参考：
generics_kb_best
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:generics_kb/generics_kb_best')
- 说明：
The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as "Dogs bark," and "Trees remove carbon dioxide from the atmosphere." Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large-scale resource containing naturally occurring generic sentences (as opposed to extracted or crowdsourced triples), and is rich in high-quality, general, semantically complete statements. Generics were primarily extracted from three large text sources, namely the Waterloo Corpus, selected parts of Simple Wikipedia, and the ARC Corpus. A filtered, high-quality subset is also available in GenericsKB-Best, containing 1,020,868 sentences. We recommend you start with GenericsKB-Best.
- 许可：cc-by-4.0
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
1020868 |
- 特征：
{
"source": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"quantifier_frequency": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"quantifier_number": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"generic_sentence": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"score": {
"dtype": "float64",
"id": null,
"_type": "Value"
}
}
generics_kb
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:generics_kb/generics_kb')
- 说明：
The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as "Dogs bark," and "Trees remove carbon dioxide from the atmosphere." Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large-scale resource containing naturally occurring generic sentences (as opposed to extracted or crowdsourced triples), and is rich in high-quality, general, semantically complete statements. Generics were primarily extracted from three large text sources, namely the Waterloo Corpus, selected parts of Simple Wikipedia, and the ARC Corpus. A filtered, high-quality subset is also available in GenericsKB-Best, containing 1,020,868 sentences. We recommend you start with GenericsKB-Best.
- 许可：cc-by-4.0
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
3433000 |
- 特征：
{
"source": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"term": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"quantifier_frequency": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"quantifier_number": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"generic_sentence": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"score": {
"dtype": "float64",
"id": null,
"_type": "Value"
}
}
generics_kb_simplewiki
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:generics_kb/generics_kb_simplewiki')
- 说明：
The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as "Dogs bark," and "Trees remove carbon dioxide from the atmosphere." Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large-scale resource containing naturally occurring generic sentences (as opposed to extracted or crowdsourced triples), and is rich in high-quality, general, semantically complete statements. Generics were primarily extracted from three large text sources, namely the Waterloo Corpus, selected parts of Simple Wikipedia, and the ARC Corpus. A filtered, high-quality subset is also available in GenericsKB-Best, containing 1,020,868 sentences. We recommend you start with GenericsKB-Best.
- 许可：cc-by-4.0
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
12765 |
- 特征：
{
"source_name": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentences_before": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"sentences_after": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"concept_name": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"quantifiers": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"bert_score": {
"dtype": "float64",
"id": null,
"_type": "Value"
},
"headings": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"categories": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
generics_kb_waterloo
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:generics_kb/generics_kb_waterloo')
- 说明：
The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as "Dogs bark," and "Trees remove carbon dioxide from the atmosphere." Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large-scale resource containing naturally occurring generic sentences (as opposed to extracted or crowdsourced triples), and is rich in high-quality, general, semantically complete statements. Generics were primarily extracted from three large text sources, namely the Waterloo Corpus, selected parts of Simple Wikipedia, and the ARC Corpus. A filtered, high-quality subset is also available in GenericsKB-Best, containing 1,020,868 sentences. We recommend you start with GenericsKB-Best.
- 许可：cc-by-4.0
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'train' |
3666725 |
- 特征：
{
"source_name": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentence": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"sentences_before": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"sentences_after": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"concept_name": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"quantifiers": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"bert_score": {
"dtype": "float64",
"id": null,
"_type": "Value"
}
}