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Create Training-Ready Inputs for BERT Models

IntermediateGuided Project

Learn essential techniques to prepare data for BERT training, including tokenization, text masking, and preprocessing for masked language modeling (MLM) and next sentence prediction (NSP) tasks. This hands-on lab covers random sample selection, vocabulary building, and practical methods for creating MLM data. You will also structure inputs for NSP. By the end, you will understand how to preprocess data efficiently, ensuring it is ready for BERT model training and downstream natural language processing (NLP) tasks.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • Machine Learning, NLP, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • May 26, 2025
About this Guided Project
In this hands-on project, you will explore the essential steps involved in preparing data for training Bidirectional Encoder Representations from Transformers (BERT) models. BERT has transformed the landscape of natural language processing (NLP) by understanding context from both directions, making it highly effective for a wide range of language tasks. However, to fully leverage BERT’s capabilities, the data fed into the model must be carefully preprocessed and formatted. This lab will guide you through key tasks such as tokenization, vocabulary building, text masking, and structuring data for masked language model (MLM) and next sentence prediction (NSP) tasks.

A look at the project ahead


Through a structured, step-by-step approach, this lab covers the fundamental techniques required to preprocess text data effectively. You will start with random sample selection and progress to applying tokenization methods to segment text into smaller units suitable for model input. You’ll also learn how to build vocabularies, ensuring that your model understands the full range of words in the dataset. Special emphasis is placed on text masking, a crucial part of preparing data for MLM, where certain words are hidden to help the model learn to predict them. Additionally, you will prepare data for NSP, a task that helps BERT understand relationships between sentences.
By the end of this lab, you will have developed the skills necessary to preprocess and structure data in a way that maximizes the performance of BERT models. These techniques are essential for tasks such as sentiment analysis, question answering, and text classification, making this lab a valuable foundation for further NLP projects.

Key learning objectives


  • Understand and apply random sample selection for data preparation.
  • Learn how to tokenize text and build custom vocabularies.
  • Implement text masking to create datasets for MLMs.
  • Prepare data for NSP tasks.
  • Gain practical experience in structuring data for BERT training.

What you'll need


  • Basic understanding of Python programming.
  • Familiarity with NLP concepts (recommended but not required).
  • A web browser (Chrome, Firefox, Safari).

Get started


Dive into this lab to build essential data preparation skills for BERT model training. Whether you are developing custom NLP models or working on practical language tasks, mastering these techniques will help you take your projects to the next level.

Instructors

Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Contributors

Karan Goswami

Data Scientist

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

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