README.md

metadata

dataset_info:
  features:
    - name: text
      dtype: string
    - name: label
      dtype: int64
    - name: label_text
      dtype: string
  splits:
    - name: train
      num_bytes: 330105
      num_examples: 392
    - name: test
      num_bytes: 87036
      num_examples: 99
  download_size: 251248
  dataset_size: 417141
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
license: cc-by-nc-sa-4.0
task_categories:
  - text-classification
language:
  - en
tags:
  - chemistry
  - wikipedia
  - chemteb
pretty_name: Wikipedia Salts vs Semiconductor Materials Binary Classification
size_categories:
  - n<1K

Wikipedia Salts vs Semiconductor Materials Binary Classification

This dataset is derived from the English Wikipedia articles and is designed for binary text classification tasks in the fields of chemistry and materials science. The dataset is divided into two classes based on the thematic content of the articles:

• Salts: This class includes articles that focus on salts, which are ionic compounds composed of cations and anions. Topics may cover the properties, types, synthesis, and applications of various salts in different fields such as chemistry, biology, and industry.
• Semiconductor Materials: This class comprises articles related to semiconductor materials, which have electrical conductivity between that of a conductor and an insulator. Topics may include the properties, types, synthesis, and applications of semiconductor materials in electronics, photovoltaics, and other technologies.