Datasets:
Tasks:
Text2Text Generation
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
Tags:
common-sense-inference
License:
File size: 3,944 Bytes
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"""TODO(event2Mind): Add a description here."""
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"],
}
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