metadata
license: cc-by-nc-nd-4.0
dataset_info:
- config_name: corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 1009047
num_examples: 2761
download_size: 303399
dataset_size: 1009047
- config_name: data
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 98514
num_examples: 2761
download_size: 23445
dataset_size: 98514
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 140191
num_examples: 977
download_size: 69828
dataset_size: 140191
configs:
- config_name: corpus
data_files:
- split: test
path: corpus/test-*
- config_name: data
default: true
data_files:
- split: test
path: data/test-*
- config_name: queries
data_files:
- split: test
path: queries/test-*
task_categories:
- question-answering
language:
- en
size_categories:
- 1K<n<10K
Data sources
- Industry Foundation Classes (IFC) published by buildingSmart International: https://ifc43-docs.standards.buildingsmart.org/
- Uniclass product tables published by NBS: https://www.thenbs.com/our-tools/uniclass
License
- cc-by-nc-nd-4.0: https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en
How to cite
Research paper on the dataset development and validations: https://arxiv.org/abs/2411.12056
Note: This dataset refers to retrieval-p2p task as introduced in the paper.
@article{shahinmoghadam2024benchmarking,
title={Benchmarking pre-trained text embedding models in aligning built asset information},
author={Shahinmoghadam, Mehrzad and Motamedi, Ali},
journal={arXiv preprint arXiv:2411.12056},
year={2024}
}