Hello,
I built a FR-EN translator.
binarised phrase-table.minphr : 13.9G
binarized recorerig-table.minlexr 6.9G
language model model.blm.en 17.1G
I am using daemon.pl running as moses server.
It successfully open the port and loaded the translation model and language
mode. I can send
Yes, these errors happened during tuning with data like that. By the
original Python implementation, do you mean the one from the CoNLL 2014
shared task? (http://www.comp.nus.edu.sg/~nlp/conll14st.html, under
"Official Scorer")
Thanks so much for the advice! I'll fix up my data tomorrow and give
Are you tuning and testing on data like that? If yes this could be part of the
problem. The M2 scorer in Moses is not really tested and probably not well
suited for heavy duty (the original python implementation is even worse). So it
would definitely be better to make sure that not too much
Ah, good to know that the scorer was called successfully and that I can
ignore the Levenshtein distance errors.
As for allocating a huge piece of memory -- I realized that though my
parallel corpus is aligned, I actually split the original corpus by
*paragraph* instead of sentence. They're mostly
There seem to be multiple issues here.
As I said, I have null experience with EMS, so maybe someone else can help with
that.
The message in extract.err seems to actually mean, that you were successful in
calling the M2 scorer in EMS, the only problem is it dies The Levenshtein
message is
looping back in mailing-list and copying message :)
Thanks so much for the response, Marcin!
I did see your original repo, thanks for sending along. I'd love to get
this going with EMS because it looks like I can just pass in the M2 scorer
with:
tuning-settings = "-mertdir $moses-bin-dir
