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Update files from the datasets library (from 1.0.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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dataset_infos.json ADDED
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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 ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:0183a29221ff1865199067a41c086ec093daf984bc818c1a5bb98eadcbb05a08
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+ size 1250
event2Mind.py ADDED
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+ """TODO(event2Mind): Add a description here."""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import csv
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+ import os
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+
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+ import datasets
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+
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+
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+ # TODO(event2Mind): BibTeX citation
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+ _CITATION = """\
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+ @inproceedings{event2Mind,
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+ title={Event2Mind: Commonsense Inference on Events, Intents, and Reactions},
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+ author={Hannah Rashkin and Maarten Sap and Emily Allaway and Noah A. Smith† Yejin Choi},
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+ year={2018}
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+ }
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+ """
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+
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+ # TODO(event2Mind):\
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+
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+ _DESCRIPTION = """\
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+ 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.
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+ """
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+ _URL = "https://uwnlp.github.io/event2mind/data/event2mind.zip"
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+
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+
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+ class Event2mind(datasets.GeneratorBasedBuilder):
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+ """TODO(event2Mind): Short description of my dataset."""
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+
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+ # TODO(event2Mind): Set up version.
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+ VERSION = datasets.Version("0.1.0")
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+
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+ def _info(self):
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+ # TODO(event2Mind): Specifies the datasets.DatasetInfo object
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+ return datasets.DatasetInfo(
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+ # This is the description that will appear on the datasets page.
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+ description=_DESCRIPTION,
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+ # datasets.features.FeatureConnectors
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+ features=datasets.Features(
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+ {
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+ # These are the features of your dataset like images, labels ...
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+ "Source": datasets.Value("string"),
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+ "Event": datasets.Value("string"),
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+ "Xintent": datasets.Value("string"),
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+ "Xemotion": datasets.Value("string"),
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+ "Otheremotion": datasets.Value("string"),
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+ "Xsent": datasets.Value("string"),
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+ "Osent": datasets.Value("string"),
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+ }
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+ ),
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+ # If there's a common (input, target) tuple from the features,
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+ # specify them here. They'll be used if as_supervised=True in
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+ # builder.as_dataset.
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+ supervised_keys=None,
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+ # Homepage of the dataset for documentation
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+ homepage="https://uwnlp.github.io/event2mind/",
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+ # TODO(event2Mind): Downloads the data and defines the splits
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+ # dl_manager is a datasets.download.DownloadManager that can be used to
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+ # download and extract URLs
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+ dl_dir = dl_manager.download_and_extract(_URL)
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={"filepath": os.path.join(dl_dir, "train.csv")},
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={"filepath": os.path.join(dl_dir, "test.csv")},
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={"filepath": os.path.join(dl_dir, "dev.csv")},
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+ ),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ """Yields examples."""
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+ # TODO(event2Mind): Yields (key, example) tuples from the dataset
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+ with open(filepath, encoding="utf-8") as f:
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+ data = csv.DictReader(f)
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+ for id_, row in enumerate(data):
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+ yield id_, {
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+ "Source": row["Source"],
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+ "Event": row["Event"],
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+ "Xintent": row["Xintent"],
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+ "Xemotion": row["Xemotion"],
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+ "Otheremotion": row["Otheremotion"],
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+ "Xsent": row["Xsent"],
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+ "Osent": row["Osent"],
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+ }