Building a Mini Version of BERT from Scratch

2026-03-01 · Source: Naturallanguageprocessing on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

BERT (Bidirectional Encoder Representations from Transformers), introduced by Google in 2018, fundamentally reshaped Natural Language Processing by enabling deep bidirectional context modeling, processing entire sequences simultaneously. While the full BERT-base model has 12 transformer layers, a hidden size of 768, 12 attention heads, and 110 million parameters, a mini version can be built for learning purposes. This scaled-down model might feature two to four transformer layers, a hidden size of 128-256, two to four attention heads, a vocabulary of a few thousand tokens, and a maximum sequence length of 64 or 128. It preserves core principles like transformer encoders, bidirectional attention, and masked language modeling, using combined token, position, and segment embeddings as input. Pretraining involves Masked Language Modeling and Next Sentence Prediction, with training typically using Adam/AdamW on a text corpus.

Key takeaway

For AI Engineers and Machine Learning Engineers seeking to deepen their understanding of transformer architectures, building a mini BERT from scratch is a highly effective exercise. This hands-on approach demystifies the interplay of embeddings, attention mechanisms, and pretraining objectives, providing a foundational grasp essential for innovating beyond existing models. You should consider this as a core learning project to build intuition for advanced NLP systems.

Key insights

Building a mini BERT from scratch clarifies transformer architecture, bidirectional context, and pretraining objectives.

Principles

• Transformers use self-attention, not recurrence.
• BERT's bidirectionality uses left and right context.
• Pretraining on unlabeled text is key for fine-tuning.

Method

Construct a mini BERT by scaling down layers (2-4), hidden size (128-256), and attention heads (2-4), while implementing token, position, and segment embeddings, transformer encoder blocks, and pretraining with Masked Language Modeling and Next Sentence Prediction.

In practice

• Implement embeddings as learnable parameters.
• Use Adam or AdamW optimizer for training.
• Evaluate by testing masked token prediction.

Topics

• BERT
• Transformer Architecture
• Natural Language Processing
• Masked Language Modeling
• Self-Attention Mechanisms

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Naturallanguageprocessing on Medium.