Hi Per

If you add the following to mert-moses.pl then it should work:

-filtercmd '<path-to-filter-script> -Binarizer 
"<path-to-processPhraseTable>"'

Obviously you replace the angle-bracket expressions with the paths, and 
you might have to fiddle with the quotes,

cheers - Barry

On 11/03/13 16:23, Per Tunedal wrote:
> Hi Barry,
> unfortunately, I don't fully understand your advice, as I'm not familiar
> with how the scripts work etc.
> At this stage I just copy some commands, and make some minor changes I
> find appropriate. I'm at the script-kiddie stage!
>
> 1. What command shall I submit if I will let the mert-moses.pl script do
> the job, as I first intended?
> 2. What commands shall I submit if I choose to do some of the job before
> running the mert-moses.pl script?
>
> Yours,
> Per Tunedal
>
>
> On Mon, Mar 11, 2013, at 16:40, Barry Haddow wrote:
>> Hi Per
>>
>> It's probably as easy to filter and binarise the input yourself before
>> tuning using the filter-model-given-input.pl script in Moses, and using
>> the -Binarizer argument to pass it the path to the processPhraseTable
>> binary.
>>
>> By default, mert-moses.pl will filter (so the command below will filter)
>> and this may reduce the size of the phrase table sufficiently for you.
>> You can add binarisation to mert-moses.pl by using the --filtercmd to
>> give it the filter script with its binariser argument,
>>
>> cheers - Barry
>>
>> On 11/03/13 15:27, Per Tunedal wrote:
>>> Hi,
>>> thanks for your kind advice. I plan to use the tuning command displayed
>>> at the Baseline page:
>>> nohup nice ~/mosesdecoder/scripts/training/mert-moses.pl
>>> ~/corpus/news-test2008.true.fr ~/corpus/news-test2008.true.en \
>>>     ~/mosesdecoder/bin/moses  train/model/moses.ini --mertdir
>>>     ~/mosesdecoder/bin/ &> mert.out &
>>> with my own "small" tuning-corpus (10% of the training-corpus size).
>>>
>>> Will this "give [the tuning script] a binariser" ? If not, what shall I
>>> add to the command?
>>>
>>> Yours,
>>> Per Tunedal
>>>
>>> On Mon, Mar 11, 2013, at 16:00, Barry Haddow wrote:
>>>> Hi Per
>>>>
>>>> The tuning script will filter the phrase table (leaving only the entries
>>>> required for the tuning set) and then binarise it (if you give it a
>>>> binariser) before running the actual tuning. So no, the whole table
>>>> doesn't need to be loaded into memory during tuning.
>>>>
>>>> You could prune before tuning, and I don't know how this will compare to
>>>> pruning after tuning. I'm not sure if anyone has tried it. The
>>>> signifcance filtering (in advanced features) works (afaik), although
>>>> there are a few steps involved in building the code. The relent
>>>> filtering has bit-rotted a bit, but you can run it with Moses v0.91.
>>>>
>>>> cheers - Barry
>>>>
>>>> On 11/03/13 14:37, Per Tunedal wrote:
>>>>> Hi Barry,
>>>>> Binarise, yes. But before that I plan to prune the translation table
>>>>> (Advanced features). Any hints?
>>>>>
>>>>> Back to my original question, can pruning be done before tuning? Is it
>>>>> possible to binarise too, before tuning? (The base-line page suggests
>>>>> binarisiation after tuning.)  I fear that the tuning might be an
>>>>> overwhelming task for my poor computer.
>>>>>
>>>>> Yours,
>>>>> Per Tunedal
>>>>>
>>>>> BTW What is actually done when tuning? Has all the tables to be loaded
>>>>> into memory?
>>>>>
>>>>> On Mon, Mar 11, 2013, at 10:42, Barry Haddow wrote:
>>>>>> Hi Per
>>>>>>
>>>>>> You need to binarise the models (phrase table, reordering table and
>>>>>> language model) before running Moses
>>>>>> http://www.statmt.org/moses/?n=Moses.AdvancedFeatures#ntoc3
>>>>>> If you don't binarise then Moses will load all the tables into memory,
>>>>>> so the memory requirement will be at least as large as the on-disk size,
>>>>>> in fact a lot more since it doesn't store them efficiently.
>>>>>>
>>>>>> The complexity of training is not easy to calculate since there are a
>>>>>> number of steps, but since one step involves sorting the list of
>>>>>> extracted phrases the complexity must be at least as bad as that.
>>>>>>
>>>>>> cheers - Barry
>>>>>>
>>>>>> On 11/03/13 08:24, Per Tunedal wrote:
>>>>>>> Hi Barry,
>>>>>>> it turns out that me too have succeeded to build my model in one day. I
>>>>>>> forced a restart and checked the log and the working directory. All is
>>>>>>> fine! The moses.ini file was created only 7 hours after submitting the
>>>>>>> command to build the model. I don't understand why the computer didn't
>>>>>>> respond, though.
>>>>>>>
>>>>>>> How does the time to build a model vary with the size of the corpus?
>>>>>>> Linearly? Or quadratic? Or what?
>>>>>>>
>>>>>>> I've now tried to do a test translation, without any tuning. I soon ran
>>>>>>> out of memory: even the virtual memory was exhausted after a while. Any
>>>>>>> way to predict the memory needed?
>>>>>>>
>>>>>>> Yours,
>>>>>>> Per Tunedal
>>>>>>>
>>>>>>>
>>>>>>> On Sun, Mar 10, 2013, at 11:39, Barry Haddow wrote:
>>>>>>>> Hi Per
>>>>>>>>
>>>>>>>> I would suggest starting from a smallish corpus, then building up to a
>>>>>>>> larger one, to get experience with the process. Using the news 
>>>>>>>> commentary
>>>>>>>> corpus described in the Moses baseline page, I was able to train and 
>>>>>>>> tune
>>>>>>>> in an evening on my laptop.
>>>>>>>>
>>>>>>>> There have been papers on predicting quality given corpus size, but
>>>>>>>> there's not an easy answer. Look for Marco Turchi at last year's EAMT, 
>>>>>>>> or
>>>>>>>> (I think) one by Xerox Grenoble from last year.
>>>>>>>>
>>>>>>>> As regards europarl, yes there's noise, but the models are quite robust
>>>>>>>> to it
>>>>>>>>
>>>>>>>> Cheers - Barry
>>>>>>>>
>>>>>>>> Per Tunedal <[email protected]> wrote:
>>>>>>>>
>>>>>>>>> Hi,
>>>>>>>>> Is there any way to predict the time for training and/or tuning, given
>>>>>>>>> the corpus size and the computer specifications? It would be nice to
>>>>>>>>> know  what would be a reasonable time for accomplishing the tasks. Now
>>>>>>>>> my computer has been running for 3 days and nights and doesn't respond
>>>>>>>>> any more: I cannot "wake it" to see what's going on. I don't know if
>>>>>>>>> it's normal or if something has gone havoc.
>>>>>>>>>
>>>>>>>>> I agree with Ken Fasano, that it would be very useful to know how big 
>>>>>>>>> a
>>>>>>>>> corpus is needed to get meaningful results. I would like to be able to
>>>>>>>>> judge the quality of the translation, to see if it would be useful to
>>>>>>>>> continue with Moses in some more serious manner.
>>>>>>>>>
>>>>>>>>> I'm a bit puzzled by the parameter limiting sentence length to, say 80
>>>>>>>>> (characters?), giving that e.g. the Europarl corpus contains mainly 
>>>>>>>>> VERY
>>>>>>>>> long sentences. Skipping long sentences probably implicates that many
>>>>>>>>> typical expressions are lost in the model. Wouldn't it be more 
>>>>>>>>> sensible
>>>>>>>>> to skip short sentences? Or to make a representative sample of the
>>>>>>>>> corpus, by doing a random sample of a sufficient size or something?
>>>>>>>>> Yours,
>>>>>>>>> Per Tunedal
>>>>>>>>>
>>>>>>>>> PS I've noticed that the Europarl corpus contains some very bad,
>>>>>>>>> completely incomprehensible, translations. That makes me question the
>>>>>>>>> quality of that corpus. How are the translations actually done? By
>>>>>>>>> humans relying heavily on machine translation? Sometimes letting some
>>>>>>>>> strange MT-translation pass?
>>>>>>>>>
>>>>>>>>> On Sat, Mar 9, 2013, at 18:43, Ken Fasano wrote:
>>>>>>>>>> I'd like to respond to this thread. I, too, have limited resources 
>>>>>>>>>> (at
>>>>>>>>>> work, at least) - a 3 GB RAM 32-bit Windows i5 machine with Linux 
>>>>>>>>>> running
>>>>>>>>>> on VMWare with 2.5 GB RAM. Training and tuning take many hours; the
>>>>>>>>>> machine is running BitParl over the weekend and may be done with
>>>>>>>>>> NewsCommentary de-en (DE) on Monday when I get back to work. I'm 
>>>>>>>>>> afraid
>>>>>>>>>> after all that I won't be able to subsequently run Collins on the
>>>>>>>>>> English, train, tune, and decode all that and, even if it takes 
>>>>>>>>>> forever,
>>>>>>>>>> expect it to run with the limited memory resources available.
>>>>>>>>>> What I think I need to do is trim the corpora according to some 
>>>>>>>>>> criteria
>>>>>>>>>> that isn't too complicated. Is it enough for learning purposes (we 
>>>>>>>>>> are a
>>>>>>>>>> long way from any sort of real comparison of results, let alone
>>>>>>>>>> production - this will receive proper hardware) to take the first n
>>>>>>>>>> sentences, or every nth sentence? The result is simply to get a feel 
>>>>>>>>>> for
>>>>>>>>>> the various modes of tree-based SMT, run hierarchical phrase,
>>>>>>>>>> string-to-tree, tree-to-string and tree-to-tree without worrying 
>>>>>>>>>> which
>>>>>>>>>> one is the best - the idea is just to get some experience with 
>>>>>>>>>> it.What
>>>>>>>>>> would be a good number of sentences to take so that it runs 
>>>>>>>>>> relatively
>>>>>>>>>> quickly, without killing RAM, but produces results that aren't 
>>>>>>>>>> useless?
>>>>>>>>>> Thanks - and I'd like to thank everyone on the group for their eager
>>>>>>>>>> helpfulness, and for discussing things that I as a newbie find very
>>>>>>>>>> useful!
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>> Date: Sat, 9 Mar 2013 11:25:45 -0500
>>>>>>>>>>> From: [email protected]
>>>>>>>>>>> To: [email protected]
>>>>>>>>>>> Subject: Re: [Moses-support] Accelerate the tuning
>>>>>>>>>>>
>>>>>>>>>>> Hi,
>>>>>>>>>>>
>>>>>>>>>>>     It won't fix everything, but there is a long-term TODO to 
>>>>>>>>>>> rewrite
>>>>>>>>>>> phrase table scoring to use binary files with vocabulary ids 
>>>>>>>>>>> instead of
>>>>>>>>>>> text files.
>>>>>>>>>>>
>>>>>>>>>>> Kenneth
>>>>>>>>>>>
>>>>>>>>>>> On 03/09/13 08:30, Per Tunedal wrote:
>>>>>>>>>>>> Hi,
>>>>>>>>>>>> the training seems to be an overwhelming task for my computer. If 
>>>>>>>>>>>> it
>>>>>>>>>>>> ever succeeds, I will have to undertake the even more demanding 
>>>>>>>>>>>> task of
>>>>>>>>>>>> tuning. Can anything be done to accelerate it?
>>>>>>>>>>>>
>>>>>>>>>>>> Specifically, I wonder if it's feasible to prune the translation 
>>>>>>>>>>>> table
>>>>>>>>>>>> before doing the tuning.
>>>>>>>>>>>>
>>>>>>>>>>>> Yours,
>>>>>>>>>>>> Per Tunedal
>>>>>>>>>>>>
>>>>>>>>>>>> PS I've abandoned the idea of building a Hierarchical phrase 
>>>>>>>>>>>> model, I'm
>>>>>>>>>>>> now trying to make a phrase-based system. I suppose that would use 
>>>>>>>>>>>> less
>>>>>>>>>>>> resources.
>>>>>>>>>>>>
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>>>>>>>>>>>>
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>>>>>>>>>> _______________________________________________
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>>>>
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>>


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