public override void run(string format, string[] args)
{
base.run(format, args);
mlParams = CmdLineUtil.loadTrainingParameters(@params.Params, false);
if (mlParams != null)
{
if (TrainUtil.isSequenceTraining(mlParams.Settings))
{
throw new TerminateToolException(1, "Sequence training is not supported!");
}
}
if (mlParams == null)
{
mlParams = ModelUtil.createTrainingParameters(@params.Iterations.Value, @params.Cutoff.Value);
}
Jfile modelOutFile = @params.Model;
CmdLineUtil.checkOutputFile("sentence detector model", modelOutFile);
char[] eos = null;
if (@params.EosChars != null)
{
eos = @params.EosChars.ToCharArray();
}
SentenceModel model;
try
{
Dictionary dict = loadDict(@params.AbbDict);
SentenceDetectorFactory sdFactory = SentenceDetectorFactory.create(@params.Factory, @params.Lang, true, dict, eos);
model = SentenceDetectorME.train(@params.Lang, sampleStream, sdFactory, mlParams);
}
catch (IOException e)
{
throw new TerminateToolException(-1, "IO error while reading training data or indexing data: " + e.Message, e);
}
finally
{
try
{
sampleStream.close();
}
catch (IOException)
{
// sorry that this can fail
}
}
CmdLineUtil.writeModel("sentence detector", modelOutFile, model);
}
public override void run(string format, string[] args)
{
base.run(format, args);
mlParams = CmdLineUtil.loadTrainingParameters(@params.Params, false);
if (mlParams != null)
{
if (!TrainUtil.isValid(mlParams.Settings))
{
throw new TerminateToolException(1, "Training parameters file '" + @params.Params + "' is invalid!");
}
if (TrainUtil.isSequenceTraining(mlParams.Settings))
{
throw new TerminateToolException(1, "Sequence training is not supported!");
}
}
if (mlParams == null)
{
mlParams = ModelUtil.createTrainingParameters(@params.Iterations.Value, @params.Cutoff.Value);
}
File modelOutFile = @params.Model;
CmdLineUtil.checkOutputFile("tokenizer model", modelOutFile);
TokenizerModel model;
try
{
Dictionary dict = loadDict(@params.AbbDict);
TokenizerFactory tokFactory = TokenizerFactory.create(@params.Factory, @params.Lang, dict, @params.AlphaNumOpt.Value, null);
model = opennlp.tools.tokenize.TokenizerME.train(sampleStream, tokFactory, mlParams);
}
catch (IOException e)
{
throw new TerminateToolException(-1, "IO error while reading training data or indexing data: " + e.Message, e);
}
finally
{
try
{
sampleStream.close();
}
catch (IOException)
{
// sorry that this can fail
}
}
CmdLineUtil.writeModel("tokenizer", modelOutFile, model);
}
public override void run(string format, string[] args)
{
base.run(format, args);
mlParams = CmdLineUtil.loadTrainingParameters(@params.Params, false);
if (mlParams == null)
{
mlParams = ModelUtil.createTrainingParameters(@params.Iterations.Value, @params.Cutoff.Value);
}
File modelOutFile = @params.Model;
sbyte[] featureGeneratorBytes = openFeatureGeneratorBytes(@params.Featuregen);
// TODO: Support Custom resources:
// Must be loaded into memory, or written to tmp file until descriptor
// is loaded which defines parses when model is loaded
IDictionary <string, object> resources = loadResources(@params.Resources);
CmdLineUtil.checkOutputFile("name finder model", modelOutFile);
if (@params.NameTypes != null)
{
string[] nameTypes = @params.NameTypes.Split(",", true);
sampleStream = new NameSampleTypeFilter(nameTypes, sampleStream);
}
TokenNameFinderModel model;
try
{
model = opennlp.tools.namefind.NameFinderME.train(@params.Lang, @params.Type, sampleStream, mlParams, featureGeneratorBytes, resources);
}
catch (IOException e)
{
throw new TerminateToolException(-1, "IO error while reading training data or indexing data: " + e.Message, e);
}
finally
{
try
{
sampleStream.close();
}
catch (IOException)
{
// sorry that this can fail
}
}
CmdLineUtil.writeModel("name finder", modelOutFile, model);
}
public override void run(string[] args)
{
if (args.Length != 2)
{
Console.WriteLine(Help);
}
else
{
File parserModelInFile = new File(args[0]);
ParserModel parserModel = (new ParserModelLoader()).load(parserModelInFile);
File taggerModelInFile = new File(args[1]);
POSModel taggerModel = (new POSModelLoader()).load(taggerModelInFile);
ParserModel updatedParserModel = parserModel.updateTaggerModel(taggerModel);
CmdLineUtil.writeModel("parser", parserModelInFile, updatedParserModel);
}
}
public override void run(string format, string[] args)
{
base.run(format, args);
mlParams = CmdLineUtil.loadTrainingParameters(@params.Params, false);
if (mlParams == null)
{
mlParams = ModelUtil.createTrainingParameters(@params.Iterations.Value, @params.Cutoff.Value);
}
File modelOutFile = @params.Model;
CmdLineUtil.checkOutputFile("sentence detector model", modelOutFile);
ChunkerModel model;
try
{
ChunkerFactory chunkerFactory = ChunkerFactory.create(@params.Factory);
model = ChunkerME.train(@params.Lang, sampleStream, mlParams, chunkerFactory);
}
catch (IOException e)
{
throw new TerminateToolException(-1, "IO error while reading training data or indexing data: " + e.Message, e);
}
finally
{
try
{
sampleStream.close();
}
catch (IOException)
{
// sorry that this can fail
}
}
CmdLineUtil.writeModel("chunker", modelOutFile, model);
}
public sealed override void run(string format, string[] args)
{
ModelUpdaterParams @params = validateAndParseParams <ModelUpdaterParams>(ArgumentParser.filter(args, typeof(ModelUpdaterParams)), typeof(ModelUpdaterParams));
// Load model to be updated
Jfile modelFile = @params.Model;
ParserModel originalParserModel = (new ParserModelLoader()).load(modelFile);
ObjectStreamFactory factory = getStreamFactory(format);
string[] fargs = ArgumentParser.filter(args, factory.Parameters);
validateFactoryArgs(factory, fargs);
ObjectStream <Parse> sampleStream = factory.create <Parse>(fargs);
ParserModel updatedParserModel;
try
{
updatedParserModel = trainAndUpdate(originalParserModel, sampleStream, @params);
}
catch (IOException e)
{
throw new TerminateToolException(-1, "IO error while reading training data or indexing data: " + e.Message, e);
}
finally
{
try
{
sampleStream.close();
}
catch (IOException)
{
// sorry that this can fail
}
}
CmdLineUtil.writeModel("parser", modelFile, updatedParserModel);
}
// TODO: Add param to train tree insert parser
public override void run(string format, string[] args)
{
base.run(format, args);
mlParams = CmdLineUtil.loadTrainingParameters(@params.Params, true);
if (mlParams != null)
{
if (!TrainUtil.isValid(mlParams.getSettings("build")))
{
throw new TerminateToolException(1, "Build training parameters are invalid!");
}
if (!TrainUtil.isValid(mlParams.getSettings("check")))
{
throw new TerminateToolException(1, "Check training parameters are invalid!");
}
if (!TrainUtil.isValid(mlParams.getSettings("attach")))
{
throw new TerminateToolException(1, "Attach training parameters are invalid!");
}
if (!TrainUtil.isValid(mlParams.getSettings("tagger")))
{
throw new TerminateToolException(1, "Tagger training parameters are invalid!");
}
if (!TrainUtil.isValid(mlParams.getSettings("chunker")))
{
throw new TerminateToolException(1, "Chunker training parameters are invalid!");
}
}
if (mlParams == null)
{
mlParams = ModelUtil.createTrainingParameters(@params.Iterations.Value, @params.Cutoff.Value);
}
Jfile modelOutFile = @params.Model;
CmdLineUtil.checkOutputFile("parser model", modelOutFile);
ParserModel model;
try
{
// TODO hard-coded language reference
HeadRules rules = new opennlp.tools.parser.lang.en.HeadRules(new InputStreamReader(new FileInputStream(@params.HeadRules), @params.Encoding));
var type = parseParserType(@params.ParserType);
if (@params.Fun.Value)
{
Parse.useFunctionTags(true);
}
if (ParserType.CHUNKING == type)
{
model = Parser.train(@params.Lang, sampleStream, rules, mlParams);
}
else if (ParserType.TREEINSERT == type)
{
model = opennlp.tools.parser.treeinsert.Parser.train(@params.Lang, sampleStream, rules, mlParams);
}
else
{
throw new IllegalStateException();
}
}
catch (IOException e)
{
throw new TerminateToolException(-1, "IO error while reading training data or indexing data: " + e.Message, e);
}
finally
{
try
{
sampleStream.close();
}
catch (IOException)
{
// sorry that this can fail
}
}
CmdLineUtil.writeModel("parser", modelOutFile, model);
}
public override void run(string format, string[] args)
{
base.run(format, args);
mlParams = CmdLineUtil.loadTrainingParameters(@params.Params, true);
if (mlParams != null && !TrainUtil.isValid(mlParams.Settings))
{
throw new TerminateToolException(1, "Training parameters file '" + @params.Params + "' is invalid!");
}
if (mlParams == null)
{
mlParams = ModelUtil.createTrainingParameters(@params.Iterations.Value, @params.Cutoff.Value);
mlParams.put(TrainingParameters.ALGORITHM_PARAM, getModelType(@params.Type).ToString());
}
File modelOutFile = @params.Model;
CmdLineUtil.checkOutputFile("pos tagger model", modelOutFile);
Dictionary ngramDict = null;
int?ngramCutoff = @params.Ngram;
if (ngramCutoff != null)
{
Console.Error.Write("Building ngram dictionary ... ");
try
{
ngramDict = POSTaggerME.buildNGramDictionary(sampleStream, ngramCutoff.Value);
sampleStream.reset();
}
catch (IOException e)
{
throw new TerminateToolException(-1, "IO error while building NGram Dictionary: " + e.Message, e);
}
Console.Error.WriteLine("done");
}
POSTaggerFactory postaggerFactory = null;
try
{
postaggerFactory = POSTaggerFactory.create(@params.Factory, ngramDict, null);
}
catch (InvalidFormatException e)
{
throw new TerminateToolException(-1, e.Message, e);
}
if (@params.Dict != null)
{
try
{
postaggerFactory.TagDictionary = postaggerFactory.createTagDictionary(@params.Dict);
}
catch (IOException e)
{
throw new TerminateToolException(-1, "IO error while loading POS Dictionary: " + e.Message, e);
}
}
if (@params.TagDictCutoff != null)
{
try
{
TagDictionary dict = postaggerFactory.TagDictionary;
if (dict == null)
{
dict = postaggerFactory.createEmptyTagDictionary();
postaggerFactory.TagDictionary = dict;
}
if (dict is MutableTagDictionary)
{
POSTaggerME.populatePOSDictionary(sampleStream, (MutableTagDictionary)dict, @params.TagDictCutoff.Value);
}
else
{
throw new System.ArgumentException("Can't extend a POSDictionary that does not implement MutableTagDictionary.");
}
sampleStream.reset();
}
catch (IOException e)
{
throw new TerminateToolException(-1, "IO error while creating/extending POS Dictionary: " + e.Message, e);
}
}
POSModel model;
try
{
model = POSTaggerME.train(@params.Lang, sampleStream, mlParams, postaggerFactory);
}
catch (IOException e)
{
throw new TerminateToolException(-1, "IO error while reading training data or indexing data: " + e.Message, e);
}
finally
{
try
{
sampleStream.close();
}
catch (IOException)
{
// sorry that this can fail
}
}
CmdLineUtil.writeModel("pos tagger", modelOutFile, model);
}
