数据集:
blimp
任务:
文本分类语言:
en计算机处理:
monolingual大小:
10K<n<100K语言创建人:
machine-generated批注创建人:
crowdsourced源数据集:
original许可:
cc-by-4.0BLiMP 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.
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
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
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
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
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 }
The data fields are the same among all splits.
adjunct_islandname | train |
---|---|
adjunct_island | 1000 |
anaphor_gender_agreement | 1000 |
anaphor_number_agreement | 1000 |
animate_subject_passive | 1000 |
animate_subject_trans | 1000 |
@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} }
Thanks to @lhoestq , @patrickvonplaten , @thomwolf for adding this dataset.