Datasets:

nyu-mll
/

blimp

	---
	annotations_creators:
	- crowdsourced
	language_creators:
	- machine-generated
	language:
	- en
	license:
	- cc-by-4.0
	multilinguality:
	- monolingual
	size_categories:
	- 10K<n<100K
	source_datasets:
	- original
	task_categories:
	- text-classification
	task_ids:
	- acceptability-classification
	paperswithcode_id: blimp
	pretty_name: BLiMP
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	- split: train
	path: causative/train-*
	- config_name: complex_NP_island
	data_files:
	- split: train
	path: complex_NP_island/train-*
	- config_name: coordinate_structure_constraint_complex_left_branch
	data_files:
	- split: train
	path: coordinate_structure_constraint_complex_left_branch/train-*
	- config_name: coordinate_structure_constraint_object_extraction
	data_files:
	- split: train
	path: coordinate_structure_constraint_object_extraction/train-*
	- config_name: determiner_noun_agreement_1
	data_files:
	- split: train
	path: determiner_noun_agreement_1/train-*
	- config_name: determiner_noun_agreement_2
	data_files:
	- split: train
	path: determiner_noun_agreement_2/train-*
	- config_name: determiner_noun_agreement_irregular_1
	data_files:
	- split: train
	path: determiner_noun_agreement_irregular_1/train-*
	- config_name: determiner_noun_agreement_irregular_2
	data_files:
	- split: train
	path: determiner_noun_agreement_irregular_2/train-*
	- config_name: determiner_noun_agreement_with_adj_2
	data_files:
	- split: train
	path: determiner_noun_agreement_with_adj_2/train-*
	- config_name: determiner_noun_agreement_with_adj_irregular_1
	data_files:
	- split: train
	path: determiner_noun_agreement_with_adj_irregular_1/train-*
	- config_name: determiner_noun_agreement_with_adj_irregular_2
	data_files:
	- split: train
	path: determiner_noun_agreement_with_adj_irregular_2/train-*
	- config_name: determiner_noun_agreement_with_adjective_1
	data_files:
	- split: train
	path: determiner_noun_agreement_with_adjective_1/train-*
	- config_name: distractor_agreement_relational_noun
	data_files:
	- split: train
	path: distractor_agreement_relational_noun/train-*
	- config_name: distractor_agreement_relative_clause
	data_files:
	- split: train
	path: distractor_agreement_relative_clause/train-*
	- config_name: drop_argument
	data_files:
	- split: train
	path: drop_argument/train-*
	- config_name: ellipsis_n_bar_1
	data_files:
	- split: train
	path: ellipsis_n_bar_1/train-*
	- config_name: ellipsis_n_bar_2
	data_files:
	- split: train
	path: ellipsis_n_bar_2/train-*
	- config_name: existential_there_object_raising
	data_files:
	- split: train
	path: existential_there_object_raising/train-*
	- config_name: existential_there_quantifiers_1
	data_files:
	- split: train
	path: existential_there_quantifiers_1/train-*
	- config_name: existential_there_quantifiers_2
	data_files:
	- split: train
	path: existential_there_quantifiers_2/train-*
	- config_name: existential_there_subject_raising
	data_files:
	- split: train
	path: existential_there_subject_raising/train-*
	- config_name: expletive_it_object_raising
	data_files:
	- split: train
	path: expletive_it_object_raising/train-*
	- config_name: inchoative
	data_files:
	- split: train
	path: inchoative/train-*
	- config_name: intransitive
	data_files:
	- split: train
	path: intransitive/train-*
	- config_name: irregular_past_participle_adjectives
	data_files:
	- split: train
	path: irregular_past_participle_adjectives/train-*
	- config_name: irregular_past_participle_verbs
	data_files:
	- split: train
	path: irregular_past_participle_verbs/train-*
	- config_name: irregular_plural_subject_verb_agreement_1
	data_files:
	- split: train
	path: irregular_plural_subject_verb_agreement_1/train-*
	- config_name: irregular_plural_subject_verb_agreement_2
	data_files:
	- split: train
	path: irregular_plural_subject_verb_agreement_2/train-*
	---

	# Dataset Card for "blimp"

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage:
	- Repository: https://github.com/alexwarstadt/blimp
	- Paper: [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://doi.org/10.1162/tacl_a_00321)
	- Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
	- Size of downloaded dataset files: 29.58 MB
	- Size of the generated dataset: 11.45 MB
	- Total amount of disk used: 41.03 MB

	### Dataset Summary

	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.

	### Supported Tasks and Leaderboards

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Languages

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Dataset Structure

	### Data Instances

	#### adjunct_island

	- Size of downloaded dataset files: 0.36 MB
	- Size of the generated dataset: 0.17 MB
	- Total amount of disk used: 0.52 MB

	An example of 'train' looks as follows.
	```
	{
	"UID": "tough_vs_raising_1",
	"field": "syntax_semantics",
	"lexically_identical": false,
	"linguistics_term": "control_raising",
	"one_prefix_method": false,
	"pair_id": 2,
	"sentence_bad": "Benjamin's tutor was certain to boast about.",
	"sentence_good": "Benjamin's tutor was easy to boast about.",
	"simple_LM_method": true,
	"two_prefix_method": false
	}
	```

	#### anaphor_gender_agreement

	- Size of downloaded dataset files: 0.44 MB
	- Size of the generated dataset: 0.14 MB
	- Total amount of disk used: 0.57 MB

	An example of 'train' looks as follows.
	```
	{
	"UID": "tough_vs_raising_1",
	"field": "syntax_semantics",
	"lexically_identical": false,
	"linguistics_term": "control_raising",
	"one_prefix_method": false,
	"pair_id": 2,
	"sentence_bad": "Benjamin's tutor was certain to boast about.",
	"sentence_good": "Benjamin's tutor was easy to boast about.",
	"simple_LM_method": true,
	"two_prefix_method": false
	}
	```

	#### anaphor_number_agreement

	- Size of downloaded dataset files: 0.45 MB
	- Size of the generated dataset: 0.14 MB
	- Total amount of disk used: 0.59 MB

	An example of 'train' looks as follows.
	```
	{
	"UID": "tough_vs_raising_1",
	"field": "syntax_semantics",
	"lexically_identical": false,
	"linguistics_term": "control_raising",
	"one_prefix_method": false,
	"pair_id": 2,
	"sentence_bad": "Benjamin's tutor was certain to boast about.",
	"sentence_good": "Benjamin's tutor was easy to boast about.",
	"simple_LM_method": true,
	"two_prefix_method": false
	}
	```

	#### animate_subject_passive

	- Size of downloaded dataset files: 0.46 MB
	- Size of the generated dataset: 0.15 MB
	- Total amount of disk used: 0.61 MB

	An example of 'train' looks as follows.
	```
	{
	"UID": "tough_vs_raising_1",
	"field": "syntax_semantics",
	"lexically_identical": false,
	"linguistics_term": "control_raising",
	"one_prefix_method": false,
	"pair_id": 2,
	"sentence_bad": "Benjamin's tutor was certain to boast about.",
	"sentence_good": "Benjamin's tutor was easy to boast about.",
	"simple_LM_method": true,
	"two_prefix_method": false
	}
	```

	#### animate_subject_trans

	- Size of downloaded dataset files: 0.43 MB
	- Size of the generated dataset: 0.13 MB
	- Total amount of disk used: 0.57 MB

	An example of 'train' looks as follows.
	```
	{
	"UID": "tough_vs_raising_1",
	"field": "syntax_semantics",
	"lexically_identical": false,
	"linguistics_term": "control_raising",
	"one_prefix_method": false,
	"pair_id": 2,
	"sentence_bad": "Benjamin's tutor was certain to boast about.",
	"sentence_good": "Benjamin's tutor was easy to boast about.",
	"simple_LM_method": true,
	"two_prefix_method": false
	}
	```

	### Data Fields

	The data fields are the same among all splits.

	#### adjunct_island
	- `sentence_good`: a `string` feature.
	- `sentence_bad`: a `string` feature.
	- `field`: a `string` feature.
	- `linguistics_term`: a `string` feature.
	- `UID`: a `string` feature.
	- `simple_LM_method`: a `bool` feature.
	- `one_prefix_method`: a `bool` feature.
	- `two_prefix_method`: a `bool` feature.
	- `lexically_identical`: a `bool` feature.
	- `pair_id`: a `int32` feature.

	#### anaphor_gender_agreement
	- `sentence_good`: a `string` feature.
	- `sentence_bad`: a `string` feature.
	- `field`: a `string` feature.
	- `linguistics_term`: a `string` feature.
	- `UID`: a `string` feature.
	- `simple_LM_method`: a `bool` feature.
	- `one_prefix_method`: a `bool` feature.
	- `two_prefix_method`: a `bool` feature.
	- `lexically_identical`: a `bool` feature.
	- `pair_id`: a `int32` feature.

	#### anaphor_number_agreement
	- `sentence_good`: a `string` feature.
	- `sentence_bad`: a `string` feature.
	- `field`: a `string` feature.
	- `linguistics_term`: a `string` feature.
	- `UID`: a `string` feature.
	- `simple_LM_method`: a `bool` feature.
	- `one_prefix_method`: a `bool` feature.
	- `two_prefix_method`: a `bool` feature.
	- `lexically_identical`: a `bool` feature.
	- `pair_id`: a `int32` feature.

	#### animate_subject_passive
	- `sentence_good`: a `string` feature.
	- `sentence_bad`: a `string` feature.
	- `field`: a `string` feature.
	- `linguistics_term`: a `string` feature.
	- `UID`: a `string` feature.
	- `simple_LM_method`: a `bool` feature.
	- `one_prefix_method`: a `bool` feature.
	- `two_prefix_method`: a `bool` feature.
	- `lexically_identical`: a `bool` feature.
	- `pair_id`: a `int32` feature.

	#### animate_subject_trans
	- `sentence_good`: a `string` feature.
	- `sentence_bad`: a `string` feature.
	- `field`: a `string` feature.
	- `linguistics_term`: a `string` feature.
	- `UID`: a `string` feature.
	- `simple_LM_method`: a `bool` feature.
	- `one_prefix_method`: a `bool` feature.
	- `two_prefix_method`: a `bool` feature.
	- `lexically_identical`: a `bool` feature.
	- `pair_id`: a `int32` feature.

	### Data Splits

	\| name \|train\|
	\|------------------------\|----:\|
	\|adjunct_island \| 1000\|
	\|anaphor_gender_agreement\| 1000\|
	\|anaphor_number_agreement\| 1000\|
	\|animate_subject_passive \| 1000\|
	\|animate_subject_trans \| 1000\|

	## Dataset Creation

	### Curation Rationale

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	#### Who are the source language producers?

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Annotations

	#### Annotation process

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	#### Who are the annotators?

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Personal and Sensitive Information

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Discussion of Biases

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Other Known Limitations

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Additional Information

	### Dataset Curators

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Licensing Information

	BLiMP is distributed under a [CC-BY](https://creativecommons.org/licenses/by/4.0/) license.

	### Citation Information

	```
	@article{warstadt2020blimp,
	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. }
	}
	```


	### Contributions

	Thanks to [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.