1. en-ner-location.bin
   
2. en-ner-money.bin
   
3. en-ner-organization.bin
   
4. en-ner-percentage.bin
   
5. en-ner-person.bin
   
6. en-ner-date.bin
   

1. To train the model we have to provide the data in Open NLP name finder training format, which is one sentence per line.
   
2. We can use other format also to train the model, the sentence must be tokenized and contain spans which mark the entities.
   
3. Documents within train file are separated by empty lines which trigger the reset of the adaptive feature generators.
   

• Read data from train file (Input Stream)
  
• Train the data using NameFinderME.train method (Training process)
  
• Store the model (TokenNameFinderModel) to a file (Output Stream)
  

public String train(String lang, String entity, InputStream taggedCorpusStream, OutputStream modelStream) 
 { 
 Charset charset = Charset.forName(CHAR_ENCODING); 
 try { 
 ObjectStream<String> lineStream = new PlainTextByLineStream( taggedCorpusStream, charset); 
 ObjectStream<NameSample> sampleStream = new NameSampleDataStream( lineStream); 
 TokenNameFinderModel model; OutputStream modelOut = null; 
 try { 
 model = NameFinderME.train(lang, entity, sampleStream, null); 
 modelOut = new BufferedOutputStream(modelStream); 
 if (model != null) { 
 model.serialize(modelOut); 
 } 
 return entity + " model trained successfully"; 
 } 
 catch (Exception ex) { 
 ex.printStackTrace(); 
 } 
 finally { 
 sampleStream.close(); 
 if (modelOut != null) { 
 modelOut.close(); 
 } 
 } 
 } 
 catch (Exception e) { 
 e.printStackTrace(); 
 } 
 return "Something goes wrong with training module."; 
} 
 
 public String train(String lang, String entity, String taggedCoprusFile, String modelFile)
 { 
 try { 
 return train(lang, entity, new FileInputStream(taggedCoprusFile), new FileOutputStream(modelFile));
 } 
 catch (Exception e) { 
 e.printStackTrace(); 
 } 
 return "Something goes wrong with training module."; 
 }