README.md

SelfAttentionLangModel

Language model based upon the Encoder units of the transformer. For Theortical back ground please refere to Attention is all you need paper @(https://arxiv.org/abs/1706.03762) and for detials regard the impelementation please refere to the source code here and to google tutorial avilable at https://www.tensorflow.org/beta/tutorials/text/transformer.

To DO:

1- build a pip package for the library. 2- more documentation and examples

Notes:

because of the difference in the bifurcating condition between return self-attention weights and outputs and only the output the fit method is not an applicable and a custom training loop should be used

Current State:

The Modeler and Annotator Models are ready for deployment.

Examples:

from SelfAttentionLangModel.Models import EncoderModels

demoModel=EncoderModels.Modeler(

                                  embedding_dim=16,
                                  vocabulary_size=28,
                                  conditional_string_length=30,
                                  num_encoder_layer=6,
                                  num_heads=4,
                                  num_neuron_pointwise=32,
                                  rate=0.1,\n
                                  return_attent_weights=False
                                     )