The DMTK Word Embedding is a parallelization of the Word2Vec algorithm on top of Multiverso. It provides an efficient "scaling to industry size" solution for word embedding.

For more details about parameters setting and performance, please view our Wiki.

##Why DMTK Word Embedding?

1. For traning a large dataset.

   The DMTK parameter server stores the parameters in a distributed way, which means that each machine just holds a partition of the entire parameter set. This allows the entire embedding vector to be very large. For example, in experiment on the ClueWeb data, the vocabulary size is 21 Million, and the parameter size reaches 6 Billion, which is the largest word embedding model ever reported in the literature,as far as we know.

2. For high quality word embedding.

   You can view the performance of Distributed Word Embedding in Wiki.

3. For less traning time.

   Large dataset need long traning time. You can accelerate process of training by use multi-machines.

1. cmake ./CMakeLists.txt

2. make

1. Get and build the DMTK Framework Multiverso.

2. Open Multiverso.sln, change configuration and platform to Release and x64 of WordEmbedding(default setting), set the include and lib path of multiverso in project property.

3. Enable openmp 2.0 support.

   To set this compiler option in the Visual Studio development environment

   1)Open the project's Property Pages dialog box.

   2)Expand the Configuration Properties node.

   3)Expand the C/C++ node.

   4)Select the Language property page.

   5)Modify the OpenMP Support property to "yes".

4. Build the solution.