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": "The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph. \nThe dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions.\n", "citation": "@article{wiqa,\n      author    = {Niket Tandon and Bhavana Dalvi Mishra and Keisuke Sakaguchi and Antoine Bosselut and Peter Clark}\n      title     = {WIQA: A dataset for \"What if...\" reasoning over procedural text},\n      journal   = {arXiv:1909.04739v1},\n      year      = {2019},\n}\n", "homepage": "https://allenai.org/data/wiqa", "license": "", "features": {"question_stem": {"dtype": "string", "id": null, "_type": "Value"}, "question_para_step": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answer_label": {"dtype": "string", "id": null, "_type": "Value"}, "answer_label_as_choice": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "metadata_question_id": {"dtype": "string", "id": null, "_type": "Value"}, "metadata_graph_id": {"dtype": "string", "id": null, "_type": "Value"}, "metadata_para_id": {"dtype": "string", "id": null, "_type": "Value"}, "metadata_question_type": {"dtype": "string", "id": null, "_type": "Value"}, "metadata_path_len": {"dtype": "int32", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "wiqa", "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": 17089298, "num_examples": 29808, "dataset_name": "wiqa"}, "test": {"name": "test", "num_bytes": 1532223, "num_examples": 3003, "dataset_name": "wiqa"}, "validation": {"name": "validation", "num_bytes": 3779584, "num_examples": 6894, "dataset_name": "wiqa"}}, "download_checksums": {"https://public-aristo-processes.s3-us-west-2.amazonaws.com/wiqa_dataset_no_explanation_v2/wiqa-dataset-v2-october-2019.zip": {"num_bytes": 5247733, "checksum": "afdab9bc33d814576f76516017f2b39dd101f8770f3f29ab6be2846ff59efb43"}}, "download_size": 5247733, "dataset_size": 22401105, "size_in_bytes": 27648838}}

dummy/0.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:878f302120c4548bda2e88da2e246f73f730f5a710dd6e753b95819bcbe70860
+size 2387

wiqa.py

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+"""TODO(wiqa): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+# TODO(wiqa): BibTeX citation
+_CITATION = """\
+@article{wiqa,
+      author    = {Niket Tandon and Bhavana Dalvi Mishra and Keisuke Sakaguchi and Antoine Bosselut and Peter Clark}
+      title     = {WIQA: A dataset for "What if..." reasoning over procedural text},
+      journal   = {arXiv:1909.04739v1},
+      year      = {2019},
+}
+"""
+
+# TODO(wiqa):
+_DESCRIPTION = """\
+The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph.
+The dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions.
+"""
+_URL = "https://public-aristo-processes.s3-us-west-2.amazonaws.com/wiqa_dataset_no_explanation_v2/wiqa-dataset-v2-october-2019.zip"
+URl = "s3://ai2-s2-research-public/open-corpus/2020-04-10/"
+
+
+class Wiqa(datasets.GeneratorBasedBuilder):
+    """TODO(wiqa): Short description of my dataset."""
+
+    # TODO(wiqa): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        # TODO(wiqa): 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(
+                {
+                    # These are the features of your dataset like images, labels ...
+                    "question_stem": datasets.Value("string"),
+                    "question_para_step": datasets.features.Sequence(datasets.Value("string")),
+                    "answer_label": datasets.Value("string"),
+                    "answer_label_as_choice": datasets.Value("string"),
+                    "choices": datasets.features.Sequence(
+                        {"text": datasets.Value("string"), "label": datasets.Value("string")}
+                    ),
+                    "metadata_question_id": datasets.Value("string"),
+                    "metadata_graph_id": datasets.Value("string"),
+                    "metadata_para_id": datasets.Value("string"),
+                    "metadata_question_type": datasets.Value("string"),
+                    "metadata_path_len": datasets.Value("int32"),
+                }
+            ),
+            # 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://allenai.org/data/wiqa",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(wiqa): 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)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(dl_dir, "train.jsonl")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(dl_dir, "test.jsonl")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(dl_dir, "dev.jsonl")},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(wiqa): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+
+                yield id_, {
+                    "question_stem": data["question"]["stem"],
+                    "question_para_step": data["question"]["para_steps"],
+                    "answer_label": data["question"]["answer_label"],
+                    "answer_label_as_choice": data["question"]["answer_label_as_choice"],
+                    "choices": {
+                        "text": [choice["text"] for choice in data["question"]["choices"]],
+                        "label": [choice["label"] for choice in data["question"]["choices"]],
+                    },
+                    "metadata_question_id": data["metadata"]["ques_id"],
+                    "metadata_graph_id": data["metadata"]["graph_id"],
+                    "metadata_para_id": data["metadata"]["para_id"],
+                    "metadata_question_type": data["metadata"]["question_type"],
+                    "metadata_path_len": data["metadata"]["path_len"],
+                }