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GAU-α

基于Gated Attention Unit的Transformer模型(尝鲜版)

介绍

评测

CLUE榜单分类任务结果

iflytek tnews afqmc cmnli ocnli wsc csl
BERT 60.06 56.80 72.41 79.56 73.93 78.62 83.93
RoBERTa 60.64 58.06 74.05 81.24 76.00 87.50 84.50
RoFormer 60.91 57.54 73.52 80.92 76.07 86.84 84.63
RoFormerV2* 60.87 56.54 72.75 80.34 75.36 80.92 84.67
GAU-α 61.41 57.76 74.17 81.82 75.86 79.93 85.67

CLUE榜单阅读理解和NER结果

cmrc2018 c3 chid cluener
BERT 56.17 60.54 85.69 79.45
RoBERTa 56.54 67.66 86.71 79.47
RoFormer 56.26 67.24 86.57 79.72
RoFormerV2* 57.91 64.62 85.09 81.08
GAU-α 58.09 68.24 87.91 80.01

使用

需要bert4keras>=0.11.3。参考代码:

from bert4keras.models import build_transformer_model
from models import GAU_alpha

gau_model = build_transformer_model(
    config_path=config_path,
    checkpoint_path=checkpoint_path,
    model=GAU_alpha,
)

下载

引用

Bibtex:

@techreport{gau-alpha,
  title={GAU-α: GAU-based Transformers for NLP - ZhuiyiAI},
  author={Jianlin Su, Shengfeng Pan, Bo Wen, Yunfeng Liu},
  year={2022},
  url="https://github.com/ZhuiyiTechnology/GAU-alpha",
}

联系

gau-alpha's People

Contributors

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