web_nlg

Referencje:

webnlg_challenge_2017

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:web_nlg/webnlg_challenge_2017')
  • Opis :
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).
  • Licencja : Brak znanej licencji
  • Wersja : 0.0.0
  • Podziały :
Podział Przykłady
'dev' 872
'test' 4615
'train' 6940
  • Cechy :
{
    "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"
    }
}

wydanie_v1

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:web_nlg/release_v1')
  • Opis :
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).
  • Licencja : Brak znanej licencji
  • Wersja : 0.0.0
  • Podziały :
Podział Przykłady
'full' 14237
  • Cechy :
{
    "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"
    }
}

wydanie_v2

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:web_nlg/release_v2')
  • Opis :
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).
  • Licencja : Brak znanej licencji
  • Wersja : 0.0.0
  • Podziały :
Podział Przykłady
'dev' 1619
'test' 1600
'train' 12876
  • Cechy :
{
    "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"
    }
}

wydanie_v2_ograniczone

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:web_nlg/release_v2_constrained')
  • Opis :
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).
  • Licencja : Brak znanej licencji
  • Wersja : 0.0.0
  • Podziały :
Podział Przykłady
'dev' 1594
'test' 1606
'train' 12895
  • Cechy :
{
    "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"
    }
}

wydanie_v2.1

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:web_nlg/release_v2.1')
  • Opis :
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).
  • Licencja : Brak znanej licencji
  • Wersja : 0.0.0
  • Podziały :
Podział Przykłady
'dev' 1619
'test' 1600
'train' 12876
  • Cechy :
{
    "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"
    }
}

wydanie_v2.1_ograniczone

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:web_nlg/release_v2.1_constrained')
  • Opis :
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).
  • Licencja : Brak znanej licencji
  • Wersja : 0.0.0
  • Podziały :
Podział Przykłady
'dev' 1594
'test' 1606
'train' 12895
  • Cechy :
{
    "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"
    }
}

wydanie_v3.0_en

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:web_nlg/release_v3.0_en')
  • Opis :
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).
  • Licencja : Brak znanej licencji
  • Wersja : 0.0.0
  • Podziały :
Podział Przykłady
'dev' 1667
'test' 5713
'train' 13211
  • Cechy :
{
    "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"
    }
}

wydanie_v3.0_ru

Użyj następującego polecenia, aby załadować ten zestaw danych do TFDS:

ds = tfds.load('huggingface:web_nlg/release_v3.0_ru')
  • Opis :
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).
  • Licencja : Brak znanej licencji
  • Wersja : 0.0.0
  • Podziały :
Podział Przykłady
'dev' 790
'test' 3410
'train' 5573
  • Cechy :
{
    "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"
    }
}