Datasets:

tasksource
/

blimp_classification

	# coding=utf-8
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	#
	# 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
	#
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	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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	# limitations under the License.

	# Lint as: python3
	"""BLIMP Acceptability"""

	from __future__ import absolute_import, division, print_function

	import csv
	import os
	import textwrap

	import six

	import datasets


	_Blimp_CITATION = r"""
	@article{warstadt2019blimp,
	author = {Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei and Wang, Sheng-Fu and Bowman, Samuel R.},
	title = {BLiMP: The Benchmark of Linguistic Minimal Pairs for English},
	journal = {Transactions of the Association for Computational Linguistics},
	volume = {8},
	number = {},
	pages = {377-392},
	year = {2020},
	doi = {10.1162/tacl\_a\_00321},
	URL = {https://doi.org/10.1162/tacl_a_00321},
	eprint = {https://doi.org/10.1162/tacl_a_00321},
	abstract = { We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4\%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands. }
	}
	@inproceedings{sileo2021analysis,
	title={Analysis and Prediction of NLP Models Via Task Embeddings},
	author={Damien Sileo and Marie-Francine Moens},
	booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
	year={2022},
	}
	"""

	_Blimp_DESCRIPTION = """\
	Acceptable/non acceptable sentences (recasted as a classification task)
	"""

	DATA_URL = "https://www.dropbox.com/s/28s8qj97nuiwyoh/blimp.zip?dl=1"


	def get_labels(task):
	return ["unacceptable","acceptable"]


	class BlimpConfig(datasets.BuilderConfig):
	"""BuilderConfig for Blimp."""

	def __init__(
	self,
	text_features,
	label_classes=None,
	**kwargs,
	):
	"""BuilderConfig for Blimp.
	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(BlimpConfig, 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("blimp", self.name)
	self.citation = textwrap.dedent(_Blimp_CITATION)
	self.description = ""
	self.url = ""


	class Blimp(datasets.GeneratorBasedBuilder):

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

	BUILDER_CONFIG_CLASS = BlimpConfig

	BUILDER_CONFIGS = [
	BlimpConfig(
	name=name,
	text_features={"sentence": "sentence"},
	) for name in ["semantics","syntax","morphology","syntax+semantics","syntax_semantics"]
	]

	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=_Blimp_DESCRIPTION,
	features=datasets.Features(features),
	homepage=self.config.url,
	citation=self.config.citation + "\n" + _Blimp_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",
	},
	),
	]

	def _generate_examples(self, data_file, split):

	label_classes = self.config.label_classes

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

	for n, row in enumerate(reader):

	example = {
	feat: row[col.replace("sentence","text")]
	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:
	print(row)
	continue
	example["label"] =label
	else:
	example["label"] = -1
	yield example["idx"], example