This device is not compatible.

Automating Contract Review with Transformer Models

PROJECT

Automating Contract Review with Transformer Models

In this project, we’ll automate contract reviews using natural language inference (NLI) with transformer-based models to determine whether a given hypothesis is entailed by, contradicts, or is irrelevant to a provided contract.

You will learn to:

Explore and visualize the dataset.

Select state-of-the-art transformer-based models.

Evaluate model performance.

Categorize and analyze prediction errors.

Skills

Transformer Models

Natural Language Processing

Data Analysis

Prerequisites

Intermediate knowledge of Python programming and its libraries

Familiarity with natural language processing (NLP) concepts

Familiarity with transformer models architecture

Technologies

PyTorch

Matplotlib

Hugging Face

Project Description

Automating contract review involves analyzing legal documents to ensure the accuracy and clarity of legal standards. Natural language inference (NLI) techniques utilize artificial intelligence to analyze the relationships between different pieces of text. The ContractNLI dataset provides a collection of contracts and corresponding hypotheses. The models are trained on this dataset, with the goal of determining whether each hypothesis is entailed by, contradicts, or is not mentioned in the contract.

In this project, we’ll use the Matplotlib library to explore the dataset visually. Using Hugging Face’s libraries like Transformers and PyTorch, we’ll aim to leverage transformer-based models ALBERT and DistilBERT to perform NLI on contract documents, enabling faster and more accurate contract analysis.

Project Tasks

Introduction

Task 0: Get Started

Task 1: Import the Libraries

Load and Explore the Dataset

Task 2: Generate the Dataset Files

Task 3: Calculate Dataset Statistics

Task 4: Create a Visualization Function for Features

Task 5: Create the Visualization Function for Labels

Perform NLI Using Transformer Models

Task 6: Load the Tokenizer and the Model

Task 7: Encode the Features

Task 8: Encode the Labels

Task 9: Prepare Dataset for the Model

Task 10: Fine-Tune the Selected Models

Perform the Error Analysis

Task 11: Test the Selected Models

Task 12: Identify Incorrect Predictions

Task 13: Categorize the Errors

Task 14: Visualize Error Categories

Congratulations!

Hear what others have to say

Join 1.4 million developers working at companies like

"Another great hands on project to apply your knowledge learned. Thank you Educative ❤️"

Atabek BEKENOV

Senior Software Engineer

"Super excited to learn E-commerce website for my own startup venture. Thanks for your great learning platform."

Pradip Pariyar

Senior Software Engineer

"This was an excellent lesson. I learned a lot working through the process. I enjoyed it so much that I rebuilt it my AWS account to see how hard it would be to deploy to a production environment."

Renzo Scriber

Senior Software Engineer

"It was my first proper data engineering project and it was amazing."

Vasiliki Nikolaidi

Senior Software Engineer

"It's a fantastic way to do hands-on practice; I enjoy this way of learning."

Juan Carlos Valerio Arrieta

Senior Software Engineer

Relevant Courses

Use the following content to review prerequisites or explore specific concepts in detail.