createD2Vmodel.py

from gensim.models.doc2vec import Doc2Vec
from gensim.models.doc2vec import TaggedLineDocument
from tqdm import tqdm
import numpy as np
import pandas as pd
from scipy.spatial.distance import euclidean, pdist, squareform
import networkx as nx
import scipy as sp

def create_Embedding(input_file,n_dim,window_size,min_count,epochs,model_name,algtype):
    model = Doc2Vec(min_count=min_count,vector_size=n_dim,window=window_size,dm=algtype)
    t = TaggedLineDocument(input_file)
    model.build_vocab(t)
    model.train(t,total_examples=model.corpus_count,epochs=epochs)
    model.save("AbstractEmb/"+model_name)


if __name__ == '__main__':
    model_name = "d2v_model_PV-DBOW
    alg_type = 0
    n_dim = 50
    window_size=5
    input_file = "sentences_line.txt"
    min_count = 2
    epochs = 10
    print("Creating",model_name)
    create_Embedding(input_file,n_dim,window_size,min_count,epochs,model_name,algtype)
    print("Model saved")
    model_name = "d2v_model_PV-DM"
    alg_type = 1
    n_dim = 50
    window_size=5
    input_file = "sentences_line.txt"
    min_count = 2
    epochs = 10
    print("Creating",model_name)
    create_Embedding(input_file,n_dim,window_size,min_count,epochs,model_name,algtype)
    print("Model saved")