Random Word2Vec Model Generation from Text

This custom function generates a Word2Vec model from a given text using the gensim library. This model can be used for text processing and natural language processing tasks.

Technology Stack : gensim

Code Type : Custom function

Code Difficulty : Intermediate

                
                    
import gensim
import random

def generate_random_word2vec_model(text, vector_size=100, window=5, min_count=5):
    """
    Generate a random Word2Vec model from given text.

    :param text: A string containing the text to be used for training the model.
    :param vector_size: The size of the word vectors.
    :param window: The maximum distance between the current and predicted word within a sentence.
    :param min_count: The minimum count of a word to consider when training the model.
    :return: A trained Word2Vec model.
    """
    # Create a dictionary representation of the documents.
    dictionary = gensim.corpora.Dictionary([text.split()])
    
    # Train a Word2Vec model on the dictionary.
    sentences = [dictionary.doc2bow(text.split())]
    model = gensim.models.Word2Vec(sentences, vector_size=vector_size, window=window, min_count=min_count)
    
    return model