Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
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. |