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": "DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.\n. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a \nquestion, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or\n sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was \n necessary for prior datasets.\n", "citation": "@inproceedings{Dua2019DROP,\n  author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},\n  title={  {DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},\n  booktitle={Proc. of NAACL},\n  year={2019}\n}\n", "homepage": "https://allennlp.org/drop", "license": "", "features": {"passage": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers_spans": {"feature": {"spans": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "drop", "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": 100119741, "num_examples": 77409, "dataset_name": "drop"}, "validation": {"name": "validation", "num_bytes": 10788180, "num_examples": 9536, "dataset_name": "drop"}}, "download_checksums": {"https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip": {"num_bytes": 8308692, "checksum": "39d2278a29fd729de301b111a45f434c24834f40df8f4ff116d864589e3249d6"}}, "download_size": 8308692, "dataset_size": 110907921, "size_in_bytes": 119216613}}

drop.py

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+"""TODO(drop): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+# TODO(drop): BibTeX citation
+_CITATION = """\
+@inproceedings{Dua2019DROP,
+  author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
+  title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
+  booktitle={Proc. of NAACL},
+  year={2019}
+}
+"""
+
+# TODO(drop):
+_DESCRIPTION = """\
+DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
+. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a
+question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or
+ sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was
+ necessary for prior datasets.
+"""
+_URl = "https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip"
+
+
+class Drop(datasets.GeneratorBasedBuilder):
+    """TODO(drop): Short description of my dataset."""
+
+    # TODO(drop): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        # TODO(drop): 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(
+                {
+                    "passage": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answers_spans": datasets.features.Sequence({"spans": 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://allennlp.org/drop",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(drop): 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, "drop_dataset")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "drop_dataset_train.json")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "drop_dataset_dev.json")},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(drop): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for i, key in enumerate(data):
+                example = data[key]
+                qa_pairs = example["qa_pairs"]
+                for j, qa in enumerate(qa_pairs):
+                    question = qa["question"]
+                    answers = qa["answer"]["spans"]
+                    yield str(i) + "_" + str(j), {
+                        "passage": example["passage"],
+                        "question": question,
+                        "answers_spans": {"spans": answers},
+                    }

dummy/0.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c03cd6d5d77bd4d2046c61dbe046a4a580a69a516deafaa657f0cf8b07b933a1
+size 2510