--- 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`](https://plato.stanford.edu/) ``` 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](https://huggingface.co/datasets/AiresPucrs/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 ```bash { "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 ```python 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](https://plato.stanford.edu/info.html). # 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.