Datasets:

bias-amplified-splits
/

anli

	# 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 Adversarial NLI Corpus."""


	import json
	import os

	import datasets


	_CITATION = """\
	@InProceedings{nie2019adversarial,
	title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
	author={Nie, Yixin
	and Williams, Adina
	and Dinan, Emily
	and Bansal, Mohit
	and Weston, Jason
	and Kiela, Douwe},
	booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
	year = "2020",
	publisher = "Association for Computational Linguistics",
	}
	"""

	_DESCRIPTION = """\
	The Adversarial Natural Language Inference (ANLI) is a new large-scale NLI benchmark dataset,
	The dataset is collected via an iterative, adversarial human-and-model-in-the-loop procedure.
	ANLI is much more difficult than its predecessors including SNLI and MNLI.
	It contains three rounds. Each round has train/dev/test splits.
	"""

	class ANLIConfig(datasets.BuilderConfig):
	"""BuilderConfig for ANLI."""

	def __init__(self, **kwargs):
	"""BuilderConfig for ANLI.

	Args:
	.
	**kwargs: keyword arguments forwarded to super.
	"""
	super(ANLIConfig, self).__init__(version=datasets.Version("0.1.0", ""), **kwargs)


	class ANLI(datasets.GeneratorBasedBuilder):
	"""ANLI: The ANLI Dataset."""

	BUILDER_CONFIGS = [
	ANLIConfig(
	name=bias_amplified_splits_type,
	description="",
	) for bias_amplified_splits_type in ["minority_examples", "partial_input"]
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"round": datasets.Value("string"),
	"uid": datasets.Value("string"),
	"premise": datasets.Value("string"),
	"hypothesis": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
	"reason": datasets.Value("string"),
	}
	),
	# No default supervised_keys (as we have to pass both premise
	# and hypothesis as input).
	supervised_keys=None,
	homepage="https://github.com/facebookresearch/anli/",
	citation=_CITATION,
	)

	def _vocab_text_gen(self, filepath):
	for _, ex in self._generate_examples(filepath):
	yield " ".join([ex["premise"], ex["hypothesis"]])

	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.biased", gen_kwargs={"filepath": dl_manager.download(os.path.join(self.config.name, "validation.biased.jsonl"))}),
	datasets.SplitGenerator(name="validation.anti_biased", gen_kwargs={"filepath": dl_manager.download(os.path.join(self.config.name, "validation.anti_biased.jsonl"))}),
	datasets.SplitGenerator(name="test.biased", gen_kwargs={"filepath": dl_manager.download(os.path.join(self.config.name, "test.biased.jsonl"))}),
	datasets.SplitGenerator(name="test.anti_biased", gen_kwargs={"filepath": dl_manager.download(os.path.join(self.config.name, "test.anti_biased.jsonl"))})
	]

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

	Args:
	filepath: a string

	Yields:
	dictionaries containing "premise", "hypothesis" and "label" strings
	"""
	for idx, line in enumerate(open(filepath, "rb")):
	if line is not None:
	line = line.strip().decode("utf-8")
	item = json.loads(line)

	reason_text = ""
	if "reason" in item:
	reason_text = item["reason"]

	yield f'{item["round"]}-{item["uid"]}', {
	"round": item["round"],
	"uid": item["uid"],
	"premise": item["premise"],
	"hypothesis": item["hypothesis"],
	"label": item["label"],
	"reason": reason_text,
	}