Each token in the input sequence is converted to a contextual embedding by a BERT-based encoder which is then input to a single-layer neural network. The output of the neural network is the entity type of the input token. Full size image ...
Note: One thing that I will explore in a later version is removing the last layer in the decoder. Normally, in autoencoders the number of encoders == number of decoders. We want, however, to extract higher level features (rather than creating the same input), so we can skip the last...
Note: One thing that I will explore in a later version is removing the last layer in the decoder. Normally, in autoencoders the number of encoders == number of decoders. We want, however, to extract higher level features (rather than creating the same input), so we can skip the last...
Note: One thing that I will explore in a later version is removing the last layer in the decoder. Normally, in autoencoders the number of encoders == number of decoders. We want, however, to extract higher level features (rather than creating the same input), so we can skip the last...
Note: One thing that I will explore in a later version is removing the last layer in the decoder. Normally, in autoencoders the number of encoders == number of decoders. We want, however, to extract higher level features (rather than creating the same input), so we can skip the last...
Note: One thing that I will explore in a later version is removing the last layer in the decoder. Normally, in autoencoders the number of encoders == number of decoders. We want, however, to extract higher level features (rather than creating the same input), so we can skip the last...
Note: One thing that I will explore in a later version is removing the last layer in the decoder. Normally, in autoencoders the number of encoders == number of decoders. We want, however, to extract higher level features (rather than creating the same input), so we can skip the last...
Note: One thing that I will explore in a later version is removing the last layer in the decoder. Normally, in autoencoders the number of encoders == number of decoders. We want, however, to extract higher level features (rather than creating the same input), so we can skip the last...
Note: One thing that I will explore in a later version is removing the last layer in the decoder. Normally, in autoencoders the number of encoders == number of decoders. We want, however, to extract higher level features (rather than creating the same input), so we can skip the last...
Note: One thing that I will explore in a later version is removing the last layer in the decoder. Normally, in autoencoders the number of encoders == number of decoders. We want, however, to extract higher level features (rather than creating the same input), so we can skip the last...