How to get wt103 model

I would like to work with the pretrained AWD-LSTM, to analyze Wikipedia text extracts.
So the weights fitted to wikitext are just what I need.

Reading the docs, it looks like you are supposed to load these models with language_model_learner. This is the only function that takes an URL for a pretrained model. But it also expects a DataBunch and will change the models embedding to suit that vocabulary.

I don’t want to change the embedding, I just want the pretrained WT103 model, with its wikitext vocabulary. So I read my way through the code for loading a model and came up with this solution to load a WT103 model, without changing anything in it:

# get weights and itos
model_path = untar_data(URLs.WT103, data=False)
fnames = [list(model_path.glob(f'*.{ext}'))[0] for ext in ['pth', 'pkl']]
wgts_fname, itos_fname = fnames
itos = pickle.load(open(itos_fname, 'rb'))
wgts = torch.load(wgts_fname, map_location=lambda storage, loc: storage)
# get parameters for language model
default_dropout = {'language': np.array([0.25, 0.1, 0.2, 0.02, 0.15]),
                   'classifier': np.array([0.4, 0.5, 0.05, 0.3, 0.4])}
drop_mult = 1.
tie_weights = True
bias = True
qrnn = False
dps = default_dropout['language'] * drop_mult
bptt = 70
vocab_size = len(itos)
emb_sz = 400
nh = 1150
nl = 3
pad_token = 1
drop_mult = 1.

model = get_language_model(vocab_size, emb_sz, nh, nl, pad_token, input_p=dps[0], output_p=dps[1],
                           weight_p=dps[2], embed_p=dps[3], hidden_p=dps[4], tie_weights=tie_weights, bias=bias,

# load weights into model

Is this the way to do it ?
It seems strange that loading a model together with a dataset is a one-liner. But if you want just the model you have to dig in the code.

Why can’t you just visit the URL in the string and download it if you just want the model?