relation-classification
Robust-Biomed-RoBERTa-RelationClassificationgene_relation_classificationrelation_classification_tacred_revisitedargument_relation_classification_UKP_sentence_robertarelation_classification_single_labelrelation_classification_single_label_correlationsrelation_classification_correlationsrelation-classification_balanced_3rdtry
task1418_bless_semantic_relation_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1418_bless_semantic_relation_classification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1418_bless_semantic_relation_classification.lexical_relation_classification[Lexical Relation Classification](https://aclanthology.org/P19-1169/)task1429_evalution_semantic_relation_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1429_evalution_semantic_relation_classification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1429_evalution_semantic_relation_classification.people_relation_classification本数据集用于人物关系分类，一共14种关系类型：不确定, 夫妻, 父母, 兄弟姐妹, 上下级, 师生, 好友, 同学, 合作, 同一个人, 情侣, 祖孙, 同门, 亲戚。
本数据集共3881条，其中训练集3105条，测试集776条，参看train.csv和test.csv。
数据集的人物关系分布如下：
关于使用R-BERT模型训练该数据集，可参考文章：NLP（四十二）人物关系分类的再次尝试.
Text-Classification-and-Relation-Event-Extraction-Mix-datasetsThe paper of GIELLM dataset.
https://arxiv.org/abs/2311.06838
Cite:
@article{gan2023giellm,
title={Giellm: Japanese general information extraction large language model utilizing mutual reinforcement effect},
author={Gan, Chengguang and Zhang, Qinghao and Mori, Tatsunori},
journal={arXiv preprint arXiv:2311.06838},
year={2023}
}
The dataset constructed base in livedoor news corpus 関口宏司 https://www.rondhuit.com/download.html
task1505_root09_semantic_relation_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1505_root09_semantic_relation_classification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1505_root09_semantic_relation_classification.