참고자료:
generics_kb_best
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:generics_kb/generics_kb_best')
- 설명 :
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.
- 라이센스 : cc-by-4.0
- 버전 : 1.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'train' | 1020868 |
- 특징 :
{
"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
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:generics_kb/generics_kb')
- 설명 :
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.
- 라이센스 : cc-by-4.0
- 버전 : 1.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'train' | 3433000 |
- 특징 :
{
"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
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:generics_kb/generics_kb_simplewiki')
- 설명 :
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.
- 라이센스 : cc-by-4.0
- 버전 : 1.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'train' | 12765 |
- 특징 :
{
"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
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:generics_kb/generics_kb_waterloo')
- 설명 :
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.
- 라이센스 : cc-by-4.0
- 버전 : 1.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'train' | 3666725 |
- 특징 :
{
"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"
}
}