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Fine-Tuning BERT for Text Reconstruction with Hugging Face

IntermediateGuided Project

Fine-tune BERT for text reconstruction using advanced NLP techniques, focusing on completing text by filling in the gaps. Learn to prepare datasets, employ transfer learning, and apply LLMs to downstream tasks using Hugging Face. This hands-on project is ideal for individuals with a solid understanding of machine learning, and can be completed in just 45 minutes. It offers a practical dive into the real-world applications of LLMs.

5.0 (11 Reviews)

Language

  • English

Topic

  • Deep Learning

Enrollment Count

  • 92

Skills You Will Learn

  • BERT, Fine-tuning, Generative AI, LLM, HuggingFace, NLP

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • January 5, 2026
About this Guided Project
Old documents and notes reveal our history, but reading faded or unclear text can be tough. Imagine finding a box of letters from your ancestors. Faded ink and and missing pages can leave their stories incomplete. But there's hope. What if you could use technology to restore these old papers and save important historical information future generations? Or simply improve digital notes by filling gaps in your writing, making them clear and complete? By fine-tuning models such as BERT (Bidirectional Encoder Representations from Transformers), you can take advantage of this transformative power. In this project, you'll work on an IMDB dataset to train a model to predict missing words in movie reviews. As you do, you'll learn to seamlessly integrate technology that can solve real-world problems.

You will explore advanced Natural Language Processing (NLP) techniques, focusing on fine-tuning BERT for text reconstruction. This hands-on project will teach you how to prepare datasets, employ transfer learning, and tune LLMs for downstream tasks and applications using the Hugging Face machine learning and data science platform. This project is particularly helpful for anyone with a solid understanding of machine learning who is looking to expand their skillset in NLP. You can complete it in just 45 minutes and, through practical exercises and real-world applications, you'll gain a deeper understanding of BERT's capabilities and its impact on text reconstruction tasks.

What you'll learn

By completing the project, you will:
  • Understand the fundamental concepts of BERT and its role in NLP
  • Learn how to prepare and preprocess datasets for text reconstruction tasks
  • Load pre-trained models from Hugging Face and make inferences using the Pipeline module
  • Gain proficiency in fine-tuning BERT using transfer learning techniques
  • Develop the ability to apply BERT for downstream tasks.

What you'll need

To get the most out of this guided project, you will need:
  • Basic knowledge of Python programming
  • Familiarity with NLP concepts and techniques
  • Access to the IBM Skills Network Labs environment, which comes with many necessary tools pre-installed (e.g., Docker)
  • A current version of a modern web browser such as Chrome, Edge, Firefox, Internet Explorer, or Safari
It's time to get started on your journey to harness the power of BERT for text reconstruction and elevate your data science and NLP skills!

Instructors

Fateme Akbari

Data Scientist @IBM

I'm a data-driven Ph.D. Candidate at McMaster University and a data scientist at IBM, specializing in machine learning (ML) and natural language processing (NLP). My research focuses on the application of ML in healthcare, and I have a strong record of publications that reflect my commitment to advancing this field. I thrive on tackling complex challenges and developing innovative, ML-based solutions that can make a meaningful impact—not only for humans but for all living beings. Outside of my research, I enjoy exploring nature through trekking and biking, and I love catching ball games.

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Wojciech "Victor" Fulmyk

Data Scientist at IBM

Wojciech "Victor" Fulmyk is a Data Scientist and AI Engineer on IBM’s Skills Network team, where he focuses on helping learners build expertise in data science, artificial intelligence, and machine learning. He is also a Kaggle competition expert, currently ranked in the top 3% globally among competition participants. An economist by training, he applies his knowledge of statistics and econometrics to bring a distinctive perspective to AI and ML—one that considers both technical depth and broader socioeconomic implications.

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Contributors

Victoria Nadar

Growth Marketer @IBM

Here to tell you what I learnt from my experience in the Marketing Technology Industry

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