Examples with Args zemberek.core.embeddings.Args used on opensource projects

Example 6 with Args

use of zemberek.core.embeddings.Args in project zemberek-nlp by ahmetaa.

the class FastTextTest method classificationTest.

/**
 * Runs the cooking label guessing task. run with -Xms8G or more.
 */
@Test
@Ignore("Not an actual Test.")
public void classificationTest() throws Exception {
    Path inputRoot = Paths.get("/home/ahmetaa/data/fasttext");
    Path trainFile = inputRoot.resolve("cooking.train");
    // Path trainFile = inputRoot.resolve("train.10k");
    Path modelPath = inputRoot.resolve("cooking.model.bin");
    Path quantizedModelPath = inputRoot.resolve("cooking.model.qbin");
    Path testFile = inputRoot.resolve("cooking.valid");
    Args argz = Args.forSupervised();
    argz.thread = 4;
    argz.epoch = 25;
    argz.wordNgrams = 2;
    argz.minCount = 1;
    argz.lr = 1.0;
    argz.dim = 100;
    argz.bucket = 1000_000;
    FastText fastText;
    /*    if(modelPath.toFile().exists())
      fastText = FastText.load(modelPath);
    else*/
    fastText = new FastTextTrainer(argz).train(trainFile);
    fastText.saveModel(modelPath);
    Log.info("Testing started.");
    test(fastText, testFile, 1);
    fastText = FastText.load(modelPath);
    test(fastText, testFile, 1);
    argz.qnorm = false;
    argz.cutoff = 3000;
    fastText = fastText.quantize(modelPath, argz);
    fastText.saveModel(quantizedModelPath);
    Log.info("Testing quantization result.");
    test(fastText, testFile, 1);
    fastText = FastText.load(quantizedModelPath);
    Log.info("Testing after loading quantized model.");
    test(fastText, testFile, 1);
}

Example 7 with Args

use of zemberek.core.embeddings.Args in project zemberek-nlp by ahmetaa.

the class FastTextTest method skipgram.

/**
 * Generates word vectors using skip-gram model. run with -Xms8G or more.
 */
@Test
@Ignore("Not an actual Test.")
public void skipgram() throws Exception {
    Args argz = Args.forWordVectors(Args.model_name.skipGram);
    argz.thread = 4;
    argz.epoch = 10;
    argz.dim = 100;
    argz.bucket = 2_000_000;
    argz.minn = 3;
    argz.maxn = 6;
    argz.subWordHashProvider = new EmbeddingHashProviders.CharacterNgramHashProvider(argz.minn, argz.maxn);
    Path input = Paths.get("/home/ahmetaa/data/nlp/corpora/sentences.50k");
    Path outRoot = Paths.get("/home/ahmetaa/data/fasttext");
    FastText fastText = new FastTextTrainer(argz).train(input);
    Path vectorFile = outRoot.resolve("sentences-50k-skipgram.vec");
    Log.info("Saving vectors to %s", vectorFile);
    fastText.saveVectors(vectorFile);
}

Example 8 with Args

use of zemberek.core.embeddings.Args in project zemberek-nlp by ahmetaa.

the class FastTextTest method dbpediaClassificationTest.

/**
 * Runs the dbpedia classification task. run with -Xms8G or more.
 */
@Test
@Ignore("Not an actual Test.")
public void dbpediaClassificationTest() throws Exception {
    Path inputRoot = Paths.get("/media/aaa/3t/aaa/fasttext");
    Path trainFile = inputRoot.resolve("dbpedia.train");
    Path modelPath = Paths.get("/media/aaa/3t/aaa/fasttext/dbpedia.model.bin");
    FastText fastText;
    if (modelPath.toFile().exists()) {
        fastText = FastText.load(modelPath);
    } else {
        Args argz = Args.forSupervised();
        argz.thread = 4;
        argz.epoch = 5;
        argz.wordNgrams = 2;
        argz.minCount = 1;
        argz.lr = 0.1;
        argz.dim = 32;
        argz.bucket = 5_000_000;
        fastText = new FastTextTrainer(argz).train(trainFile);
        fastText.saveModel(modelPath);
    }
    Path testFile = inputRoot.resolve("dbpedia.test");
    Log.info("Testing started.");
    EvaluationResult result = fastText.test(testFile, 1);
    Log.info(result.toString());
}