Hi Jörn, Appreciate your interest. No such errors are thrown when I make calls to the 'evaluation' routines (so the data format is correct, but I *have* run into that one before), and the folds should contain plenty of names.
Thanks again On Thu, May 15, 2014 at 1:20 AM, Jörn Kottmann <[email protected]> wrote: > The exception indicates that there is not enough training data. > > Maybe the data does not contain enough names? > > Jörn > > > On 05/14/2014 07:10 PM, Walrus theCat wrote: > >> This is using the Java API. That is the entire stack trace. >> >> Thanks >> >> >> On Tue, May 13, 2014 at 2:14 AM, Jörn Kottmann <[email protected]> >> wrote: >> >> Hello, >>> >>> can you post the entire output you get from OpenNLP? >>> Are you using the command line interface? >>> >>> Jörn >>> >>> >>> On 05/13/2014 02:25 AM, Walrus theCat wrote: >>> >>> This is with plenty of data, by the way. >>>> >>>> >>>> On Thu, May 8, 2014 at 2:30 PM, Walrus theCat <[email protected] >>>> >>>>> wrote: >>>>> >>>> Hi, >>>> >>>>> The CrossValidator is hitting a NPE in a pretty standard use case. I >>>>> traced it through, and, when cross-validating, DataIndexer.getContexts >>>>> is >>>>> returning null (and thus, the call to contexts.length is throwing the >>>>> exception.) What can I do here? >>>>> >>>>> Thanks, >>>>> >>>>> Walrus theCat >>>>> >>>>> Exception in thread "main" java.lang.NullPointerException >>>>> at opennlp.tools.ml.maxent.GISTrainer.trainModel( >>>>> GISTrainer.java:264) >>>>> at opennlp.tools.ml.maxent.GIS.trainModel(GIS.java:298) >>>>> at opennlp.tools.ml.maxent.GIS.doTrain(GIS.java:83) >>>>> at opennlp.tools.ml.maxent.GIS.doTrain(GIS.java:36) >>>>> at >>>>> opennlp.tools.ml.AbstractEventTrainer.train( >>>>> AbstractEventTrainer.java:93) >>>>> at opennlp.tools.namefind.NameFinderME.train( >>>>> NameFinderME.java:410) >>>>> at opennlp.tools.namefind.NameFinderME.train( >>>>> NameFinderME.java:472) >>>>> at >>>>> opennlp.tools.namefind.TokenNameFinderCrossValidator.evaluate( >>>>> TokenNameFinderCrossValidator.java:225) >>>>> at precyse.ml.ner.TrainNERModels$.getResults(TrainNERModels. >>>>> scala:153) >>>>> at >>>>> precyse.ml.ner.TrainNERModels$$anonfun$main$3.apply( >>>>> TrainNERModels.scala:58) >>>>> at >>>>> precyse.ml.ner.TrainNERModels$$anonfun$main$3.apply( >>>>> TrainNERModels.scala:53) >>>>> at >>>>> scala.collection.mutable.ResizableArray$class.foreach( >>>>> ResizableArray.scala:60) >>>>> at scala.collection.mutable.ArrayBuffer.foreach( >>>>> ArrayBuffer.scala:47) >>>>> at initech.ml.ner.TrainNERModels$.main(TrainNERModels.scala:53) >>>>> at initech.ml.ner.TrainNERModels.main(TrainNERModels.scala) >>>>> >>>>> >>>>> >
