Datasets:

tasksource
/

recast

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# Lint as: python3
	"""Recast datasets"""

	from __future__ import absolute_import, division, print_function

	import csv
	import os
	import textwrap

	import six

	import datasets


	_Recast_CITATION = r"""@inproceedings{poliak-etal-2018-collecting,
	title = "Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation",
	author = "Poliak, Adam and
	Haldar, Aparajita and
	Rudinger, Rachel and
	Hu, J. Edward and
	Pavlick, Ellie and
	White, Aaron Steven and
	Van Durme, Benjamin",
	booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
	month = oct # "-" # nov,
	year = "2018",
	address = "Brussels, Belgium",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/D18-1007",
	doi = "10.18653/v1/D18-1007",
	pages = "67--81",
	abstract = "We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at \url{https://www.decomp.net}, and will grow over time as additional resources are recast and added from novel sources.",
	}
	"""

	_Recast_DESCRIPTION = """\
	A diverse collection of tasks recasted as natural language inference tasks.
	"""

	DATA_URL = "https://www.dropbox.com/s/z1mcq6ygfsae0wj/recast.zip?dl=1"

	TASK_TO_LABELS = {
	"recast_kg_relations": ["1", "2", "3", "4", "5", "6"],
	"recast_puns": ["not-entailed", "entailed"],
	"recast_factuality": ["not-entailed", "entailed"],
	"recast_verbnet": ["not-entailed", "entailed"],
	"recast_verbcorner": ["not-entailed", "entailed"],
	"recast_sentiment": ["not-entailed", "entailed"],
	"recast_megaveridicality": ["not-entailed", "entailed"],
	"recast_ner": ["not-entailed", "entailed"],
	"recast_winogender": ["not-entailed", "entailed"],
	"recast_ner": ["not-entailed", "entailed"],
	}


	def get_labels(task):
	return TASK_TO_LABELS[task]


	class RecastConfig(datasets.BuilderConfig):
	"""BuilderConfig for Recast."""

	def __init__(
	self,
	text_features,
	label_classes=None,
	process_label=lambda x: x,
	**kwargs,
	):
	"""BuilderConfig for Recast.
	Args:
	text_features: `dict[string, string]`, map from the name of the feature
	dict for each text field to the name of the column in the tsv file
	label_column: `string`, name of the column in the tsv file corresponding
	to the label
	data_url: `string`, url to download the zip file from
	data_dir: `string`, the path to the folder containing the tsv files in the
	downloaded zip
	citation: `string`, citation for the data set
	url: `string`, url for information about the data set
	label_classes: `list[string]`, the list of classes if the label is
	categorical. If not provided, then the label will be of type
	`datasets.Value('float32')`.
	process_label: `Function[string, any]`, function taking in the raw value
	of the label and processing it to the form required by the label feature
	**kwargs: keyword arguments forwarded to super.
	"""

	super(RecastConfig, self).__init__(
	version=datasets.Version("1.0.0", ""), **kwargs
	)

	self.text_features = text_features
	self.label_column = "label"
	self.label_classes = get_labels(self.name)
	self.data_url = DATA_URL
	self.data_dir = os.path.join("recast", self.name)
	self.citation = textwrap.dedent(_Recast_CITATION)
	self.process_label = lambda x: str(x)
	self.description = ""
	self.url = ""


	class Recast(datasets.GeneratorBasedBuilder):

	"""The General Language Understanding Evaluation (Recast) benchmark."""

	BUILDER_CONFIG_CLASS = RecastConfig

	BUILDER_CONFIGS = [
	RecastConfig(
	name="recast_kg_relations",
	text_features={"context": "context", "hypothesis": "hypothesis"},
	),
	RecastConfig(
	name="recast_puns",
	text_features={"context": "context", "hypothesis": "hypothesis"},
	),
	RecastConfig(
	name="recast_factuality",
	text_features={"context": "context", "hypothesis": "hypothesis"},
	),
	RecastConfig(
	name="recast_verbnet",
	text_features={"context": "context", "hypothesis": "hypothesis"},
	),
	RecastConfig(
	name="recast_verbcorner",
	text_features={"context": "context", "hypothesis": "hypothesis"},
	),
	RecastConfig(
	name="recast_ner",
	text_features={"context": "context", "hypothesis": "hypothesis"},
	),
	RecastConfig(
	name="recast_sentiment",
	text_features={"context": "context", "hypothesis": "hypothesis"},
	),
	RecastConfig(
	name="recast_megaveridicality",
	text_features={"context": "context", "hypothesis": "hypothesis"},
	),
	]

	def _info(self):
	features = {
	text_feature: datasets.Value("string")
	for text_feature in six.iterkeys(self.config.text_features)
	}
	if self.config.label_classes:
	features["label"] = datasets.features.ClassLabel(
	names=self.config.label_classes
	)
	else:
	features["label"] = datasets.Value("float32")
	features["idx"] = datasets.Value("int32")
	return datasets.DatasetInfo(
	description=_Recast_DESCRIPTION,
	features=datasets.Features(features),
	homepage=self.config.url,
	citation=self.config.citation + "\n" + _Recast_CITATION,
	)

	def _split_generators(self, dl_manager):
	dl_dir = dl_manager.download_and_extract(self.config.data_url)
	data_dir = os.path.join(dl_dir, self.config.data_dir)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"data_file": os.path.join(data_dir or "", "train.tsv"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"data_file": os.path.join(data_dir or "", "dev.tsv"),
	"split": "dev",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"data_file": os.path.join(data_dir or "", "test.tsv"),
	"split": "test",
	},
	),
	]

	def _generate_examples(self, data_file, split):

	process_label = self.config.process_label
	label_classes = self.config.label_classes

	with open(data_file, encoding="utf8") as f:
	reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)

	for n, row in enumerate(reader):

	example = {
	feat: row[col]
	for feat, col in six.iteritems(self.config.text_features)
	}
	example["idx"] = n

	if self.config.label_column in row:
	label = row[self.config.label_column]
	if label_classes and label not in label_classes:
	label = int(label) if label else None
	example["label"] = process_label(label)
	else:
	example["label"] = process_label(-1)
	yield example["idx"], example