ALBERT
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations,
Zhenzhong Lan et al.
Same as BERT but with a few tweaks:
Embedding size E is different from hidden size H justified because the embeddings are context independent (one
  embedding vector represents one token), whereas hidden states are context dependent (one hidden state represents a
  sequence of tokens) so it's more logical to have H >> E. Also, the embedding matrix is large since it's V x E (V
  being the vocab size). If E < H, it has less parameters.
Layers are split in groups that share parameters (to save memory).
Next sentence prediction is replaced by a sentence ordering prediction: in the inputs, we have two sentences A and
  B (that are consecutive) and we either feed A followed by B or B followed by A. The model must predict if they have
