update docs

  then max-over-time pooling is applied on all filters, extracting one feature per filter.

  All features are concatenated and transformed through several hidden layers to form predictions.

  This model is initially developed for sentence-level classification tasks, with

  words represented as vectors. In this implementation, SMILES strings are dissected

  into characters and transformed to one-hot vectors in a similar way. The model can

  be used for general molecular-level classification or regression tasks. It is also

  used in the ORGAN model as discriminator.

  Training of the model only requires SMILES strings input, all featurized datasets

  that include SMILES in the `ids` attribute are accepted. PDBbind, QM7 and QM7b 

  are not supported. To use the model, `build_char_dict` should be called first

  before defining the model to build character dict of input dataset, example can

  be found in examples/delaney/delaney_textcnn.py

  """

  def __init__(