text classification performance in NLP with deep learning

摘自:https://github.com/brightmart/text_classification

Performance

(mulit-label label prediction task,ask to prediction top5, 3 million training data,full score:0.5)

Model fastText TextCNN TextRNN RCNN HierAtteNet Seq2seqAttn EntityNet DynamicMemory Transformer
Score 0.362 0.405 0.358 0.395 0.398 0.322 0.400 0.392 0.322
Training 10m 2h 10h 2h 2h 3h 3h 5h 7h

Bert model achieves 0.368 after first 9 epoch from validation set.

Ensemble of TextCNN,EntityNet,DynamicMemory: 0.411

Ensemble EntityNet,DynamicMemory: 0.403

如何避免社交网络禁止外国号码使用外国手机号码注册 文本相似度计算方法进行实验与比较
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