Comments (5)
Thank you Amit for pointing out this bug. loader
actually is in language_model
. I've just fixed this.
from pseudo-ref.
Great, thanks!
One more dependency missing is nltk
.
Also, it now fails to import Model
:
========= READ DATA COMPLETE ===========
Traceback (most recent call last):
File "lattice.py", line 607, in
lm_model = torch.load(f, map_location=lambda storage, loc: storage)
File "/home/nlp/amit/anaconda2/envs/pseudo-ref/lib/python2.7/site-packages/torch/serialization.py", line 358, in load
return _load(f, map_location, pickle_module)
File "/home/nlp/amit/anaconda2/envs/pseudo-ref/lib/python2.7/site-packages/torch/serialization.py", line 542, in _load
result = unpickler.load()
ImportError: No module named Model
from pseudo-ref.
It's fixed now. language_model
should be added into path.
from pseudo-ref.
Thanks. This works! I am running the soft code overnight, as it seems to take seconds for some sentences, but many minutes for others, but I ran it on a sample and it worked.
from pseudo-ref.
Thanks. This works! I am running the soft code overnight, as it seems to take seconds for some sentences, but many minutes for others, but I ran it on a sample and it worked.
Yes. If the lattice is complex, it will take a long time to generate the pseudo-sentences. Actually you can do some optimization on it, e.g. cache the suffixes of each nodes instead of simple DFS.
from pseudo-ref.
Related Issues (2)
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