Processing language data with deep learning models often involves a lookup of a pre-trained embedding model. In the model development phase, it's very annoying that every time the entire embeddings are loaded, as embeddings could be very big (e.g.,
Glove) and would consume a lot of timing loading it.
Is there a way to build/find a smaller and workable embedding just for debugging?
Right now what I've done is shrink the dimensions of
Glove, for example, reduce the dimension of embeddings from
30d by taking the first 30 dims, but that might potentially induce a risk that some of words will share the same embedding.