id_liputan6

Referencias:

• Código
• cara de abrazo

canónico

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:id_liputan6/canonical')

• Descripción :

In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from this http URL,
an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop
benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual
BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have
low ROUGE scores, and expose both issues with ROUGE it-self, as well as with extractive and abstractive
summarization models.

• Licencia : Sin licencia conocida
• Versión : 1.0.0
• Divisiones :

• Características :

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "url": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "clean_article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "clean_summary": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "extractive_summary": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

extremo

Utilice el siguiente comando para cargar este conjunto de datos en TFDS:

ds = tfds.load('huggingface:id_liputan6/xtreme')

• Descripción :

In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from this http URL,
an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop
benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual
BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have
low ROUGE scores, and expose both issues with ROUGE it-self, as well as with extractive and abstractive
summarization models.

• Licencia : Sin licencia conocida
• Versión : 1.0.0
• Divisiones :

• Características :

{
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "url": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "clean_article": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "clean_summary": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "extractive_summary": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}