In this video, we explore how to build a Sentence Similarity Checker using Python, utilizing three powerful methods: TF-IDF, Sentence Transformers, and Word2Vec. We'll walk you through the implementation process, from loading the necessary libraries to creating a Streamlit web application for user interaction.
Why You Should Watch This Video:
🔗TF-IDF (Term Frequency-Inverse Document Frequency): Understand how this statistical measure evaluates the importance of words in a document relative to a corpus.
🔗Sentence Transformers: Discover how to leverage pre-trained BERT models to compute sentence embeddings and measure similarity.
🔗Word2Vec: Explore how this neural network model maps words to vectors of real numbers and calculates sentence similarity.
Whether you're a beginner or an experienced programmer, this tutorial will provide you with the knowledge to create your own sentence similarity checker for various applications like text analysis, NLP tasks, and more.
This video will help you to resolve:
1.) How to build a sentence similarity checker with Python
2.) Implementing TF-IDF for sentence similarity in Python
3.) Using Sentence Transformers BERT for sentence similarity
4.) Word2Vec sentence similarity tutorial
5.) Streamlit application for text similarity
6.) Python code for sentence similarity
7.) Natural Language Processing with TF-IDF, BERT, and Word2Vec
8.) Sentence similarity using machine learning in Python
9.) Comparing sentences with cosine similarity in Python
10.) Creating a text similarity checker with Streamlit and Python
⏰ Time Stamp:
00:00 Introduction to Sentence Similarity App
00:58 Importing Necessary Libraries for TF-IDF, BERT and Word2Vec
02:00 Explain TF-IDF, BERT and Word2Vec
07:20 Build a Function to Calc the Similarity
13:09 Create a Web App using Streamlit
17:42 Use Web App with different-2 example and check scores
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13 июл 2024
