Datasets:
Tasks:
Text2Text Generation
Languages:
English
Multilinguality:
monolingual
Size Categories:
10M<n<100M
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
License:
File size: 9,136 Bytes
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"""TODO(discofuse): Add a description here."""
from __future__ import absolute_import, division, print_function
import csv
import os
import datasets
# TODO(discofuse): BibTeX citation
_URL_ = "https://storage.googleapis.com/discofuse_dataset_v1/"
_CITATION = """\
@InProceedings{GevaEtAl2019,
title = {DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion},
author = {Geva, Mor and Malmi, Eric and Szpektor, Idan and Berant, Jonathan},
booktitle = {Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
note = {arXiv preprint arXiv:1902.10526},
year = {2019}
}
"""
# TODO(discofuse):
_DESCRIPTION = """\
DISCOFUSE is a large scale dataset for discourse-based sentence fusion.
"""
class DiscofuseConfig(datasets.BuilderConfig):
""" BuilderConfig for Discofuse"""
def __init__(self, data_url, balanced=False, **kwargs):
"""
Args:
balanced: to specify if we want to load the balanced file or the full file
**kwargs: keyword arguments forwarded to super.
"""
super(DiscofuseConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.balanced = balanced
self.data_url = data_url
class Discofuse(datasets.GeneratorBasedBuilder):
"""TODO(discofuse): Short description of my dataset."""
# TODO(discofuse): Set up version.
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
DiscofuseConfig(
name="discofuse-sport", description="sentence fusion", data_url=_URL_ + "discofuse_v1_sports.tar.gz"
),
DiscofuseConfig(
name="discofuse-wikipedia", description="sentence fusion", data_url=_URL_ + "discofuse_v1_wikipedia.tar.gz"
),
]
def _info(self):
# TODO(discofuse): 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(
{
"connective_string": datasets.Value("string"),
"discourse_type": datasets.Value("string"),
"coherent_second_sentence": datasets.Value("string"),
"has_coref_type_pronoun": datasets.Value("float32"),
"incoherent_first_sentence": datasets.Value("string"),
"incoherent_second_sentence": datasets.Value("string"),
"has_coref_type_nominal": datasets.Value("float32"),
"coherent_first_sentence": 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://github.com/google-research-datasets/discofuse",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(discofuse): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
if self.config.name == "discofuse-sport":
dl_dir = dl_manager.download_and_extract(self.config.data_url)
data_dir = os.path.join(dl_dir, "discofuse_v1/sports")
if self.config.balanced:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "train_balanced.tsv")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "test_balanced.tsv")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "dev_balanced.tsv")},
),
]
else:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "train.tsv")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "dev.tsv")},
),
]
else:
if self.config.name == "discofuse-wikipedia":
dl_dir = dl_manager.download_and_extract(self.config.data_url)
data_dir = os.path.join(dl_dir, "discofuse_v1/wikipedia")
if self.config.balanced:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "train_balanced.tsv")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "test_balanced.tsv")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "dev_balanced.tsv")},
),
]
else:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "train.tsv")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "test.tsv")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": os.path.join(data_dir, "dev.tsv")},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(discofuse): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
data = csv.DictReader(f, delimiter="\t")
for id_, row in enumerate(data):
co_first_sent = row["coherent_first_sentence"]
co_second_sent = row["coherent_second_sentence"]
connect_str = row["connective_string"]
discourse_type = row["discourse_type"]
has_coref_pronoun = row["has_coref_type_pronoun"]
has_coref_nominal = row["has_coref_type_nominal"]
inco_first_sent = row["incoherent_first_sentence"]
inco_second_sent = row["incoherent_second_sentence"]
yield id_, {
"connective_string": connect_str,
"discourse_type": discourse_type,
"coherent_second_sentence": co_second_sent,
"has_coref_type_pronoun": has_coref_pronoun,
"incoherent_first_sentence": inco_first_sent,
"incoherent_second_sentence": inco_second_sent,
"has_coref_type_nominal": has_coref_nominal,
"coherent_first_sentence": co_first_sent,
}
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