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": "In Event2Mind, we explore the task of understanding stereotypical intents and reactions to events. Through crowdsourcing, we create a large corpus with 25,000 events and free-form descriptions of their intents and reactions, both of the event's subject and (potentially implied) other participants.\n", "citation": "@inproceedings{event2Mind,\n    title={Event2Mind: Commonsense Inference on Events, Intents, and Reactions},\n    author={Hannah Rashkin and Maarten Sap and Emily Allaway and Noah A. Smith\u2020 Yejin Choi},\n    year={2018}\n}\n", "homepage": "https://uwnlp.github.io/event2mind/", "license": "", "features": {"Source": {"dtype": "string", "id": null, "_type": "Value"}, "Event": {"dtype": "string", "id": null, "_type": "Value"}, "Xintent": {"dtype": "string", "id": null, "_type": "Value"}, "Xemotion": {"dtype": "string", "id": null, "_type": "Value"}, "Otheremotion": {"dtype": "string", "id": null, "_type": "Value"}, "Xsent": {"dtype": "string", "id": null, "_type": "Value"}, "Osent": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "event2mind", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 649273, "num_examples": 5221, "dataset_name": "event2mind"}, "train": {"name": "train", "num_bytes": 5916384, "num_examples": 46472, "dataset_name": "event2mind"}, "validation": {"name": "validation", "num_bytes": 672365, "num_examples": 5401, "dataset_name": "event2mind"}}, "download_checksums": {"https://uwnlp.github.io/event2mind/data/event2mind.zip": {"num_bytes": 1300770, "checksum": "07dcd0a1278b474ffbe29eff309ff8e0c512521022e2623cbecd417402802a2e"}}, "download_size": 1300770, "dataset_size": 7238022, "size_in_bytes": 8538792}}

dummy/0.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0183a29221ff1865199067a41c086ec093daf984bc818c1a5bb98eadcbb05a08
+size 1250

event2Mind.py

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+"""TODO(event2Mind): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+# TODO(event2Mind): BibTeX citation
+_CITATION = """\
+@inproceedings{event2Mind,
+    title={Event2Mind: Commonsense Inference on Events, Intents, and Reactions},
+    author={Hannah Rashkin and Maarten Sap and Emily Allaway and Noah A. Smith† Yejin Choi},
+    year={2018}
+}
+"""
+
+# TODO(event2Mind):\
+
+_DESCRIPTION = """\
+In Event2Mind, we explore the task of understanding stereotypical intents and reactions to events. Through crowdsourcing, we create a large corpus with 25,000 events and free-form descriptions of their intents and reactions, both of the event's subject and (potentially implied) other participants.
+"""
+_URL = "https://uwnlp.github.io/event2mind/data/event2mind.zip"
+
+
+class Event2mind(datasets.GeneratorBasedBuilder):
+    """TODO(event2Mind): Short description of my dataset."""
+
+    # TODO(event2Mind): Set up version.
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        # TODO(event2Mind): 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 ...
+                    "Source": datasets.Value("string"),
+                    "Event": datasets.Value("string"),
+                    "Xintent": datasets.Value("string"),
+                    "Xemotion": datasets.Value("string"),
+                    "Otheremotion": datasets.Value("string"),
+                    "Xsent": datasets.Value("string"),
+                    "Osent": 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="https://uwnlp.github.io/event2mind/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(event2Mind): 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.csv")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(dl_dir, "test.csv")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": os.path.join(dl_dir, "dev.csv")},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(event2Mind): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            data = csv.DictReader(f)
+            for id_, row in enumerate(data):
+                yield id_, {
+                    "Source": row["Source"],
+                    "Event": row["Event"],
+                    "Xintent": row["Xintent"],
+                    "Xemotion": row["Xemotion"],
+                    "Otheremotion": row["Otheremotion"],
+                    "Xsent": row["Xsent"],
+                    "Osent": row["Osent"],
+                }