Introducion: BERT Variants
Explore notable BERT variants including ALBERT, RoBERTa, ELECTRA, and SpanBERT, focusing on their unique architectures and applications. Understand knowledge distillation and its role in creating efficient BERT models like DistilBERT and TinyBERT. This lesson helps you grasp the differences and enhancements that make these variants suitable for various NLP challenges and resource constraints.
We'll cover the following...
In this section, we will explore several interesting variants of BERT. We'll learn about popular variants of BERT, such as ALBERT, RoBERTa, ELECTRA, and SpanBERT. We will also explore BERT variants based on knowledge distillation, such as DistilBERT and TinyBERT.
The following chapters are included in this section:
Different BERT Variants
BERT Variants—Based on Knowledge Distillation
Different BERT variants
We will start with understanding how ALBERT works. ALBERT is basically A Lite version of BERT model. The ALBERT model includes a few architectural changes to the BERT to minimize the training time. We will cover how ALBERT works and how it differs from BERT in detail.
Moving on, we will learn about the RoBERTa model, which stands for a Robustly Optimized BERT pre-training Approach. RoBERTa is one of the most popular variants of the BERT, and it is used in many state-of-the-art systems. RoBERTa works similarly to BERT but with a few changes in the pre-training steps. We will explore how RoBERTa works and how it differs from the BERT model in detail.
Going ahead, we will learn about the ELECTRA model, which stands for Efficiently Learning an Encoder that Classifies Token Replacements Accurately. Unlike other BERT variants, ELECTRA uses a generator and a discriminator. It is pre-trained using a new task called a replaced token detection task. We will learn how exactly ELECTRA works in detail.
At the end of the chapter, we will learn about SpanBERT. It is popularly used in use cases such as question-answering, relation extraction, and so on. We will understand how SpanBERT works by exploring its architecture.
BERT variants—Based on knowledge distillation
One of the challenges with using the pre-trained BERT model is that it is computationally expensive and very difficult to run with limited resources. The pre-trained BERT model has a large number of parameters and also high inference time, which makes it harder to use it on edge devices such as mobile phones.
To alleviate this issue, we transfer knowledge from a large pre-trained BERT to a small BERT using knowledge distillation. We will learn about several variants of the BERT model that are based on knowledge distillation.
We will begin the chapter by understanding what knowledge distillation is and how it works in detail. Next, we will learn about DistilBERT. With DistilBERT, we will see how to transfer knowledge from a large pre-trained BERT to a small BERT by using knowledge distillation in detail.
Going forward, we will learn about TinyBERT. We will understand what TinyBERT is and how it acquires knowledge from a large pre-trained BERT using knowledge distillation. We will also look into the different data augmentation methods used in TinyBERT.
At the end of the chapter, we will learn how to transfer knowledge from a large pre-trained BERT to a simple neural network.