I have an idea of
1. RNN Embedding
2. RNN with pack padded sequence
3. FastTest
4. Bi-LSTM
5. CNN
6. CNN-LSTM
7. BERT Transformer
these models.
I am looking model apart form these.
You can use MobileBERT, it's a compact BERT model open sourced on GitHub. ... or the other Google open source models using projection methods, namely SGNN, PRADO and pQRNN. pQRNN is much smaller than BERT, but is quantized, and can nearly achieve BERT-level performance, despite being 300x smaller and being trained on only supervised data.
I suggest you follow https://arxiv.org/pdf/2004.03705.pdf
Via MATLAB : https://au.mathworks.com/help/textanalytics/ug/classify-text-data-using-deep-learning.html
https://au.mathworks.com/help/deeplearning/ug/classify-text-data-using-convolutional-neural-network.html
https://au.mathworks.com/help/deeplearning/ug/multilabel-text-classification-using-deep-learning.html
https://machinelearningmastery.com/best-practices-document-classification-deep-learning/
Please have a look
You can use karas library for you project ..... That will help you alot
https://keras.io/api/preprocessing/text/
I hope it was helpful
Ensemble Deep Learning based Multilabel Binary Classification can be used for text classification.
Further, I suggest the use of gated recurrent unit and support vector machine for some remarkable results. The gated recurrent unit can perform well in sequential learning tasks and overcomes the issues of vanishing and explosion of gradients in standard recurrent neural networks when capturing long-term dependencies.
You can use MobileBERT, it's a compact BERT model open sourced on GitHub. ... or the other Google open source models using projection methods, namely SGNN, PRADO and pQRNN. pQRNN is much smaller than BERT, but is quantized, and can nearly achieve BERT-level performance, despite being 300x smaller and being trained on only supervised data.
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