参考：
adjunct_island
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/adjunct_island')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
anaphor_gender_agreement
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/anaphor_gender_agreement')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
anaphor_number_agreement
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/anaphor_number_agreement')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
animate_subject_passive
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/animate_subject_passive')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
animate_subject_trans
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/animate_subject_trans')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
causative
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/causative')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
complex_NP_island
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/complex_NP_island')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
coordinate_structure_constraint_complex_left_branch
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/coordinate_structure_constraint_complex_left_branch')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
coordinate_structure_constraint_object_extraction
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/coordinate_structure_constraint_object_extraction')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
determiner_noun_agreement_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/determiner_noun_agreement_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
determiner_noun_agreement_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/determiner_noun_agreement_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
determiner_noun_agreement_irregular_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/determiner_noun_agreement_irregular_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
determiner_noun_agreement_irregular_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/determiner_noun_agreement_irregular_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
determiner_noun_agreement_with_adj_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/determiner_noun_agreement_with_adj_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
determiner_noun_agreement_with_adj_irregular_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/determiner_noun_agreement_with_adj_irregular_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
determiner_noun_agreement_with_adj_irregular_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/determiner_noun_agreement_with_adj_irregular_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
determiner_noun_agreement_with_adjective_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/determiner_noun_agreement_with_adjective_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
distractor_agreement_relational_noun
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/distractor_agreement_relational_noun')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
distractor_agreement_relative_clause
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/distractor_agreement_relative_clause')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
drop_argument
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/drop_argument')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
ellipsis_n_bar_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/ellipsis_n_bar_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
ellipsis_n_bar_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/ellipsis_n_bar_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
existential_there_object_raising
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/existential_there_object_raising')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
existential_there_quantifiers_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/existential_there_quantifiers_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
existential_there_quantifiers_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/existential_there_quantifiers_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
existential_there_subject_raising
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/existential_there_subject_raising')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
expletive_it_object_raising
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/expletive_it_object_raising')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
inchoative
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/inchoative')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
intransitive
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/intransitive')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
irregular_past_participle_adjectives
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/irregular_past_participle_adjectives')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
irregular_past_participle_verbs
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/irregular_past_participle_verbs')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
irregular_plural_subject_verb_agreement_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/irregular_plural_subject_verb_agreement_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
irregular_plural_subject_verb_agreement_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/irregular_plural_subject_verb_agreement_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
left_branch_island_echo_question
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/left_branch_island_echo_question')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
left_branch_island_simple_question
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/left_branch_island_simple_question')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
matrix_question_npi_licensor_present
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/matrix_question_npi_licensor_present')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
npi_present_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/npi_present_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
npi_present_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/npi_present_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
only_npi_licensor_present
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/only_npi_licensor_present')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
only_npi_scope
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/only_npi_scope')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
passive_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/passive_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
passive_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/passive_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
principle_A_c_command
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/principle_A_c_command')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
principle_A_case_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/principle_A_case_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
principle_A_case_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/principle_A_case_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
principle_A_domain_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/principle_A_domain_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
principle_A_domain_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/principle_A_domain_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
principle_A_domain_3
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/principle_A_domain_3')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
principle_A_reconstruction
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/principle_A_reconstruction')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
regular_plural_subject_verb_agreement_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/regular_plural_subject_verb_agreement_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
regular_plural_subject_verb_agreement_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/regular_plural_subject_verb_agreement_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
sentential_negation_npi_licensor_present
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/sentential_negation_npi_licensor_present')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
sentential_negation_npi_scope
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/sentential_negation_npi_scope')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
sentential_subject_island
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/sentential_subject_island')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
superlative_quantifiers_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/superlative_quantifiers_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
superlative_quantifiers_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/superlative_quantifiers_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
tough_vs_raising_1
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/tough_vs_raising_1')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
tough_vs_raising_2
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/tough_vs_raising_2')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
transitive
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/transitive')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
wh_island
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/wh_island')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
wh_questions_object_gap
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/wh_questions_object_gap')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
wh_questions_subject_gap
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/wh_questions_subject_gap')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
wh_questions_subject_gap_long_distance
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/wh_questions_subject_gap_long_distance')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
wh_vs_that_no_gap
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/wh_vs_that_no_gap')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
wh_vs_that_no_gap_long_distance
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/wh_vs_that_no_gap_long_distance')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
wh_vs_that_with_gap
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/wh_vs_that_with_gap')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
wh_vs_that_with_gap_long_distance
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:blimp/wh_vs_that_with_gap_long_distance')
- 说明：
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.
- 许可：无已知许可
- 版本：0.1.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1000 |
- 特征：
{
"sentence_good": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence_bad": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"field": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"linguistics_term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"UID": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"simple_LM_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"one_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"two_prefix_method": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"lexically_identical": {
"dtype": "bool",
"id": null,
"_type": "Value"
},
"pair_id": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}