stanfordnlp
How big is your truecase model?
Hey there!
I've trained a truecase model for the german language on a dataset of 1 million sentences. The resulting model is quite big (80MB) and I am having memory issues including it into my annotating pipeline. But when I use the english truecase model there are no issues.
How big is your truecase model (edu/stanford/nlp/models/truecase/truecasing.fast.caseless.qn.ser.gz)? And how does the annotator impact the memory consumption of the pipeline?
Ok I unpacked your jar and found out: truecasing.fast.caseless.qn.ser.gz - 15.8 MB
But how is this possible? You are training on 4.5 million sentences and I am training on only 1 million. I have the exact same configuration except for:
useQN=false (I have true)
l1reg=1.0 (I don't have this line)
because I read somewhere that I can only use QN Minimizer and it is throwing an error when I use that configuration.
Is it because maybe the german language has more words in general (don't know if that's true)?
This is my whole training configration
serializeTo=truecasing.fast.caseless.qn.ser.gz
trainFileList=data.train
testFile=data.test
map=word=0,answer=1
wordFunction = edu.stanford.nlp.process.LowercaseFunction
useClassFeature=true
useWord=true
useNGrams=true
noMidNGrams=true
maxNGramLeng=6
usePrev=true
useNext=true
useLongSequences=true
useSequences=true
usePrevSequences=true
useTypeSeqs=true
useTypeSeqs2=true
useTypeySequences=true
useOccurrencePatterns=true
useLastRealWord=true
useNextRealWord=true
useDisjunctive=true
disjunctionWidth=5
wordShape=chris2useLC
usePosition=true
useBeginSent=true
useTitle=true
useObservedSequencesOnly=true
saveFeatureIndexToDisk=true
normalize=true
useQN=true
QNSize=25
maxLeft=1
readerAndWriter=edu.stanford.nlp.sequences.TrueCasingForNISTDocumentReaderAndWriter
featureFactory=edu.stanford.nlp.ie.NERFeatureFactory
featureDiffThresh=0.02
What happens if you add back the l1reg? That should force weights to 0, which should reduce the size of the final model.
Also, I retrained the model recently on 1.5M sentences and the resulting model is significantly bigger, at 48M.
On Mon, Jan 20, 2020 at 6:46 AM erksch [email protected] wrote:
This is my whole training configration:
serializeTo=truecasing.fast.caseless.qn.ser.gz trainFileList=data.train testFile=data.test
map=word=0,answer=1
wordFunction = edu.stanford.nlp.process.LowercaseFunction
useClassFeature=true useWord=true useNGrams=true noMidNGrams=true maxNGramLeng=6 usePrev=true useNext=true useLongSequences=true useSequences=true usePrevSequences=true useTypeSeqs=true useTypeSeqs2=true useTypeySequences=true useOccurrencePatterns=true useLastRealWord=true useNextRealWord=true useDisjunctive=true disjunctionWidth=5 wordShape=chris2useLC usePosition=true useBeginSent=true useTitle=true
useObservedSequencesOnly=true saveFeatureIndexToDisk=true normalize=true
useQN=true QNSize=25
maxLeft=1
readerAndWriter=edu.stanford.nlp.sequences.TrueCasingForNISTDocumentReaderAndWriter featureFactory=edu.stanford.nlp.ie.NERFeatureFactory
featureDiffThresh=0.02
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When I add l1reg I get the following error:
Exception in thread "main" edu.stanford.nlp.util.ReflectionLoading$ReflectionLoadingException: Error creating edu.stanford.nlp.optimization.OWLQNMinimizer
at edu.stanford.nlp.util.ReflectionLoading.loadByReflection(ReflectionLoading.java:38)
at edu.stanford.nlp.ie.crf.CRFClassifier.getMinimizer(CRFClassifier.java:2003)
at edu.stanford.nlp.ie.crf.CRFClassifier.trainWeights(CRFClassifier.java:1902)
at edu.stanford.nlp.ie.crf.CRFClassifier.train(CRFClassifier.java:1742)
at edu.stanford.nlp.ie.AbstractSequenceClassifier.train(AbstractSequenceClassifier.java:785)
at edu.stanford.nlp.ie.AbstractSequenceClassifier.train(AbstractSequenceClassifier.java:756)
at edu.stanford.nlp.ie.crf.CRFClassifier.main(CRFClassifier.java:3011)
Caused by: edu.stanford.nlp.util.MetaClass$ClassCreationException: java.lang.ClassNotFoundException: edu.stanford.nlp.optimization.OWLQNMinimizer
at edu.stanford.nlp.util.MetaClass.createFactory(MetaClass.java:364)
at edu.stanford.nlp.util.MetaClass.createInstance(MetaClass.java:381)
at edu.stanford.nlp.util.ReflectionLoading.loadByReflection(ReflectionLoading.java:36)
... 6 more
Caused by: java.lang.ClassNotFoundException: edu.stanford.nlp.optimization.OWLQNMinimizer
at java.base/jdk.internal.loader.BuiltinClassLoader.loadClass(BuiltinClassLoader.java:582)
at java.base/jdk.internal.loader.ClassLoaders$AppClassLoader.loadClass(ClassLoaders.java:178)
at java.base/java.lang.ClassLoader.loadClass(ClassLoader.java:521)
at java.base/java.lang.Class.forName0(Native Method)
at java.base/java.lang.Class.forName(Class.java:315)
at edu.stanford.nlp.util.MetaClass$ClassFactory.construct(MetaClass.java:135)
at edu.stanford.nlp.util.MetaClass$ClassFactory.<init>(MetaClass.java:202)
at edu.stanford.nlp.util.MetaClass$ClassFactory.<init>(MetaClass.java:69)
at edu.stanford.nl
According to the things I read this tries to use the OWLQNMinimizer which is not publicly available in CoreNLP thus the class is not found.
Right, we are not licensed to release that optimizer. [...] add the flag useQN=true
It turns out you also need to turn of l1reg (remove the l1reg=...flag) to use the qn implementation. For all I know, turning off the regularization may make the classifier much worse, unfortunately.
From this thread
Ah, that's a good point. I'll check with our PI to see if things have changed in terms of what we can publicly release.
On Mon, Jan 20, 2020 at 9:42 AM erksch [email protected] wrote:
When I add l1reg I get the following error:
Exception in thread "main" edu.stanford.nlp.util.ReflectionLoading$ReflectionLoadingException: Error creating edu.stanford.nlp.optimization.OWLQNMinimizer at edu.stanford.nlp.util.ReflectionLoading.loadByReflection(ReflectionLoading.java:38) at edu.stanford.nlp.ie.crf.CRFClassifier.getMinimizer(CRFClassifier.java:2003) at edu.stanford.nlp.ie.crf.CRFClassifier.trainWeights(CRFClassifier.java:1902) at edu.stanford.nlp.ie.crf.CRFClassifier.train(CRFClassifier.java:1742) at edu.stanford.nlp.ie.AbstractSequenceClassifier.train(AbstractSequenceClassifier.java:785) at edu.stanford.nlp.ie.AbstractSequenceClassifier.train(AbstractSequenceClassifier.java:756) at edu.stanford.nlp.ie.crf.CRFClassifier.main(CRFClassifier.java:3011) Caused by: edu.stanford.nlp.util.MetaClass$ClassCreationException: java.lang.ClassNotFoundException: edu.stanford.nlp.optimization.OWLQNMinimizer at edu.stanford.nlp.util.MetaClass.createFactory(MetaClass.java:364) at edu.stanford.nlp.util.MetaClass.createInstance(MetaClass.java:381) at edu.stanford.nlp.util.ReflectionLoading.loadByReflection(ReflectionLoading.java:36) ... 6 more Caused by: java.lang.ClassNotFoundException: edu.stanford.nlp.optimization.OWLQNMinimizer at java.base/jdk.internal.loader.BuiltinClassLoader.loadClass(BuiltinClassLoader.java:582) at java.base/jdk.internal.loader.ClassLoaders$AppClassLoader.loadClass(ClassLoaders.java:178) at java.base/java.lang.ClassLoader.loadClass(ClassLoader.java:521) at java.base/java.lang.Class.forName0(Native Method) at java.base/java.lang.Class.forName(Class.java:315) at edu.stanford.nlp.util.MetaClass$ClassFactory.construct(MetaClass.java:135) at edu.stanford.nlp.util.MetaClass$ClassFactory.
(MetaClass.java:202) at edu.stanford.nlp.util.MetaClass$ClassFactory. (MetaClass.java:69) at edu.stanford.nl According to the things I read this tries to use the OWLQNMinimizer which is not publicly available in CoreNLP thus the class is not found.
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Nice, thank you very much! Have you trained with the normal QNMinimizer in your recent training?
Sorry (again) for the late reply. What should work is the parameters
useQN=true useOWLQN=true priorLambda= (some hyperparameter)
Thank you! I think I'll retrain our truecaser in the next days and give the parameters a try!