/** * Frees any resources this parser may be holding. */ public void close() { parser.close(); } }
/** * Frees any resources this parser may be holding. */ public void close() { parser.close(); } }
/** Frees any resources this parser may be holding. */ public void close() { parser.close(); }
/** * Frees any resources this parser may be holding. */ public void close() { parser.close(); } }
/** * Frees any resources this parser may be holding. */ public void close() { parser.close(); } }
/** * Frees any resources this parser may be holding. */ public void close() { parser.close(); } }
/** * Frees any resources this parser may be holding. */ public void close() { parser.close(); } }
/** * Frees any resources this parser may be holding. */ public void close() { parser.close(); } }
/** Frees any resources this parser may be holding. */ public void close() { parser.close(); } }
/** * Frees any resources this parser may be holding. */ public void close() { parser.close(); } }
public void test() { ConstrainedPrepSRLClassifier classifier = new ConstrainedPrepSRLClassifier(); Parser testDataReader = new PrepSRLDataReader(dataDir, "test"); TestDiscrete tester = new TestDiscrete(); TestDiscrete.testDiscrete(tester, classifier, new PrepSRLClassifier.Label(), testDataReader, true, 100); testDataReader.close(); }
public void test() { ConstrainedPrepSRLClassifier classifier = new ConstrainedPrepSRLClassifier(); Parser testDataReader = new PrepSRLDataReader(dataDir, "test"); TestDiscrete tester = new TestDiscrete(); TestDiscrete.testDiscrete(tester, classifier, new PrepSRLClassifier.Label(), testDataReader, true, 100); testDataReader.close(); }
+ new Date()); parser.close(); eos.close();
+ " examples at " + new Date()); parser.close(); eos.close();
public void train() { if (!IOUtils.exists(modelsDir)) IOUtils.mkdir(modelsDir); Learner classifier = new PrepSRLClassifier(modelName + ".lc", modelName + ".lex"); Parser trainDataReader = new PrepSRLDataReader(dataDir, "train"); BatchTrainer trainer = new BatchTrainer(classifier, trainDataReader, 1000); trainer.train(20); classifier.save(); trainDataReader.close(); }
public void train() { if (!IOUtils.exists(modelsDir)) IOUtils.mkdir(modelsDir); Learner classifier = new PrepSRLClassifier(modelName + ".lc", modelName + ".lex"); Parser trainDataReader = new PrepSRLDataReader(dataDir, "train"); BatchTrainer trainer = new BatchTrainer(classifier, trainDataReader, 1000); trainer.train(20); classifier.save(); trainDataReader.close(); }