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

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

Recipe Objective

What is a skip gram model and when to use it? As we have discussed earlier only about Word2vec and Skip Gram comes under Word2Vec. Skip Gram which predicts the the surrounding context words within specific window given current word. The input layer contains the current word and the output layer contains the context words. The hidden layer contains the number of dimensions in which we want to represent current word present at the input layer.

Access Loan Eligibility Prediction Projects 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 it as ignore because we know that there might be some warnings comming 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 take a for loop which will iterate through each sentences present in the text file, and the second for loop will tokenize the sentences into words.

Step 5 - Create a Skip Gram model

model2 = gensim.models.Word2Vec(data, min_count = 1, size = 100, window = 5, sg = 1)

Step 6 - Print the result of Skip Gram model

print("Cosine similarity between 'alice' " + "and 'wonderland' - Skip Gram : ", model2.similarity('alice', 'wonderland')) print("Cosine similarity between 'alice' " + "and 'machines' - Skip Gram : ", model2.similarity('alice', 'machines'))

Cosine similarity between 'alice' and 'wonderland' - Skip Gram :  0.9486537
Cosine similarity between 'alice' and 'machines' - Skip Gram :  0.94141114

What Users are saying..

profile image

Ray han

Tech Leader | Stanford / Yale University
linkedin profile url

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

Relevant Projects

Build a Credit Default Risk Prediction Model with LightGBM
In this Machine Learning Project, you will build a classification model for default prediction with LightGBM.

MLOps using Azure Devops to Deploy a Classification Model
In this MLOps Azure project, you will learn how to deploy a classification machine learning model to predict the customer's license status on Azure through scalable CI/CD ML pipelines.

Demand prediction of driver availability using multistep time series analysis
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

Topic modelling using Kmeans clustering to group customer reviews
In this Kmeans clustering machine learning project, you will perform topic modelling in order to group customer reviews based on recurring patterns.

Customer Churn Prediction Analysis using Ensemble Techniques
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

AWS MLOps Project for Gaussian Process Time Series Modeling
MLOps Project to Build and Deploy a Gaussian Process Time Series Model in Python on AWS

Isolation Forest Model and LOF for Anomaly Detection in Python
Credit Card Fraud Detection Project - Build an Isolation Forest Model and Local Outlier Factor (LOF) in Python to identify fraudulent credit card transactions.

Learn to Build a Siamese Neural Network for Image Similarity
In this Deep Learning Project, you will learn how to build a siamese neural network with Keras and Tensorflow for Image Similarity.

Build Real Estate Price Prediction Model with NLP and FastAPI
In this Real Estate Price Prediction Project, you will learn to build a real estate price prediction machine learning model and deploy it on Heroku using FastAPI Framework.

Credit Card Default Prediction using Machine learning techniques
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.