---
dataset_info:
- config_name: corpus
  features:
  - name: _id
    dtype: string
  - name: title
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: corpus
    num_bytes: 11814444.868325485
    num_examples: 22933
  download_size: 6046630
  dataset_size: 11814444.868325485
- config_name: default
  features:
  - name: query-id
    dtype: string
  - name: corpus-id
    dtype: string
  - name: score
    dtype: float64
  splits:
  - name: test
    num_bytes: 1110.7605332063795
    num_examples: 35
  download_size: 2196
  dataset_size: 1110.7605332063795
- config_name: queries
  features:
  - name: _id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: queries
    num_bytes: 1724.433371958285
    num_examples: 27
  download_size: 2922
  dataset_size: 1724.433371958285
configs:
- config_name: corpus
  data_files:
  - split: corpus
    path: corpus/corpus-*
- config_name: default
  data_files:
  - split: test
    path: data/test-*
- config_name: queries
  data_files:
  - split: queries
    path: queries/queries-*
task_categories:
- question-answering
language:
- en
tags:
- chemistry
- wikipedia
- nq
- natural questions
- chemteb
pretty_name: Chemical Natural Questions
size_categories:
- 10K<n<100K
license: cc-by-nc-sa-4.0
---
# Chemical Natural Questions

This dataset is created from the [mteb/nq](https://huggingface.co/datasets/mteb/nq) dataset on Hugging Face, which is part of the [Natural Questions](https://ai.google.com/research/NaturalQuestions/) dataset containing real user questions issued to Google search, with answers sourced from Wikipedia. In this chemistry-specific subset, we filtered queries related to chemistry by starting from the chemistry category in Wikipedia and traversing up to three levels deep in linked articles. This approach allowed us to focus on chemistry-related queries, providing a targeted subset of the original dataset for domain-specific retrieval tasks.