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namespace ZeroLevel.Services.Semantic.Fasttext
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{
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public class FTArgs
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{
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#region Args
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public double lr;
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public int lrUpdateRate;
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public int dim;
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public int ws;
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public int epoch;
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public int minCount;
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public int minCountLabel;
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public int neg;
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public int wordNgrams;
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public loss_name loss;
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public model_name model;
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public int bucket;
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public int minn;
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public int maxn;
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public int thread;
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public double t;
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public string label;
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public int verbose;
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public string pretrainedVectors;
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public bool saveOutput;
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public bool qout;
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public bool retrain;
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public bool qnorm;
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public ulong cutoff;
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public ulong dsub;
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#endregion
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public FTArgs()
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{
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lr = 0.05;
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dim = 100;
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ws = 5;
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epoch = 5;
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minCount = 5;
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minCountLabel = 0;
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neg = 5;
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wordNgrams = 1;
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loss = loss_name.ns;
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model = model_name.sg;
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bucket = 2000000;
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minn = 3;
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maxn = 6;
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thread = 12;
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lrUpdateRate = 100;
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t = 1e-4;
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label = "__label__";
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verbose = 2;
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pretrainedVectors = "";
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saveOutput = false;
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qout = false;
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retrain = false;
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qnorm = false;
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cutoff = 0;
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dsub = 2;
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}
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protected string lossToString(loss_name ln)
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{
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switch (ln)
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{
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case loss_name.hs:
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return "hs";
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case loss_name.ns:
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return "ns";
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case loss_name.softmax:
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return "softmax";
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}
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return "Unknown loss!"; // should never happen
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}
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protected string boolToString(bool b)
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{
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if (b)
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{
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return "true";
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}
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else
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{
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return "false";
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}
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}
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protected string modelToString(model_name mn)
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{
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switch (mn)
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{
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case model_name.cbow:
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return "cbow";
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case model_name.sg:
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return "sg";
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case model_name.sup:
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return "sup";
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}
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return "Unknown model name!"; // should never happen
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}
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#region Help
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public string printHelp()
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{
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return
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printBasicHelp() +
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printDictionaryHelp() +
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printTrainingHelp() +
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printQuantizationHelp();
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}
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private string printBasicHelp()
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{
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return "\nThe following arguments are mandatory:\n" +
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" -input training file path\n" +
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" -output output file path\n" +
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"\nThe following arguments are optional:\n" +
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" -verbose verbosity level [" + verbose + "]\n";
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}
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private string printDictionaryHelp()
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{
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return
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"\nThe following arguments for the dictionary are optional:\n" +
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" -minCount minimal number of word occurences [" + minCount + "]\n" +
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" -minCountLabel minimal number of label occurences [" + minCountLabel + "]\n" +
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" -wordNgrams max length of word ngram [" + wordNgrams + "]\n" +
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" -bucket number of buckets [" + bucket + "]\n" +
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" -minn min length of char ngram [" + minn + "]\n" +
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" -maxn max length of char ngram [" + maxn + "]\n" +
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" -t sampling threshold [" + t + "]\n" +
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" -label labels prefix [" + label + "]\n";
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}
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private string printTrainingHelp()
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{
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return
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"\nThe following arguments for training are optional:\n" +
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" -lr learning rate [" + lr + "]\n" +
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" -lrUpdateRate change the rate of updates for the learning rate [" + lrUpdateRate + "]\n" +
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" -dim size of word vectors [" + dim + "]\n" +
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" -ws size of the context window [" + ws + "]\n" +
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" -epoch number of epochs [" + epoch + "]\n" +
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" -neg number of negatives sampled [" + neg + "]\n" +
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" -loss loss function {ns, hs, softmax} [" + lossToString(loss) + "]\n" +
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" -thread number of threads [" + thread + "]\n" +
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" -pretrainedVectors pretrained word vectors for supervised learning [" + pretrainedVectors + "]\n" +
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" -saveOutput whether output params should be saved [" + boolToString(saveOutput) + "]\n";
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}
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private string printQuantizationHelp()
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{
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return
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"\nThe following arguments for quantization are optional:\n" +
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" -cutoff number of words and ngrams to retain [" + cutoff + "]\n" +
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" -retrain whether embeddings are finetuned if a cutoff is applied [" + boolToString(retrain) + "]\n" +
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" -qnorm whether the norm is quantized separately [" + boolToString(qnorm) + "]\n" +
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" -qout whether the classifier is quantized [" + boolToString(qout) + "]\n" +
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" -dsub size of each sub-vector [" + dsub + "]\n";
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}
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#endregion
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}
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}
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