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
- Dataset Name: stanford-encyclopedia-philosophy
- Language: English
- Total Size: 182,531 demonstrations
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.