reddit

• Deskripsi :

Korpus ini berisi postingan yang telah diproses sebelumnya dari kumpulan data Reddit. Dataset terdiri dari 3.848.330 postingan dengan panjang rata-rata 270 kata untuk konten, dan 28 kata untuk ringkasan.

Fitur termasuk string: penulis, badan, badan normal, konten, ringkasan, subreddit, subreddit_id. Konten digunakan sebagai dokumen dan ringkasan digunakan sebagai ringkasan.

• Dokumentasi Tambahan : Jelajahi di Makalah Dengan Kode

• Beranda : https://github.com/webis-de/webis-tldr-17-corpus

• Kode sumber : tfds.datasets.reddit.Builder

• Versi :

  • 1.0.0 (default): Tidak ada catatan rilis.

• Ukuran unduhan : 2.93 GiB

• Ukuran dataset : 18.09 GiB

• Di-cache otomatis ( dokumentasi ): Tidak

• Perpecahan :

Membelah	Contoh
'train'	3.848.330

• Struktur fitur :

FeaturesDict({
    'author': string,
    'body': string,
    'content': string,
    'id': string,
    'normalizedBody': string,
    'subreddit': string,
    'subreddit_id': string,
    'summary': string,
})

• Dokumentasi fitur :

Fitur	Kelas	Membentuk	Dtype	Keterangan
	fiturDict
pengarang	Tensor		rangkaian
tubuh	Tensor		rangkaian
isi	Tensor		rangkaian
Indo	Tensor		rangkaian
normalizedBody	Tensor		rangkaian
subreddit	Tensor		rangkaian
subreddit_id	Tensor		rangkaian
ringkasan	Tensor		rangkaian

• Kunci yang diawasi (Lihat as_supervised doc ): ('content', 'summary')

• Gambar ( tfds.show_examples ): Tidak didukung.

• Contoh ( tfds.as_dataframe ):

• Kutipan :

@inproceedings{volske-etal-2017-tl,
    title = "{TL};{DR}: Mining {R}eddit to Learn Automatic Summarization",
    author = {V{\"o}lske, Michael  and
      Potthast, Martin  and
      Syed, Shahbaz  and
      Stein, Benno},
    booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W17-4508",
    doi = "10.18653/v1/W17-4508",
    pages = "59--63",
    abstract = "Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.",
}