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Semantic Search with Transformers

Learn how to use the sentence-transformers and the Faiss libraries to create an efficient search engine.

Semantic Search with Transformers

You will learn to:

Use the sentence-transformers library to generate embeddings for natural language.

Use the Faiss library to create a search index.

Preprocess the dataset using the scikit-learn library.

Use the search index to efficiently search for machine learning research papers.


Deep Learning

Natural Language Processing

Semantic Search


Basic programming skills in Python

Basic knowledge of deep learning

Basic knowledge of Transformer-based models



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Project Description

In this project, we’ll use the sentence-transformers library to perform semantic search in a corpus of machine learning research papers. sentence-transformers allows us to use Transformer models that have been fine-tuned to give semantically meaningful embeddings for natural language. Transformer-based models are known to form high-level linguistic and semantic representations when used for natural languages. As we’ll see in the Experiments section, semantic search can retrieve articles based on synonyms and similar contexts, even without the exact occurrence of the searched words. Most search engines are powered by Transformer-based models, which is the current state of the art and is quickly replacing lexical search.

We’ll encode the dataset using sentence-transformers and create an index for k-nearest-neighbors search using Facebook’s Faiss library. We will then perform a few experiments, using summaries from the dataset and text inputs to search the database for similar articles.

Project Tasks


Getting Started

Task 0: Introduction

Task 1: Import the Libraries

Task 2: Load the Data


Setting Up the Environment

Task 3: Retrieve the Model

Task 4: Generate or Load the Embeddings

Task 5: Data Preparation and Helper Methods

Task 6: Set up the Index



Task 7: Search with a Summary

Task 8: Search with a Prompt