task_categories:text-classification task_categories:text-retrieval task_categories:token-classification task_ids:multi-label-classification language_creators:expert-generated multilinguality:multilingual size_categories:100K<n<1M source_datasets:original license:apache-2.0 humanitarian research analytical-framework multilabel humset humbert bias gender-bias country-bias region:us
"""
#@misc{https://doi.org/10.48550/arxiv.2210.04573,
#  doi = {10.48550/ARXIV.2210.04573},
#  url = {https://arxiv.org/abs/2210.04573},
#  author = {Fekih, Selim and Tamagnone, Nicolò and Minixhofer, Benjamin and Shrestha, Ranjan and Contla, Ximena and Oglethorpe, Ewan and Rekabsaz, Navid},
#  keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
#  title = {HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response},
#  publisher = {arXiv},
#  year = {2022},
#  copyright = {arXiv.org perpetual, non-exclusive license}
#}
#
---
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: HumSetBias
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- humanitarian
- research
- analytical-framework
- multilabel
- humset
- humbert
- bias
- gender-bias
- country-bias
task_categories:
- text-classification
- text-retrieval
- token-classification
task_ids:
- multi-label-classification
---