Examples with ClassModel zemberek.ner.PerceptronNer.ClassModel used on opensource projects

Example 1 with ClassModel

use of zemberek.ner.PerceptronNer.ClassModel in project zemberek-nlp by ahmetaa.

the class PerceptronNerTrainer method train.

public PerceptronNer train(NerDataSet trainingSet, NerDataSet devSet, int iterationCount, float learningRate) {
    Map<String, ClassModel> averages = new HashMap<>();
    Map<String, ClassModel> model = new HashMap<>();
    IntValueMap<String> counts = new IntValueMap<>();
    // initialize model weights for all classes.
    for (String typeId : trainingSet.typeIds) {
        model.put(typeId, new ClassModel(typeId));
        averages.put(typeId, new ClassModel(typeId));
    }
    for (int it = 0; it < iterationCount; it++) {
        int errorCount = 0;
        int tokenCount = 0;
        trainingSet.shuffle();
        for (NerSentence sentence : trainingSet.sentences) {
            for (int i = 0; i < sentence.tokens.size(); i++) {
                tokenCount++;
                NerToken currentToken = sentence.tokens.get(i);
                String currentId = currentToken.tokenId;
                FeatureData data = new FeatureData(morphology, sentence, i);
                List<String> sparseFeatures = data.getTextualFeatures();
                if (i > 0) {
                    sparseFeatures.add("PreType=" + sentence.tokens.get(i - 1).tokenId);
                }
                if (i > 1) {
                    sparseFeatures.add("2PreType=" + sentence.tokens.get(i - 2).tokenId);
                }
                if (i > 2) {
                    sparseFeatures.add("3PreType=" + sentence.tokens.get(i - 3).tokenId);
                }
                ScoredItem<String> predicted = PerceptronNer.predictTypeAndPosition(model, sparseFeatures);
                String predictedId = predicted.item;
                if (predictedId.equals(currentId)) {
                    // do nothing
                    counts.addOrIncrement(predictedId);
                    continue;
                }
                counts.addOrIncrement(currentId);
                counts.addOrIncrement(predictedId);
                errorCount++;
                model.get(currentId).updateSparse(sparseFeatures, +learningRate);
                model.get(predictedId).updateSparse(sparseFeatures, -learningRate);
                averages.get(currentId).updateSparse(sparseFeatures, counts.get(currentId) * learningRate);
                averages.get(predictedId).updateSparse(sparseFeatures, -counts.get(predictedId) * learningRate);
            }
        }
        Log.info("Iteration %d, Token error = %.6f", it + 1, (errorCount * 1d) / tokenCount);
        Map<String, ClassModel> copyModel = copyModel(model);
        averageWeights(averages, copyModel, counts);
        PerceptronNer ner = new PerceptronNer(copyModel, morphology);
        if (devSet != null) {
            NerDataSet result = ner.evaluate(devSet);
            Log.info(collectEvaluationData(devSet, result).dump());
        }
    }
    averageWeights(averages, model, counts);
    Log.info("Training finished.");
    return new PerceptronNer(model, morphology);
}