With TrainW2V you can train a word2vec model from raw text
Possible parameters are:
-input (required)
Path of the raw file
-output (required)
Path of the output model
-dimension (optional: positive integer)
Desired dimension of the vectors (default: 100)
-iter (optional: positive integer)
Number of iterations (default: 1)
-minfreq (optional: positive integer)
Minimum term frequency (default: 5).
NOTICE: any term with lower frequency than the specified frequency will not be considered while training
-seed (optional: positive integer)
Seed size (default: 42)
-window (optional: positive integer)
Window size (default: 5)
With SearchW2V you can train a word2vec model from raw text
Possible parameters are:
-model
Path of the word2vec model
-gmodel
Specify if the model was trained with Google format.
-input (optional)
Path to the input file. Note that the file should have a word/line format.
-term (optional)
Input a word to find its word embedding. NOTICE: both term and input file can't be empty
-output (optional)
Path of the output file.
-nearest (optional: positive integer)
Number of nearest word embedding (default: 10)