Word2Vec implementation for .Net framework based on Google word2vec.
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.
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");OR
//more explicit options
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)
.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");