                        # Others are extracted here for the ease of future expansions.
+                        yield id_, {
+                            "title": title,
+                            "context": context,
+                            "question": question,
+                            "id": id_,
+                            "answers": {
+                                "answer_start": answer_starts,
+                                "text": answers,
+                            },
+                            "url": url,
+                        }