BERT (Bidirectional Encoder Representations from Transformers)

What is BERT?

BERT stands for Bidirectional Encoder Representations from Transformers.
It’s a machine learning model made by Google in 2018 that helps computers understand human language better than ever before.

In short:

BERT helps machines understand the meaning of words in a sentence, just like humans do.

---

Why is BERT Special?

Before BERT, most models read text in one direction—either left to right or right to left.
BERT reads the sentence in both directions at the same time.
This helps it truly understand the context of each word.

For example:

"He sat by the bank and watched the river."
vs
"She went to the bank to deposit money."

The word "bank" means different things in both sentences.
Old models would give "bank" the same meaning everywhere.
BERT knows the difference, because it sees the words before and after it.

---

How BERT Works (In Simple Terms)

1. BERT reads a lot of text (like Wikipedia) and learns the meaning of words by looking at their surroundings.

2. It learns in two main ways:

   • Masked Words: Some words are hidden (like blanks), and BERT guesses them.

   • Sentence Pairs: It also learns whether one sentence follows another.

3. Once trained, BERT can be fine-tuned for specific tasks like:

   • Classifying emails as spam or not

   • Answering questions

   • Understanding customer reviews

   • Finding useful information in resumes or documents

---

Where is BERT Used?

You’ve probably used BERT without knowing it.

• Google Search uses BERT to understand your queries better

• Chatbots use BERT to understand and respond to your messages

• Email filters, voice assistants, resume screeners—they all use models like BERT

---

Why You Should Care

If you’ve ever:

• Typed something into Google

• Talked to a chatbot

• Used voice commands

• Read auto-generated answers online...

Then you’ve likely seen BERT in action.
It’s one of the reasons why AI today understands language so well.