Datasets:

bias-amplified-splits
/

mnli

	# 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
	"""The General Language Understanding Evaluation (GLUE) benchmark."""


	import csv
	import os
	import textwrap
	import json

	import numpy as np

	import datasets


	_GLUE_CITATION = """\
	@inproceedings{wang2019glue,
	title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
	author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
	note={In the Proceedings of ICLR.},
	year={2019}
	}
	"""

	_GLUE_DESCRIPTION = """\
	GLUE, the General Language Understanding Evaluation benchmark
	(https://gluebenchmark.com/) is a collection of resources for training,
	evaluating, and analyzing natural language understanding systems.
	"""

	_MNLI_BASE_KWARGS = dict(
	text_features={
	"premise": "sentence1",
	"hypothesis": "sentence2",
	},
	label_classes=["entailment", "neutral", "contradiction"],
	label_column="label",
	data_url="https://dl.fbaipublicfiles.com/glue/data/MNLI.zip",
	data_dir="MNLI",
	citation=textwrap.dedent(
	"""\
	@InProceedings{N18-1101,
	author = "Williams, Adina
	and Nangia, Nikita
	and Bowman, Samuel",
	title = "A Broad-Coverage Challenge Corpus for
	Sentence Understanding through Inference",
	booktitle = "Proceedings of the 2018 Conference of
	the North American Chapter of the
	Association for Computational Linguistics:
	Human Language Technologies, Volume 1 (Long
	Papers)",
	year = "2018",
	publisher = "Association for Computational Linguistics",
	pages = "1112--1122",
	location = "New Orleans, Louisiana",
	url = "http://aclweb.org/anthology/N18-1101"
	}
	@article{bowman2015large,
	title={A large annotated corpus for learning natural language inference},
	author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D},
	journal={arXiv preprint arXiv:1508.05326},
	year={2015}
	}"""
	),
	url="http://www.nyu.edu/projects/bowman/multinli/",
	)


	class GlueConfig(datasets.BuilderConfig):
	"""BuilderConfig for GLUE."""

	def __init__(
	self,
	text_features,
	label_column,
	data_url,
	data_dir,
	citation,
	url,
	label_classes=None,
	process_label=lambda x: x,
	**kwargs,
	):
	"""BuilderConfig for GLUE.
	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(GlueConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
	self.text_features = text_features
	self.label_column = label_column
	self.label_classes = label_classes
	self.data_url = data_url
	self.data_dir = data_dir
	self.citation = citation
	self.url = url
	self.process_label = process_label


	class Glue(datasets.GeneratorBasedBuilder):
	"""The General Language Understanding Evaluation (GLUE) benchmark."""

	BUILDER_CONFIGS = [
	GlueConfig(
	name=bias_amplified_splits_type,
	description=textwrap.dedent(
	"""\
	The Multi-Genre Natural Language Inference Corpus is a crowdsourced
	collection of sentence pairs with textual entailment annotations. Given a premise sentence
	and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis
	(entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are
	gathered from ten different sources, including transcribed speech, fiction, and government reports.
	We use the standard test set, for which we obtained private labels from the authors, and evaluate
	on both the matched (in-domain) and mismatched (cross-domain) section. We also use and recommend
	the SNLI corpus as 550k examples of auxiliary training data."""
	),
	**_MNLI_BASE_KWARGS,
	) for bias_amplified_splits_type in ["minority_examples", "partial_input"]
	]

	def _info(self):
	features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
	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=_GLUE_DESCRIPTION,
	features=datasets.Features(features),
	homepage=self.config.url,
	citation=self.config.citation + "\n" + _GLUE_CITATION,
	)

	def _split_generators(self, dl_manager):
	return [
	datasets.SplitGenerator(
	name="train.biased",
	gen_kwargs={
	"filepath": dl_manager.download(os.path.join(self.config.name, "train.biased.jsonl")),
	},
	),
	datasets.SplitGenerator(
	name="train.anti_biased",
	gen_kwargs={
	"filepath": dl_manager.download(os.path.join(self.config.name, "train.anti_biased.jsonl")),
	},
	),
	datasets.SplitGenerator(
	name="validation_matched.biased",
	gen_kwargs={
	"filepath": dl_manager.download(os.path.join(self.config.name, "validation_matched.biased.jsonl")),
	},
	),
	datasets.SplitGenerator(
	name="validation_matched.anti_biased",
	gen_kwargs={
	"filepath": dl_manager.download(os.path.join(self.config.name, "validation_matched.anti_biased.jsonl")),
	},
	),
	datasets.SplitGenerator(
	name="validation_mismatched.biased",
	gen_kwargs={
	"filepath": dl_manager.download(os.path.join(self.config.name, "validation_mismatched.biased.jsonl")),
	},
	),
	datasets.SplitGenerator(
	name="validation_mismatched.anti_biased",
	gen_kwargs={
	"filepath": dl_manager.download(os.path.join(self.config.name, "validation_mismatched.anti_biased.jsonl")),
	},
	),
	]

	def _generate_examples(self, filepath):
	"""Generate examples.

	Args:
	filepath: a string

	Yields:
	dictionaries containing "premise", "hypothesis" and "label" strings
	"""
	process_label = self.config.process_label
	label_classes = self.config.label_classes

	for idx, line in enumerate(open(filepath, "rb")):
	if line is not None:
	line = line.strip().decode("utf-8")
	item = json.loads(line)
	example = {
	"idx": item["idx"],
	"premise": item["premise"],
	"hypothesis": item["hypothesis"],
	}
	if self.config.label_column in item:
	label = item[self.config.label_column]
	example["label"] = process_label(label)
	else:
	example["label"] = process_label(-1)

	yield example["idx"], example