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

dummy/0.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f304d5119a93a37a3cb44de9c33977fbeb6c290c54ad0319474381a5385384a2
+size 2654

reclor.py

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+"""TODO(reclor): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+# TODO(reclor): BibTeX citation
+_CITATION = """\
+@inproceedings{yu2020reclor,
+        author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
+        title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
+        booktitle = {International Conference on Learning Representations (ICLR)},
+        month = {April},
+        year = {2020}
+    }
+
+"""
+
+# TODO(reclor):
+_DESCRIPTION = """\
+Logical reasoning is an important ability to examine, analyze, and critically evaluate arguments as they occur in ordinary
+language as the definition from LSAC. ReClor is a dataset extracted from logical reasoning questions of standardized graduate
+admission examinations. Empirical results show that the state-of-the-art models struggle on ReClor with poor performance
+indicating more research is needed to essentially enhance the logical reasoning ability of current models. We hope this
+dataset could help push Machine Reading Comprehension (MRC) towards more complicated reasonin
+"""
+
+
+class Reclor(datasets.GeneratorBasedBuilder):
+    """TODO(reclor): Short description of my dataset."""
+
+    # TODO(reclor): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    @property
+    def manual_download_instructions(self):
+        return """\
+  to use ReClor you need to download it manually. Please go to its homepage (http://whyu.me/reclor/) fill the google
+  form and you will recive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path/to/folder/folder_name')
+  """
+
+    def _info(self):
+        # TODO(reclor): 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 ...
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answers": datasets.features.Sequence(datasets.Value("string")),
+                    "label": datasets.Value("string"),
+                    "id_string": datasets.Value("string"),
+                }
+            ),
+            # 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="http://whyu.me/reclor/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(reclor): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+
+        if not os.path.exists(data_dir):
+            raise FileNotFoundError(
+                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('wikihow', data_dir=...)` that includes files unzipped from the reclor zip. Manual download instructions: {}".format(
+                    data_dir, self.manual_download_instructions
+                )
+            )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "train.json")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "test.json")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "val.json")},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(reclor): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for id_, row in enumerate(data):
+                yield id_, {
+                    "context": row["context"],
+                    "question": row["question"],
+                    "answers": row["answers"],
+                    "label": str(row.get("label", "")),
+                    "id_string": row["id_string"],
+                }

urls_checksums/checksums.txt

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+# TODO(reclor): If your dataset downloads files, then the checksums will be
+# automatically added here when running the download_and_prepare script
+# with --register_checksums.