What is a cbow model in nlp and when to use it?

This recipe explains what is a cbow model in nlp and when to use it

Recipe Objective

What is a cbow model and when to use it? As we have discussed earlier only about Word2vec and CBOW comes under Word2Vec. CBOW (Continuous Bag of Words) which predicts the current word given the context of words within a specific window. The output layer containing the current word and the input layer contains context words. The other layer which is called a hidden layer it contains the number of dimensions where we want to represent the current word present at the output layer.

Build a Multi Touch Attribution Model in Python with Source Code

Step 1 - Import the necessary libraries

from nltk.tokenize import sent_tokenize, word_tokenize import warnings warnings.filterwarnings(action = 'ignore') import gensim from gensim.models import Word2Vec

Here we have imported the necessary packages along with the warnings and kept them as ignore because we know that there might be some warnings coming up when we run our program, but that can be ignored.

Step 2 - load the sample data

sample = open("/content/alice_in_wonderland.txt", "r") s = sample.read()

Step 3 - Replace the escape character with spaces

f = s.replace("\n", " ")

Step 4 - Iterate and tokenize

import nltk nltk.download('punkt') data = [] for i in sent_tokenize(f): temp = [] for j in word_tokenize(i): temp.append(j.lower()) data.append(temp)

Here we are taking a list as variable named data which is initially empty, after that we are going to take a for loop which will iterate through each sentence present in the text file, and the second for loop will tokenize the sentences into words.

Step 5 - Create a CBOW model

model1 = gensim.models.Word2Vec(data, min_count = 1, size = 100, window = 5)

Step 6 - Print the result of CBOW model

print("Cosine similarity between 'alice' " + "and 'wonderland' - CBOW : ", model1.similarity('alice', 'wonderland')) print("Cosine similarity between 'alice' " + "and 'machines' - CBOW : ", model1.similarity('alice', 'machines'))

Cosine similarity between 'alice' and 'wonderland' - CBOW :  0.99817955
Cosine similarity between 'alice' and 'machines' - CBOW :  0.9881186

What Users are saying..

profile image

Jingwei Li

Graduate Research assistance at Stony Brook University
linkedin profile url

ProjectPro is an awesome platform that helps me learn much hands-on industrial experience with a step-by-step walkthrough of projects. There are two primary paths to learn: Data Science and Big Data.... Read More

Relevant Projects

Learn to Build a Polynomial Regression Model from Scratch
In this Machine Learning Regression project, you will learn to build a polynomial regression model to predict points scored by the sports team.

Build Portfolio Optimization Machine Learning Models in R
Machine Learning Project for Financial Risk Modelling and Portfolio Optimization with R- Build a machine learning model in R to develop a strategy for building a portfolio for maximized returns.

Learn to Build an End-to-End Machine Learning Pipeline - Part 2
In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, incorporating Hopsworks' feature store and Weights and Biases for model experimentation.

Build a Face Recognition System in Python using FaceNet
In this deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.

Multilabel Classification Project for Predicting Shipment Modes
Multilabel Classification Project to build a machine learning model that predicts the appropriate mode of transport for each shipment, using a transport dataset with 2000 unique products. The project explores and compares four different approaches to multilabel classification, including naive independent models, classifier chains, natively multilabel models, and multilabel to multiclass approaches.

Recommender System Machine Learning Project for Beginners-2
Recommender System Machine Learning Project for Beginners Part 2- Learn how to build a recommender system for market basket analysis using association rule mining.

Customer Market Basket Analysis using Apriori and Fpgrowth algorithms
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.

Build a Multi ClassText Classification Model using Naive Bayes
Implement the Naive Bayes Algorithm to build a multi class text classification model in Python.

Build a Multi Touch Attribution Machine Learning Model in Python
Identifying the ROI on marketing campaigns is an essential KPI for any business. In this ML project, you will learn to build a Multi Touch Attribution Model in Python to identify the ROI of various marketing efforts and their impact on conversions or sales..

Build Time Series Models for Gaussian Processes in Python
Time Series Project - A hands-on approach to Gaussian Processes for Time Series Modelling in Python