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metadata
language:
  - en
task_categories:
  - text-classification
  - text-generation
dataset_info:
  features:
    - name: metadata
      dtype: string
    - name: text
      dtype: string
    - name: category
      dtype: string
  splits:
    - name: train
      num_bytes: 146747420
      num_examples: 182531
  download_size: 72070745
  dataset_size: 146747420
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: other

Stanford Encyclopedia Philosophy

Overview

The Stanford Encyclopedia of Philosophy (SEP) is a dynamic reference work, including over 1,770 entries written by top scholars in the field of philosophy. This dataset contains the full text of all articles contained within the SEP. Every row possesses information related to the original page (URL), the subject of the page (Category), and the text of the page (Text). This dataset can be used for NLP applications like text mining, classification, and generation.

Dataset Details

We will create a text dataset using the articles from the Stanford Encyclopedia of Philosophy

Title: The Stanford Encyclopedia of Philosophy
https://plato.stanford.edu/
Publisher:
The Metaphysics Research Lab
Philosophy Department
Stanford University
Stanford, CA 94305-4115
International Standard Serial Number: ISSN 1095-5054

Contents

The dataset consists of a data frame with the following columns:

  • metadata
  • label
  • category
{
  "metadata": https://plato.stanford.edu/entries/abduction/,
  "text": "See also the entry on scientific discovery, in particular the section on discovery as abduction.",
  "category": abduction
}

How to use

from datasets import load_dataset

dataset = load_dataset("AiresPucrs/stanford-encyclopedia-philosophy", split='train')

License

The Stanford Encyclopedia of Philosophy Dataset is licensed under the Other.

Cite as

@misc{teenytinycastle,
    doi = {10.5281/zenodo.7112065},
    url = {https://github.com/Nkluge-correa/TeenyTinyCastle},
    author = {Nicholas Kluge Corr{\^e}a},
    title = {Teeny-Tiny Castle},
    year = {2024},
    publisher = {GitHub},
    journal = {GitHub repository}
}

Disclaimer

This dataset is provided as is, without any warranty or guarantee of its accuracy or suitability for any purpose. The creators and contributors of this dataset are not liable for any damages or losses arising from its use. Please review and comply with the licenses and terms of the original datasets before use.