Maybe experiment with Transformers, which are arguably the most advanced types of architectures for quite a few domains.
Some ideas:

BERT example: Classify text with BERT  |  Text  |  TensorFlow 1 (uses an “out-of-the-box” IMDB dataset)
A Keras example from the community: Text classification with Switch Transformer (uses an “out-of-the-box” IMDB dataset)
(External) Multi-Class Classification With Transformers: https://towardsdatascience.com/multi-class-classification-with-transformers-6cf7b59a033a (haven’t tested this but may be useful)




 Ekaterina_Dranitsyna:

You can start with this tutorial: Classify structured data with feature columns | TensorFlow Core 
It demonstrates how to deal with various data types.


+1 (note that Keras preprocessing layers and/or TF Text API may be more recommended cc @markdaoust)
Pre-processing the dataset and creating a pipeline could be a challenging task especially with a custom dataset. How about also: BERT Preprocessing with TF Text  |  TensorFlow and Text classification with an RNN  |  TensorFlow (if you want to try a less complex model first).