Datasets:

nyu-mll
/

blimp

	# 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
	"""BLiMP dataset with minimal pairs of grammatical phenomena in English."""


	import json

	import datasets


	_CITATION = """
	@article{warstadt2019blimp,
	title={BLiMP: A Benchmark of Linguistic Minimal Pairs for English},
	author={Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei, and Wang, Sheng-Fu and Bowman, Samuel R},
	journal={arXiv preprint arXiv:1912.00582},
	year={2019}
	}
	"""

	_DESCRIPTION = """
	BLiMP is a challenge set for evaluating what language models (LMs) know about
	major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each
	containing 1000 minimal pairs isolating specific contrasts in syntax,
	morphology, or semantics. The data is automatically generated according to
	expert-crafted grammars.
	"""

	_PROJECT_URL = "https://github.com/alexwarstadt/blimp/tree/master/"
	_DOWNLOAD_URL = "https://raw.githubusercontent.com/alexwarstadt/blimp/master/"


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

	def __init__(self, name, version=datasets.Version("0.1.0"), **kwargs):
	"""BuilderConfig for Blimp.

	Args:
	name (str): UID of the linguistic paradigm
	**kwargs: keyword arguments forwarded to super.
	"""
	description = _DESCRIPTION
	description += f"This configuration includes the paradigm {name}."

	super().__init__(name=name, description=description, version=version, **kwargs)


	class Blimp(datasets.GeneratorBasedBuilder):
	"""Minimal grammatical and ungrammatical pairs of 67 linguistic paradigms."""

	all_paradigms = [
	"adjunct_island",
	"anaphor_gender_agreement",
	"anaphor_number_agreement",
	"animate_subject_passive",
	"animate_subject_trans",
	"causative",
	"complex_NP_island",
	"coordinate_structure_constraint_complex_left_branch",
	"coordinate_structure_constraint_object_extraction",
	"determiner_noun_agreement_1",
	"determiner_noun_agreement_2",
	"determiner_noun_agreement_irregular_1",
	"determiner_noun_agreement_irregular_2",
	"determiner_noun_agreement_with_adj_2",
	"determiner_noun_agreement_with_adj_irregular_1",
	"determiner_noun_agreement_with_adj_irregular_2",
	"determiner_noun_agreement_with_adjective_1",
	"distractor_agreement_relational_noun",
	"distractor_agreement_relative_clause",
	"drop_argument",
	"ellipsis_n_bar_1",
	"ellipsis_n_bar_2",
	"existential_there_object_raising",
	"existential_there_quantifiers_1",
	"existential_there_quantifiers_2",
	"existential_there_subject_raising",
	"expletive_it_object_raising",
	"inchoative",
	"intransitive",
	"irregular_past_participle_adjectives",
	"irregular_past_participle_verbs",
	"irregular_plural_subject_verb_agreement_1",
	"irregular_plural_subject_verb_agreement_2",
	"left_branch_island_echo_question",
	"left_branch_island_simple_question",
	"matrix_question_npi_licensor_present",
	"npi_present_1",
	"npi_present_2",
	"only_npi_licensor_present",
	"only_npi_scope",
	"passive_1",
	"passive_2",
	"principle_A_c_command",
	"principle_A_case_1",
	"principle_A_case_2",
	"principle_A_domain_1",
	"principle_A_domain_2",
	"principle_A_domain_3",
	"principle_A_reconstruction",
	"regular_plural_subject_verb_agreement_1",
	"regular_plural_subject_verb_agreement_2",
	"sentential_negation_npi_licensor_present",
	"sentential_negation_npi_scope",
	"sentential_subject_island",
	"superlative_quantifiers_1",
	"superlative_quantifiers_2",
	"tough_vs_raising_1",
	"tough_vs_raising_2",
	"transitive",
	"wh_island",
	"wh_questions_object_gap",
	"wh_questions_subject_gap",
	"wh_questions_subject_gap_long_distance",
	"wh_vs_that_no_gap",
	"wh_vs_that_no_gap_long_distance",
	"wh_vs_that_with_gap",
	"wh_vs_that_with_gap_long_distance",
	]

	BUILDER_CONFIGS = [BlimpConfig(paradigm) for paradigm in all_paradigms]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"sentence_good": datasets.Value("string"),
	"sentence_bad": datasets.Value("string"),
	"field": datasets.Value("string"),
	"linguistics_term": datasets.Value("string"),
	"UID": datasets.Value("string"),
	"simple_LM_method": datasets.Value("bool"),
	"one_prefix_method": datasets.Value("bool"),
	"two_prefix_method": datasets.Value("bool"),
	"lexically_identical": datasets.Value("bool"),
	"pair_id": datasets.Value("int32"),
	}
	),
	homepage=_PROJECT_URL,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	download_urls = _DOWNLOAD_URL + f"data/{self.config.name}.jsonl"
	downloaded_file = dl_manager.download_and_extract(download_urls)
	return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file})]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, "r", encoding="utf-8") as f:
	for line in f:
	line_dict = json.loads(line)
	id_ = line_dict["UID"] + "_" + line_dict["pairID"]
	feats = {
	"sentence_good": line_dict["sentence_good"],
	"sentence_bad": line_dict["sentence_bad"],
	"field": line_dict["field"],
	"linguistics_term": line_dict["linguistics_term"],
	"UID": line_dict["UID"],
	"simple_LM_method": line_dict["simple_LM_method"],
	"one_prefix_method": line_dict["one_prefix_method"],
	"two_prefix_method": line_dict["two_prefix_method"],
	"lexically_identical": line_dict["lexically_identical"],
	"pair_id": int(line_dict["pairID"]),
	}
	yield id_, feats