The Word2vec algorithm is used to compute the vector representations of words from an input dataset.
There are six parameters that have to be considered :
* embedding_size : Denotes the dimension of the embedding vector
* min_occurrence : Removes all words that do not appear at least ‘n’ number of times
* max_vocabulary_size : Denotes the total number of unique words in the vocabulary
* num_skips : Denotes the number of times you can reuse an input to generate a label
* num_sampled : Denotes the number of negative examples to sample from the input
* skip_window : Denotes words to be considered or not for processing
