Added scripts on how to use the library for recsys in response to issue #133. Also Added a simple/basic transformer model for the text component before integrating with HF. Also added the option of specify the dimension of the feed forward network

Added Self Supervised Pretraining for all tabular models. Need to unit test and surely there will be some code readjustments

Cleaned and refactored the code for the attentive mlps. Adjusted unit tests. Docs build. Ready to move to the Bayesian mlp

First commit towards v2. Re-organized the models module and added a few new functionalities for the models in there

Added a proper implementation of transformer models when needed and re-organised the transformers module for clarity

finished implementing TabNet encoder. Next I need to implement the interpretability methods. Also adjusted WideDeep class to be able to work with TabNet. Renamed some example scripts to be consistent with each other

redo all docs to match pytorch theme. Adjust all documentation to the new code structure. Remove partial imports from...everywhere. Updated examples

changed the dense layer to be almost identical to that of fastai, which I really like. Changed the code accordingly. Changed the name of DeepDense and DeepDenseResnet to TabMlp and TabResnet. Change the tests acccordingly