Hi everyone,
We have trained a good language model, using awd_lstm architecture. Then we have saved it using learner.save()
Now we would like to build our own classifier. We can get the AWD_LSTM
from fastai.text.models
, but then we need to separately store at least the vocab_sz, emb_sz, n_hid and n_layers - the required arguments.
We thought about creating a new learner, loading the saved artifact and then extracting the encoder from there, but this seems like overkill.
Is there a cleaner way to load the encoder from the saved learner.save()
file?