참고자료:
webnlg_challenge_2017
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:web_nlg/webnlg_challenge_2017')
- 설명 :
The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text).
- 라이센스 : 알려진 라이센스 없음
- 버전 : 0.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'dev' | 872 |
'test' | 4615 |
'train' | 6940 |
- 특징 :
{
"category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"size": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"eid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"original_triple_sets": {
"feature": {
"otriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"modified_triple_sets": {
"feature": {
"mtriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"shape": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"shape_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lex": {
"feature": {
"comment": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lang": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"test_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"dbpedia_links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
release_v1
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:web_nlg/release_v1')
- 설명 :
The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text).
- 라이센스 : 알려진 라이센스 없음
- 버전 : 0.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'full' | 14237 |
- 특징 :
{
"category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"size": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"eid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"original_triple_sets": {
"feature": {
"otriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"modified_triple_sets": {
"feature": {
"mtriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"shape": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"shape_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lex": {
"feature": {
"comment": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lang": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"test_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"dbpedia_links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
release_v2
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:web_nlg/release_v2')
- 설명 :
The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text).
- 라이센스 : 알려진 라이센스 없음
- 버전 : 0.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'dev' | 1619년 |
'test' | 1600 |
'train' | 12876 |
- 특징 :
{
"category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"size": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"eid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"original_triple_sets": {
"feature": {
"otriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"modified_triple_sets": {
"feature": {
"mtriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"shape": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"shape_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lex": {
"feature": {
"comment": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lang": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"test_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"dbpedia_links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
release_v2_constrained
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:web_nlg/release_v2_constrained')
- 설명 :
The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text).
- 라이센스 : 알려진 라이센스 없음
- 버전 : 0.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'dev' | 1594년 |
'test' | 1606 |
'train' | 12895 |
- 특징 :
{
"category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"size": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"eid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"original_triple_sets": {
"feature": {
"otriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"modified_triple_sets": {
"feature": {
"mtriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"shape": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"shape_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lex": {
"feature": {
"comment": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lang": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"test_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"dbpedia_links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
release_v2.1
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:web_nlg/release_v2.1')
- 설명 :
The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text).
- 라이센스 : 알려진 라이센스 없음
- 버전 : 0.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'dev' | 1619년 |
'test' | 1600 |
'train' | 12876 |
- 특징 :
{
"category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"size": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"eid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"original_triple_sets": {
"feature": {
"otriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"modified_triple_sets": {
"feature": {
"mtriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"shape": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"shape_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lex": {
"feature": {
"comment": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lang": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"test_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"dbpedia_links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
release_v2.1_제약됨
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:web_nlg/release_v2.1_constrained')
- 설명 :
The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text).
- 라이센스 : 알려진 라이센스 없음
- 버전 : 0.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'dev' | 1594년 |
'test' | 1606 |
'train' | 12895 |
- 특징 :
{
"category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"size": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"eid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"original_triple_sets": {
"feature": {
"otriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"modified_triple_sets": {
"feature": {
"mtriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"shape": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"shape_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lex": {
"feature": {
"comment": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lang": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"test_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"dbpedia_links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
release_v3.0_en
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:web_nlg/release_v3.0_en')
- 설명 :
The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text).
- 라이센스 : 알려진 라이센스 없음
- 버전 : 0.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'dev' | 1667년 |
'test' | 5713 |
'train' | 13211 |
- 특징 :
{
"category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"size": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"eid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"original_triple_sets": {
"feature": {
"otriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"modified_triple_sets": {
"feature": {
"mtriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"shape": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"shape_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lex": {
"feature": {
"comment": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lang": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"test_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"dbpedia_links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
release_v3.0_ru
TFDS에 이 데이터세트를 로드하려면 다음 명령어를 사용하세요.
ds = tfds.load('huggingface:web_nlg/release_v3.0_ru')
- 설명 :
The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
aggregation (how to avoid repetitions) and surface realisation
(how to build a syntactically correct and natural sounding text).
- 라이센스 : 알려진 라이센스 없음
- 버전 : 0.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'dev' | 790 |
'test' | 3410 |
'train' | 5573 |
- 특징 :
{
"category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"size": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"eid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"original_triple_sets": {
"feature": {
"otriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"modified_triple_sets": {
"feature": {
"mtriple_set": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"shape": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"shape_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lex": {
"feature": {
"comment": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lid": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"lang": {
"dtype": "string",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"test_category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"dbpedia_links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"links": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}