Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
acceptability-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
Commit
•
0127ff0
1
Parent(s):
9230f73
Add determiner_noun_agreement_irregular_1 data files
Browse files
README.md
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dtype: int32
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splits:
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- name: train
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num_bytes:
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num_examples: 1000
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download_size:
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dataset_size:
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- config_name: determiner_noun_agreement_irregular_2
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features:
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- name: sentence_good
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data_files:
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- split: train
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path: determiner_noun_agreement_2/train-*
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---
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# Dataset Card for "blimp"
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dtype: int32
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splits:
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- name: train
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+
num_bytes: 164473
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num_examples: 1000
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+
download_size: 47274
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dataset_size: 164473
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- config_name: determiner_noun_agreement_irregular_2
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features:
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- name: sentence_good
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data_files:
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- split: train
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path: determiner_noun_agreement_2/train-*
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- config_name: determiner_noun_agreement_irregular_1
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data_files:
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- split: train
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path: determiner_noun_agreement_irregular_1/train-*
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---
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# Dataset Card for "blimp"
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dataset_infos.json
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"builder_name": "blimp",
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"config_name": "determiner_noun_agreement_irregular_1",
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"determiner_noun_agreement_irregular_2": {
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"description": "\nBLiMP is a challenge set for evaluating what language models (LMs) know about\nmajor grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each\ncontaining 1000 minimal pairs isolating specific contrasts in syntax,\nmorphology, or semantics. The data is automatically generated according to\nexpert-crafted grammars.\n",
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"builder_name": "blimp",
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"config_name": "determiner_noun_agreement_irregular_1",
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"determiner_noun_agreement_irregular_2": {
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"description": "\nBLiMP is a challenge set for evaluating what language models (LMs) know about\nmajor grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each\ncontaining 1000 minimal pairs isolating specific contrasts in syntax,\nmorphology, or semantics. The data is automatically generated according to\nexpert-crafted grammars.\n",
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determiner_noun_agreement_irregular_1/train-00000-of-00001.parquet
ADDED
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size 47274
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