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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+{"winogrande_xs": {"description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a \nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires \ncommonsense reasoning. \n", "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n", "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "option1": {"dtype": "string", "id": null, "_type": "Value"}, "option2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "winogrande", "config_name": "winogrande_xs", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 228754, "num_examples": 1767, "dataset_name": "winogrande"}, "train": {"name": "train", "num_bytes": 20804, "num_examples": 160, "dataset_name": "winogrande"}, "validation": {"name": "validation", "num_bytes": 164994, "num_examples": 1267, "dataset_name": "winogrande"}}, "download_checksums": {"https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip": {"num_bytes": 2797793, "checksum": "d5699402a41de2b4e6b3c6e2a1e6b4faab2130543a12019a8c5d4f35ec502d47"}}, "download_size": 2797793, "dataset_size": 414552, "size_in_bytes": 3212345}, "winogrande_s": {"description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a \nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires \ncommonsense reasoning. \n", "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n", "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "option1": {"dtype": "string", "id": null, "_type": "Value"}, "option2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "winogrande", "config_name": "winogrande_s", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 228754, "num_examples": 1767, "dataset_name": "winogrande"}, "train": {"name": "train", "num_bytes": 82708, "num_examples": 640, "dataset_name": "winogrande"}, "validation": {"name": "validation", "num_bytes": 164994, "num_examples": 1267, "dataset_name": "winogrande"}}, "download_checksums": {"https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip": {"num_bytes": 2797793, "checksum": "d5699402a41de2b4e6b3c6e2a1e6b4faab2130543a12019a8c5d4f35ec502d47"}}, "download_size": 2797793, "dataset_size": 476456, "size_in_bytes": 3274249}, "winogrande_m": {"description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a \nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires \ncommonsense reasoning. \n", "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n", "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "option1": {"dtype": "string", "id": null, "_type": "Value"}, "option2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "winogrande", "config_name": "winogrande_m", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 228754, "num_examples": 1767, "dataset_name": "winogrande"}, "train": {"name": "train", "num_bytes": 330601, "num_examples": 2558, "dataset_name": "winogrande"}, "validation": {"name": "validation", "num_bytes": 164994, "num_examples": 1267, "dataset_name": "winogrande"}}, "download_checksums": {"https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip": {"num_bytes": 2797793, "checksum": "d5699402a41de2b4e6b3c6e2a1e6b4faab2130543a12019a8c5d4f35ec502d47"}}, "download_size": 2797793, "dataset_size": 724349, "size_in_bytes": 3522142}, "winogrande_l": {"description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a \nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires \ncommonsense reasoning. \n", "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n", "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "option1": {"dtype": "string", "id": null, "_type": "Value"}, "option2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "winogrande", "config_name": "winogrande_l", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 228754, "num_examples": 1767, "dataset_name": "winogrande"}, "train": {"name": "train", "num_bytes": 1325960, "num_examples": 10234, "dataset_name": "winogrande"}, "validation": {"name": "validation", "num_bytes": 164994, "num_examples": 1267, "dataset_name": "winogrande"}}, "download_checksums": {"https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip": {"num_bytes": 2797793, "checksum": "d5699402a41de2b4e6b3c6e2a1e6b4faab2130543a12019a8c5d4f35ec502d47"}}, "download_size": 2797793, "dataset_size": 1719708, "size_in_bytes": 4517501}, "winogrande_xl": {"description": "WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a \nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires \ncommonsense reasoning. \n", "citation": "@InProceedings{ai2:winogrande,\ntitle = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},\nauthors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi\n},\nyear={2019}\n}\n", "homepage": "https://leaderboard.allenai.org/winogrande/submissions/get-started", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "option1": {"dtype": "string", "id": null, "_type": "Value"}, "option2": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "winogrande", "config_name": "winogrande_xl", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 228754, "num_examples": 1767, "dataset_name": "winogrande"}, "train": {"name": "train", "num_bytes": 5211018, "num_examples": 40398, "dataset_name": "winogrande"}, "validation": {"name": "validation", "num_bytes": 164994, "num_examples": 1267, "dataset_name": "winogrande"}}, "download_checksums": {"https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip": {"num_bytes": 2797793, "checksum": "d5699402a41de2b4e6b3c6e2a1e6b4faab2130543a12019a8c5d4f35ec502d47"}}, "download_size": 2797793, "dataset_size": 5604766, "size_in_bytes": 8402559}}

dummy/winogrande_l/1.0.0/dummy_data.zip

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dummy/winogrande_m/1.0.0/dummy_data.zip

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dummy/winogrande_s/1.0.0/dummy_data.zip

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dummy/winogrande_xl/1.0.0/dummy_data.zip

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dummy/winogrande_xs/1.0.0/dummy_data.zip

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winogrande.py

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+"""TODO(winogrande): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+# TODO(winogrande): BibTeX citation
+_CITATION = """\
+@InProceedings{ai2:winogrande,
+title = {WinoGrande: An Adversarial Winograd Schema Challenge at Scale},
+authors={Keisuke, Sakaguchi and Ronan, Le Bras and Chandra, Bhagavatula and Yejin, Choi
+},
+year={2019}
+}
+"""
+
+# TODO(winogrande):
+_DESCRIPTION = """\
+WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern
+ 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a
+fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires
+commonsense reasoning.
+"""
+
+_URL = "https://storage.googleapis.com/ai2-mosaic/public/winogrande/winogrande_1.1.zip"
+_SIZES = ["xs", "s", "m", "l", "xl"]
+
+
+class WinograndeConfig(datasets.BuilderConfig):
+
+    """ BuilderConfig for Discofuse"""
+
+    def __init__(self, data_size, **kwargs):
+        """
+
+        Args:
+            data_size: the size of the training set we want to us (xs, s, m, l, xl)
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(WinograndeConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.data_size = data_size
+
+
+class Winogrande(datasets.GeneratorBasedBuilder):
+    """TODO(winogrande): Short description of my dataset."""
+
+    # TODO(winogrande): Set up version.
+    VERSION = datasets.Version("1.1.0")
+    BUILDER_CONFIGS = [
+        WinograndeConfig(name="winogrande_" + size, description="AI2 dataset", data_size=size) for size in _SIZES
+    ]
+
+    def _info(self):
+        # TODO(winogrande): 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(
+                {
+                    "sentence": datasets.Value("string"),
+                    "option1": datasets.Value("string"),
+                    "option2": datasets.Value("string"),
+                    "answer": 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/winogrande/submissions/get-started",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(winogrande): 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, "winogrande_1.1")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train_{}.jsonl".format(self.config.data_size)),
+                    # 'labelpath': os.path.join(data_dir, 'train_{}-labels.lst'.format(self.config.data_size)),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(data_dir, "test.jsonl"), "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "dev.jsonl"),
+                    # 'labelpath': os.path.join(data_dir, 'dev-labels.lst'),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+        # TODO(winogrande): 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)
+                if split == "test":
+                    yield id_, {
+                        "sentence": data["sentence"],
+                        "option1": data["option1"],
+                        "option2": data["option2"],
+                        "answer": "",
+                    }
+                else:
+                    yield id_, {
+                        "sentence": data["sentence"],
+                        "option1": data["option1"],
+                        "option2": data["option2"],
+                        "answer": data["answer"],
+                    }
+
+
+# def _generate_test_example(filepath, split, labelpath=None):
+#       with open(filepath, encoding="utf-8") as f:
+#           for id_, row in enumerate(f):
+#               data = json.loads(row)
+#               yield id_,{
+#                   'sentence': data['sentence'],
+#                   'option1': data['option1'],
+#                   'option2': data['option2'],
+#                   'answer': None
+#               }