|
--- |
|
license: cc-by-nc-nd-4.0 |
|
dataset_info: |
|
features: |
|
- name: sentences |
|
sequence: string |
|
- name: labels |
|
sequence: string |
|
splits: |
|
- name: test |
|
num_bytes: 1259522 |
|
num_examples: 20 |
|
download_size: 318366 |
|
dataset_size: 1259522 |
|
configs: |
|
- config_name: default |
|
data_files: |
|
- split: test |
|
path: data/test-* |
|
task_categories: |
|
- text-classification |
|
language: |
|
- en |
|
pretty_name: Built-Bench-Clustering-P2P |
|
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 |
|
|
|
``` |
|
@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} |
|
} |
|
``` |
