# 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.""" from __future__ import absolute_import, division, print_function import json import os 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, paradigm_uid, **kwargs): """BuilderConfig for Blimp. Args: paradigm_uid: string, UID of the linguistic paradigm **kwargs: keyword arguments forwarded to super. """ name = paradigm_uid description = _DESCRIPTION description += ("This configuration includes the paradigm {}.").format(name) super(BlimpConfig, self).__init__( name=name, description=description, version=datasets.Version("0.1.0"), **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_uid=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"), } ), supervised_keys=None, # Homepage of the dataset for documentation homepage=_PROJECT_URL, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" cfg = self.config download_urls = {cfg.name: os.path.join(_DOWNLOAD_URL, "data", cfg.name + ".jsonl")} downloaded_files = dl_manager.download_and_extract(download_urls) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files[cfg.name]}) ] def _generate_examples(self, filepath): """Yields examples.""" with open(filepath, "rb") 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