implementation Word2Vec(https://code.google.com/p/word2vec/) for .Net framework
var builder = Word2VecBuilder.Create(); if ((i = ArgPos("-train", args)) > -1) builder.WithTrainFile(args[i + 1]); if ((i = ArgPos("-output", args)) > -1) builder.WithOutputFile(args[i + 1]); //to all other parameters will be set default values var word2Vec = builder.Build(); word2Vec.TrainModel(); var distance = new Distance(args[i + 1]); BestWord bestwords = distance.Search("some_word");
//more explicit option string trainfile="C:/data.txt"; string outputFileName = "C:/output.bin"; var word2Vec = Word2VecBuilder.Create() .WithTrainFile(trainfile)// Use text data to train the model; .WithOutputFile(outputFileName)//Use to save the resulting word vectors / word clusters .WithSize(200)//Set size of word vectors; default is 100 .WithSaveVocubFile()//The vocabulary will be saved to <file> .WithDebug(2)//Set the debug mode (default = 2 = more info during training) .WithBinary(1)//Save the resulting vectors in binary moded; default is 0 (off) .WithCBow(1)//Use the continuous bag of words model; default is 1 (use 0 for skip-gram model) .WithAlpha(0.05)//Set the starting learning rate; default is 0.025 for skip-gram and 0.05 for CBOW .WithWindow(7)//Set max skip length between words; default is 5 .WithSample((float) 1e-3)//Set threshold for occurrence of words. Those that appear with higher frequency in the training data twill be randomly down-sampled; default is 1e-3, useful range is (0, 1e-5) .WithHs(0)//Use Hierarchical Softmax; default is 0 (not used) .WithNegative(5)//Number of negative examples; default is 5, common values are 3 - 10 (0 = not used) .WithThreads(5)//Use <int> threads (default 12) .WithIter(5)//Run more training iterations (default 5) .WithMinCount(5)//This will discard words that appear less than <int> times; default is 5 .WithClasses(0)//Output word classes rather than word vectors; default number of classes is 0 (vectors are written) .Build(); word2Vec.TrainModel(); var distance = new Distance(outputFile); BestWord bestwords = distance.Search("some_word");
##Information from Google word2vec: ###Tools for computing distributed representtion of words
We provide an implementation of the Continuous Bag-of-Words (CBOW) and the Skip-gram model (SG), as well as several demo scripts.
Given a text corpus, the word2vec tool learns a vector for every word in the vocabulary using the Continuous Bag-of-Words or the Skip-Gram neural network architectures. The user should to specify the following:
- desired vector dimensionality
- the size of the context window for either the Skip-Gram or the Continuous Bag-of-Words model
- training algorithm: hierarchical softmax and / or negative sampling
- threshold for downsampling the frequent words
- number of threads to use
- the format of the output word vector file (text or binary)
Usually, the other hyper-parameters such as the learning rate do not need to be tuned for different training sets.
The script demo-word.sh downloads a small (100MB) text corpus from the web, and trains a small word vector model. After the training is finished, the user can interactively explore the similarity of the words.
More information about the scripts is provided at https://code.google.com/p/word2vec/