Welcome to the exciting world of BERT (Bidirectional Encoder Representations from Transformers)! In this lesson, we’ll dive into the uniqueness of BERT, explore its groundbreaking contributions to Natural Language Processing (NLP), and highlight how it compares to other models like GPT. By the end of this lesson, you’ll understand why BERT has become a game-changer in the NLP field and why it excels in tasks like sentiment analysis, question answering, and more.

What makes BERT special?

BERT is a transformer-based model designed by Google AI researchers, and it’s had a massive impact on the world of NLP since its release in 2018. BERT’s bidirectional approach to language understanding sets it apart from other models, like GPT (Generative Pre-trained Transformer). This means that, unlike GPT, which reads text from left to right (or right to left in some cases), BERT simultaneously reads text in both directions! This allows it to capture context before and after a word, leading to a deeper understanding of the meaning.

Think of it this way: Imagine you’re reading a sentence like, “The cat sat on the ____.” With a left-to-right model like GPT, the model can only predict the word “mat” after reading the entire context. But with BERT, it already knows that the word “mat” fits perfectly by considering both the previous and following context—truly powerful!

Why does BERT excel over other models?

  • Bidirectional context understanding: As mentioned, BERT’s ability to read text in both directions (left and right) allows it to understand the full context of a word, making it more accurate for NLP tasks like question answering, sentence classification, and named entity recognition (NER).

  • Pretraining and fine-tuning: BERT is pretrained on vast amounts of text and can be easily fine-tuned on specific tasks with relatively small datasets, making it incredibly versatile and effective even in specialized domains.

  • No generation, just understanding: Unlike GPT, which is generative and capable of creating text, BERT is discriminative, meaning it’s excellent at understanding and classifying input data but not generating text. This makes BERT particularly powerful in tasks like sentiment analysis and question answering, where understanding context is paramount.

BERT vs. GPT: A quick comparison

Feature

BERT

GPT

Model Type

Encoder (Bidirectional)

Decoder (Autoregressive)

Training Direction

Bidirectional (both left-to-right and right-to-left)

Left-to-right (or right-to-left)

Primary Use Case

Understanding text (classification, NER, QA)

Text generation (dialogue, content)

Pretraining Tasks

Masked Language Modeling (MLM)

Causal Language Modeling

Fine-Tuning

Easily fine-tuned for specific tasks

Fine-tuned for text-generation tasks

BERT excels in understanding language, while GPT excels in generating language. Depending on your task, you might choose BERT for its superior understanding or GPT for creating text.

Fun Fact: Where did the name BERT come from?

You might be wondering—where did the name BERT come from? Well, it’s not just a random combination of letters! BERT was named after the famous Sesame Street character, BERT. The idea behind the name is that, like BERT on the show, this model understands language deeply and meaningfully. It’s also a fun nod to the creative minds at Google.

BERT’s groundbreaking impact on NLP

Before BERT, models like Word2Vec and GloVe dominated the NLP scene. However, these models had a limitation: they could only understand words in isolation. On the other hand, BERT represents words in context, meaning that the meaning of a word depends on the other words around it. For example, the word "bank" has different meanings in "river bank" and "bank account." BERT understands these nuances, making it a huge leap forward.

Some notable milestones where BERT made waves:

  • SQuAD (Stanford Question Answering Dataset): BERT set a new record by outperforming human-level performance in question answering!

  • GLUE benchmark: BERT achieved state-of-the-art results across various NLP tasks, including textual entailment, sentence similarity, and more.

What this course covers

  • A Primer on Transformers: This chapter explains the transformer model in detail. We will understand how the encoder and decoder of the transformer work by looking at their components in detail.

  • Understanding the BERT model: This chapter helps us to understand the BERT model. We will learn how the BERT model is pre-trained using Masked Language Model (MLM) and Next Sentence Prediction (NSP) tasks. We will also learn several interesting subword tokenization algorithms.

  • Getting Hands-On with BERT: This chapter explains how to use the pre-trained BERT model. We will learn how to extract contextual sentences and word embeddings using the pre-trained BERT model. We will also learn how to fine-tune the pre-trained BERT for downstream tasks such as question-answering, text classification, and more.

  • BERT Variants I—ALBERT, RoBERTa, ELECTRA, and SpanBERT: This chapter explains several variants of BERT. We will learn how BERT variants differ from BERT and how they are useful in detail.

  • BERT Variants II—Based on Knowledge Distillation: This chapter deals with BERT models based on distillation, such as DistilBERT and TinyBERT. We will also learn how to transfer knowledge from a pre-trained BERT model to a simple neural network.

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  • Exploring BERTSUM for Text Summarization: This chapter explains how to fine-tune the pre-trained BERT model for a text summarization task. We will understand how to fine-tune BERT for extractive summarization and abstractive summarization in detail.

  • Applying BERT to Other Languages: This chapter deals with applying BERT to languages other than English. We will learn about the effectiveness of multilingual BERT in detail. We will also explore several cross-lingual models, such as XLM and XLM-R.

  • Exploring Sentence and Domain-Specific BERT: This chapter explains Sentence-BERT, which is used to obtain sentence representation. We will also learn how to use the pre-trained Sentence-BERT model. Along with this, we will also explore domain-specific BERT models such as ClinicalBERT and BioBERT.

  • Working with VideoBERT, BART, and More: This chapter deals with an interesting type of BERT called VideoBERT. We will also learn about a model called BART in detail. We will also explore a popular library known as ktrain.