web_nlg

مراجع:

• کد
• صورت در آغوش گرفته

webnlg_challenge_2017

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:web_nlg/webnlg_challenge_2017')

• توضیحات :

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).

• مجوز : مجوز شناخته شده ای وجود ندارد
• نسخه : 0.0.0
• تقسیمات :

• ویژگی ها :

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    "eid": {
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    "original_triple_sets": {
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    "lex": {
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                "_type": "Value"
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            "lid": {
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    "test_category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
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    "dbpedia_links": {
        "feature": {
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}

release_v1

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:web_nlg/release_v1')

• توضیحات :

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).

• مجوز : مجوز شناخته شده ای وجود ندارد
• نسخه : 0.0.0
• تقسیمات :

• ویژگی ها :

{
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        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "size": {
        "dtype": "int32",
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    "eid": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "original_triple_sets": {
        "feature": {
            "otriple_set": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
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                "length": -1,
                "id": null,
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            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "modified_triple_sets": {
        "feature": {
            "mtriple_set": {
                "feature": {
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                },
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                "id": null,
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        "length": -1,
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    "shape": {
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    "shape_type": {
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    },
    "lex": {
        "feature": {
            "comment": {
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                "id": null,
                "_type": "Value"
            },
            "lid": {
                "dtype": "string",
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            "text": {
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        },
        "length": -1,
        "id": null,
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    "test_category": {
        "dtype": "string",
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    "dbpedia_links": {
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        "length": -1,
        "id": null,
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}

release_v2

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:web_nlg/release_v2')

• توضیحات :

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).

• مجوز : مجوز شناخته شده ای وجود ندارد
• نسخه : 0.0.0
• تقسیمات :

• ویژگی ها :

{
    "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": {
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                },
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                "id": null,
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    },
    "lex": {
        "feature": {
            "comment": {
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                "id": null,
                "_type": "Value"
            },
            "lid": {
                "dtype": "string",
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            "text": {
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        },
        "length": -1,
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    },
    "test_category": {
        "dtype": "string",
        "id": null,
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    },
    "dbpedia_links": {
        "feature": {
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            "_type": "Value"
        },
        "length": -1,
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    "links": {
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        },
        "length": -1,
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    }
}

release_v2_constrained

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:web_nlg/release_v2_constrained')

• توضیحات :

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).

• مجوز : مجوز شناخته شده ای وجود ندارد
• نسخه : 0.0.0
• تقسیمات :

• ویژگی ها :

{
    "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": {
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                },
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    "lex": {
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            "lid": {
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    "test_category": {
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    "dbpedia_links": {
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    "links": {
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        "length": -1,
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    }
}

release_v2.1

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:web_nlg/release_v2.1')

• توضیحات :

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).

• مجوز : مجوز شناخته شده ای وجود ندارد
• نسخه : 0.0.0
• تقسیمات :

• ویژگی ها :

{
    "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"
                },
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                "id": null,
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            }
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        "length": -1,
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    },
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}

release_v2.1_constrained

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:web_nlg/release_v2.1_constrained')

• توضیحات :

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).

• مجوز : مجوز شناخته شده ای وجود ندارد
• نسخه : 0.0.0
• تقسیمات :

• ویژگی ها :

{
    "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"
                },
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                "id": null,
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            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "modified_triple_sets": {
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                "feature": {
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                    "_type": "Value"
                },
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    "lex": {
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    "test_category": {
        "dtype": "string",
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    },
    "dbpedia_links": {
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        "length": -1,
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        "id": null,
        "_type": "Sequence"
    }
}

release_v3.0_en

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:web_nlg/release_v3.0_en')

• توضیحات :

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).

• مجوز : مجوز شناخته شده ای وجود ندارد
• نسخه : 0.0.0
• تقسیمات :

• ویژگی ها :

{
    "category": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "size": {
        "dtype": "int32",
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    },
    "eid": {
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        "id": null,
        "_type": "Value"
    },
    "original_triple_sets": {
        "feature": {
            "otriple_set": {
                "feature": {
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                },
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            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "modified_triple_sets": {
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            "mtriple_set": {
                "feature": {
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                    "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

برای بارگذاری این مجموعه داده در TFDS از دستور زیر استفاده کنید:

ds = tfds.load('huggingface:web_nlg/release_v3.0_ru')

• توضیحات :

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).

• مجوز : مجوز شناخته شده ای وجود ندارد
• نسخه : 0.0.0
• تقسیمات :

• ویژگی ها :

{
    "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"
    }
}