BERT Overview

BERT stands for Bidirectional Encoder Representations from Transformers. It is one of the most influential models in modern Natural Language Processing.

BERT fundamentally changed how machines understand language by learning context from both directions at the same time.

Instead of reading text only left-to-right or right-to-left, BERT looks at the entire sentence simultaneously.

Why BERT Was a Breakthrough

Earlier language models processed text in a single direction.

This meant the meaning of a word was often incomplete because future context was ignored.

BERT introduced true bidirectional understanding, allowing each word to be interpreted using both its left and right context.

This made BERT especially powerful for tasks that require deep language understanding.

Bidirectional Context Explained

Consider the sentence:

"The bank will not approve the loan."

The word bank could mean a financial institution or the side of a river.

By seeing the entire sentence, BERT correctly understands that bank refers to a financial institution.

BERT Architecture

BERT is built entirely using the Transformer encoder.

It does not use:

• RNNs • LSTMs • CNNs

Instead, it relies on:

• Self-attention • Multi-head attention • Deep encoder stacks

This architecture allows BERT to scale efficiently and learn rich representations.

Pretraining Before Fine-Tuning

BERT is first trained on large amounts of unlabeled text.

This phase is called pretraining.

After pretraining, BERT can be fine-tuned on smaller labeled datasets for specific tasks.

This two-step process made transfer learning practical and highly effective in NLP.

Masked Language Modeling (MLM)

One of BERT’s core pretraining tasks is Masked Language Modeling.

Random words in a sentence are hidden, and the model learns to predict them.

Sentence: "Deep learning is very powerful"
Masked:   "Deep [MASK] is very powerful"
Target:   "learning"

This forces the model to learn context from both sides of the masked word.

Next Sentence Prediction (NSP)

BERT also learns relationships between sentences.

Given two sentences, the model predicts whether the second sentence logically follows the first.

This helps BERT understand:

• Sentence coherence • Discourse relationships • Document-level meaning

Why BERT Uses Only Encoders

BERT focuses on understanding language, not generating it.

Encoder-only architecture is ideal for:

• Classification • Question answering • Named entity recognition • Semantic similarity

This design choice makes BERT extremely strong for comprehension tasks.

Using Pretrained BERT Models

In practice, we rarely train BERT from scratch.

Instead, we load pretrained weights and fine-tune them for our task.

from transformers import BertTokenizer, BertModel

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = BertModel.from_pretrained("bert-base-uncased")

inputs = tokenizer("Deep learning is powerful", return_tensors="pt")
outputs = model(**inputs)

This gives us contextual embeddings that capture deep semantic meaning.

Strengths of BERT

BERT excels at understanding language in context.

It performs especially well in:

• Reading comprehension • Question answering • Text classification • Semantic search

Its bidirectional nature makes it far more accurate than earlier models.

Limitations of BERT

Despite its power, BERT has limitations.

• Computationally expensive • Not designed for text generation • Fixed maximum sequence length

Later models improved upon these limitations while keeping BERT’s core ideas.

Exercises

Quick Check

Next, we will explore GPT and see how autoregressive transformer models are built for language generation.