HyperAI超神经

Text Classification On Hatexplain 1

评估指标

Accuracy (2 classes)

F1 Macro

评测结果

各个模型在此基准测试上的表现结果

			Paper Title
Space-XLNet	0.8798	0.8797	Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
XLNet	0.8160	0.8156	Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
Space-BERT	0.8110	0.8108	Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
BERT-base	0.6588	0.6555	Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs

0 of 4 row(s) selected.

HyperAI超神经

Text Classification On Hatexplain 1

评估指标

Accuracy (2 classes)

F1 Macro

评测结果

各个模型在此基准测试上的表现结果

			Paper Title
Space-XLNet	0.8798	0.8797	Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
XLNet	0.8160	0.8156	Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
Space-BERT	0.8110	0.8108	Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
BERT-base	0.6588	0.6555	Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs

0 of 4 row(s) selected.

Text Classification On Hatexplain 1 | SOTA | HyperAI超神经