I'm trying to train an encoder-decoder transformer model for completion of binary-valued data. The each input is basically a length-n bitstring $x = (x_1, \dots, x_n) \in \{0,1\}^n$, generated according to some probability distribution, which has been masked to hide a subset of bits during training.
I am confused about what kind of embedding to use for this kind of input/output data. In the case of translation, one would tokenize the input text into $N$ tokens then pre-train an embedding from $\{1, \dots, N\} \rightarrow \mathbb{R}^d$, which seems reasonable when $N \gg d$. But for binary data using a single bit for a token means $N=2$, and so its not clear what the advantage is of embedding such a small alphabet of tokens into some high dimensional space.
What are some justifiable choices for embedding or tokenizing binary-valued data for use in a transformer?