Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
acceptability-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
Commit
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blimp.py
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# coding=utf-8
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# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""BLiMP dataset with minimal pairs of grammatical phenomena in English."""
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import json
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import datasets
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_CITATION = """
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@article{warstadt2019blimp,
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title={BLiMP: A Benchmark of Linguistic Minimal Pairs for English},
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author={Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei, and Wang, Sheng-Fu and Bowman, Samuel R},
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journal={arXiv preprint arXiv:1912.00582},
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year={2019}
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}
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"""
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_DESCRIPTION = """
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BLiMP is a challenge set for evaluating what language models (LMs) know about
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major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each
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containing 1000 minimal pairs isolating specific contrasts in syntax,
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morphology, or semantics. The data is automatically generated according to
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expert-crafted grammars.
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"""
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_PROJECT_URL = "https://github.com/alexwarstadt/blimp/tree/master/"
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_DOWNLOAD_URL = "https://raw.githubusercontent.com/alexwarstadt/blimp/master/"
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class BlimpConfig(datasets.BuilderConfig):
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"""BuilderConfig for Blimp."""
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def __init__(self, name, version=datasets.Version("0.1.0"), **kwargs):
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"""BuilderConfig for Blimp.
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Args:
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name (str): UID of the linguistic paradigm
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**kwargs: keyword arguments forwarded to super.
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"""
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description = _DESCRIPTION
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description += f"This configuration includes the paradigm {name}."
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super().__init__(name=name, description=description, version=version, **kwargs)
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class Blimp(datasets.GeneratorBasedBuilder):
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"""Minimal grammatical and ungrammatical pairs of 67 linguistic paradigms."""
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all_paradigms = [
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"adjunct_island",
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"anaphor_gender_agreement",
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"anaphor_number_agreement",
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"animate_subject_passive",
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"animate_subject_trans",
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"causative",
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"complex_NP_island",
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"coordinate_structure_constraint_complex_left_branch",
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"coordinate_structure_constraint_object_extraction",
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"determiner_noun_agreement_1",
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"determiner_noun_agreement_2",
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"determiner_noun_agreement_irregular_1",
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"determiner_noun_agreement_irregular_2",
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"determiner_noun_agreement_with_adj_2",
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"determiner_noun_agreement_with_adj_irregular_1",
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"determiner_noun_agreement_with_adj_irregular_2",
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"determiner_noun_agreement_with_adjective_1",
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"distractor_agreement_relational_noun",
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"distractor_agreement_relative_clause",
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"drop_argument",
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"ellipsis_n_bar_1",
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"ellipsis_n_bar_2",
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"existential_there_object_raising",
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"existential_there_quantifiers_1",
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"existential_there_quantifiers_2",
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"existential_there_subject_raising",
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"expletive_it_object_raising",
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"inchoative",
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"intransitive",
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"irregular_past_participle_adjectives",
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"irregular_past_participle_verbs",
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"irregular_plural_subject_verb_agreement_1",
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"irregular_plural_subject_verb_agreement_2",
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"left_branch_island_echo_question",
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"left_branch_island_simple_question",
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"matrix_question_npi_licensor_present",
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"npi_present_1",
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"npi_present_2",
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"only_npi_licensor_present",
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"only_npi_scope",
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"passive_1",
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"passive_2",
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"principle_A_c_command",
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"principle_A_case_1",
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"principle_A_case_2",
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"principle_A_domain_1",
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"principle_A_domain_2",
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"principle_A_domain_3",
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"principle_A_reconstruction",
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"regular_plural_subject_verb_agreement_1",
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"regular_plural_subject_verb_agreement_2",
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"sentential_negation_npi_licensor_present",
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"sentential_negation_npi_scope",
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"sentential_subject_island",
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"superlative_quantifiers_1",
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"superlative_quantifiers_2",
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"tough_vs_raising_1",
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"tough_vs_raising_2",
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"transitive",
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"wh_island",
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"wh_questions_object_gap",
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"wh_questions_subject_gap",
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"wh_questions_subject_gap_long_distance",
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"wh_vs_that_no_gap",
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"wh_vs_that_no_gap_long_distance",
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"wh_vs_that_with_gap",
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"wh_vs_that_with_gap_long_distance",
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]
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BUILDER_CONFIGS = [BlimpConfig(paradigm) for paradigm in all_paradigms]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"sentence_good": datasets.Value("string"),
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"sentence_bad": datasets.Value("string"),
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"field": datasets.Value("string"),
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"linguistics_term": datasets.Value("string"),
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"UID": datasets.Value("string"),
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"simple_LM_method": datasets.Value("bool"),
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"one_prefix_method": datasets.Value("bool"),
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"two_prefix_method": datasets.Value("bool"),
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"lexically_identical": datasets.Value("bool"),
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"pair_id": datasets.Value("int32"),
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}
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),
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homepage=_PROJECT_URL,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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download_urls = _DOWNLOAD_URL + f"data/{self.config.name}.jsonl"
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downloaded_file = dl_manager.download_and_extract(download_urls)
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return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file})]
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def _generate_examples(self, filepath):
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"""Yields examples."""
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with open(filepath, "r", encoding="utf-8") as f:
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for line in f:
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line_dict = json.loads(line)
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id_ = line_dict["UID"] + "_" + line_dict["pairID"]
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feats = {
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"sentence_good": line_dict["sentence_good"],
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"sentence_bad": line_dict["sentence_bad"],
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"field": line_dict["field"],
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"linguistics_term": line_dict["linguistics_term"],
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"UID": line_dict["UID"],
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"simple_LM_method": line_dict["simple_LM_method"],
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"one_prefix_method": line_dict["one_prefix_method"],
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"two_prefix_method": line_dict["two_prefix_method"],
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"lexically_identical": line_dict["lexically_identical"],
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"pair_id": int(line_dict["pairID"]),
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}
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yield id_, feats
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