Referensi:
webnlg_challenge_2017
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:web_nlg/webnlg_challenge_2017')
- Keterangan :
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).
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 0.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'dev' | 872 |
'test' | 4615 |
'train' | 6940 |
- Fitur :
{
"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"
}
}
rilis_v1
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:web_nlg/release_v1')
- Keterangan :
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).
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 0.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'full' | 14237 |
- Fitur :
{
"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"
}
}
rilis_v2
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:web_nlg/release_v2')
- Keterangan :
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).
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 0.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'dev' | 1619 |
'test' | 1600 |
'train' | 12876 |
- Fitur :
{
"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"
}
}
rilis_v2_dibatasi
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:web_nlg/release_v2_constrained')
- Keterangan :
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).
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 0.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'dev' | 1594 |
'test' | 1606 |
'train' | 12895 |
- Fitur :
{
"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"
}
}
rilis_v2.1
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:web_nlg/release_v2.1')
- Keterangan :
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).
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 0.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'dev' | 1619 |
'test' | 1600 |
'train' | 12876 |
- Fitur :
{
"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"
}
}
rilis_v2.1_terbatas
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:web_nlg/release_v2.1_constrained')
- Keterangan :
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).
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 0.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'dev' | 1594 |
'test' | 1606 |
'train' | 12895 |
- Fitur :
{
"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"
}
}
rilis_v3.0_en
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:web_nlg/release_v3.0_en')
- Keterangan :
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).
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 0.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'dev' | 1667 |
'test' | 5713 |
'train' | 13211 |
- Fitur :
{
"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"
}
}
rilis_v3.0_ru
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:web_nlg/release_v3.0_ru')
- Keterangan :
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).
- Lisensi : Tidak ada lisensi yang diketahui
- Versi : 0.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'dev' | 790 |
'test' | 3410 |
'train' | 5573 |
- Fitur :
{
"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"
}
}