Word2Vec and FastText Word Embedding with Gensim in Python

In this NLP Project, you will learn how to use the popular topic modelling library Gensim for implementing two state-of-the-art word embedding methods Word2Vec and FastText models.

START PROJECT

Project Template Outcomes

  • Understanding the business problem
  • Understanding the architecture to build the Streamlit application
  • Learning the Word2Vec and FastText model
  • Importing the dataset and required libraries
  • Performing data Pre-processing
  • Performing basic Exploratory Data Analysis (EDA)
  • Training the Skip-gram model with varying parameters
  • Training the FastText model with varying parameters
  • Understanding and performing the model embeddings
  • Plotting the PCA plots
  • Getting vectors for each attribute
  • Performing the Cosine similarity function
  • Pre-processing the input query
  • Evaluating the results
  • Creating a function to return top ‘n’ similar results for a given query
  • Understanding the code for executing the Streamlit application.
  • Run the Streamlit application.

Get started today

Request for free demo with us.

white grid

Architecture Diagram

Word2Vec and FastText Word Embedding with Gensim in Python architecture diagram

Unlimited 1:1 Live Interactive Sessions

  • number-icon
    60-minute live session

    Schedule 60-minute live interactive 1-to-1 video sessions with experts.

  • number-icon
    No extra charges

    Unlimited number of sessions with no extra charges. Yes, unlimited!

  • number-icon
    We match you to the right expert

    Give us 72 hours prior notice with a problem statement so we can match you to the right expert.

  • number-icon
    Schedule recurring sessions

    Schedule recurring sessions, once a week or bi-weekly, or monthly.

  • number-icon
    Pick your favorite expert

    If you find a favorite expert, schedule all future sessions with them.

  • number-icon
    Use the 1-to-1 sessions to
    • Troubleshoot your projects
    • Customize our templates to your use-case
    • Build a project portfolio
    • Brainstorm architecture design
    • Bring any project, even from outside ProjectPro
    • Mock interview practice
    • Career guidance
    • Resume review
squarebox svg

Customers sharing their love on online platforms

user review

Source: quora

user review

Source: quora

user review

Source: trustpilot

user review

Source: quora

user review

Source: quora

user review

Source: quora

user review

Source: trustpilot

user review

Source: quora

user review

Source: quora

user review

Source: quora

user review

Source: quora

user review

Source: quora

user review

Source: quora

arrow left svg
arrow right svg

Benefits

250+ end-to-end project solutions

250+ end-to-end project solutions

Each project solves a real business problem from start to finish. These projects cover the domains of Data Science, Machine Learning, Data Engineering, Big Data and Cloud.

15 new projects added every month

15 new projects added every month

New projects every month to help you stay updated in the latest tools and tactics.

500,000 lines of code

500,000 lines of code

Each project comes with verified and tested solutions including code, queries, configuration files, and scripts. Download and reuse them.

600+ hours of videos

600+ hours of videos

Each project solves a real business problem from start to finish. These projects cover the domains of Data Science, Machine Learning, Data Engineering, Big Data and Cloud.

Cloud Lab Workspace

Cloud Lab Workspace

New projects every month to help you stay updated in the latest tools and tactics.

Unlimited 1:1 sessions

Unlimited 1:1 sessions

Each project comes with verified and tested solutions including code, queries, configuration files, and scripts. Download and reuse them.

Technical Support

Technical Support

Chat with our technical experts to solve any issues you face while building your projects.

7 Days risk-free trial

We offer an unconditional 7-day money-back guarantee. Use the product for 7 days and if you don't like it we will make a 100% full refund. No terms or conditions.

Payment Options

Payment Options

0% interest monthly payment schemes available for all countries.

listed companies

Testimonials

white grid

Comparison with other platforms

We provide ready-made project templates that solve real business problems, end-to-end and comes with solution code,
explanation videos, cloud lab environment and tech support.

End-to-end implementation
Real industry grade projects
by industry experts
Ready-made solutions to real
business problems
Detailed Explanations
kaggle
icon
Courses/ Tutorials
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon

Our expert panel

world bg

Project Description

Business Objective

The biggest challenge in the NLP (Natural Language Processing) domain is to extract the context from text data, and word embeddings are the solution that represents words as semantically meaningful dense vectors. They overcome many of the problems that other techniques like one-hot encodings and TFIDF have.

Embeddings boost generalization and performance for downstream NLP applications even with fewer data. So, word embedding is the feature learning technique where words or phrases from the vocabulary are mapped to vectors of real numbers capturing the contextual hierarchy.

General word embeddings might not perform well enough on all the domains. Hence, we need to build domain-specific embeddings to get better outcomes. In this project, we will create medical word embeddings using Word2vec and FastText in python.

Word2vec is a combination of models used to represent distributed representations of words in a corpus. Word2Vec (W2V) is an algorithm that accepts text corpus as an input and outputs a vector representation for each word. FastText is a library created by the Facebook Research Team for efficient learning of word representations and sentence classification.

This project aims to use the trained models (Word2Vec and FastText) to build a search engine and Streamlit UI.

Data Description 

We are considering a clinical trials dataset for our project based on Covid-19. The link for this dataset is as follows:

Link:https://dimensions.figshare.com/articles/dataset/Dimensions_COVID-19_publications_datasets_and_clinical_trials/11961063

There are 10666 rows and 21 columns present in the dataset. The following two columns are essential for us,

  • Title
  • Abstract

Aim

The project aims to train the Skip-gram and FastText models for performing word embeddings and then building a search engine along with a Streamlit UI.

Tech stack

  • Language - Python
  • Libraries and Packages - pandas, numpy, matplotlib, plotly, gensim, streamlit, nltk.

Environment – Jupyter Notebook

Approach

  1. Importing the required libraries
  2. Reading the dataset
  3. Pre-processing
    • Remove URLs
    • Convert text to lower case
    • Remove numerical values
    • Remove punctuation.
    • Perform tokenization
    • Remove stop words
    • Perform lemmatization
    • Remove ‘\n’ character from the columns
  1. Exploratory Data Analysis (EDA) 
    • Data Visualization using word cloud
  1. Training the ‘Skip-gram’ model
  2. Training the ‘FastText’ model
  3. Model embeddings – Similarity
  4. PCA plots for Skip-gram and FastText models
  5. Convert abstract and title to vectors using the Skip-gram and FastText model
  6. Use the Cosine similarity function
  7. Perform input query pre-processing
  8. Define a function to return top ‘n’ similar results  
  9. Result evaluation
  10. Run the Streamlit Application

Latest Blogs

Adaboost Algorithm Explained in Depth

Adaboost Algorithm Explained in Depth

Exploring the AdaBoost Algorithm Applications, Working and Projects in Python.| ProjectPro

Data Engineer’s Guide to 6 Essential Snowflake Data Types

Data Engineer’s Guide to 6 Essential Snowflake Data Types

From strings to timestamps, six key snowflake datatypes a data engineer must know for optimized analytics and storage | ProjectPro

30+ NumPy Interview Questions and Answers for Data Analysts

30+ NumPy Interview Questions and Answers for Data Analysts

Prepare for success in data analyst interviews with our curated list of 30+ NumPy Interview Questions and Answers. | ProjectPro

View all blogs

We power Data Science & Data Engineering
projects at

projectpro i trusted leader projectpro i trusted leader projectpro i trusted leader

Join more than
115,000+ developers worldwide

Get a free demo