Implementation of State-of-the-art Text Classification Models in Pytorch
Implementation of State-of-the-art Text Classification Models in Pytorch
python train.py <path_to_training_file> <path_to_test_file>
Model | Dataset | |||
---|---|---|---|---|
AG_News | Query_Well_formedness | |||
Accuracy (%) | Runtime | Accuracy (%) | Runtime | |
fastText | 89.46 | 16.0 Mins | 62.10 | 7.0 Mins |
TextCNN | 88.57 | 17.2 Mins | 67.38 | 7.43 Mins |
TextRNN | 88.07 (Seq len = 20) 90.43 (Flexible seq len) |
21.5 Mins 36.8 Mins |
68.29 66.29 |
7.69 Mins 7.25 Mins |
RCNN | 90.61 | 22.73 Mins | 66.70 | 7.21 Mins |
CharCNN | 87.70 | 13.08 Mins | 68.83 | 2.49 Mins |
Seq2Seq_Attention | 90.26 | 19.10 Mins | 67.84 | 7.36 Mins |
Transformer | 88.54 | 46.47 Mins | 63.43 | 5.77 Mins |
[1] Bag of Tricks for Efficient Text Classification
[2] Convolutional Neural Networks for Sentence Classification
[3] Recurrent Convolutional Neural Networks for Text Classification
[4] Character-level Convolutional Networks for Text Classification
[5] Neural Machine Translation by Jointly Learning to Align and Translate
[6] Text Classification Research with Attention-based Recurrent Neural Networks
[7] Attention Is All You Need
[8] Rethinking the Inception Architecture for Computer Vision
[9] Identifying Well-formed Natural Language Questions