Is BERT still used in 2025?

Yes. BERT and its descendants (RoBERTa, DistilBERT, DeBERTa) are widely used in production for search ranking, document classification, sentiment analysis, and named entity recognition. Larger generative models like GPT-4 dominate headlines, but BERT-style encoders remain the workhorse of many real enterprise NLP systems.

BERT — What It Is, How It Reads Language in Both Directions & Why It Changed Search Forever

Q: What is BERT in simple terms?

BERT is a language model that reads a sentence by looking at all words simultaneously — left and right context at once. Before BERT, models read left to right like a person reading for the first time. BERT reads the way you do when you re-read a sentence — knowing the end helps you understand the beginning.

Q: How does Google use BERT?

Google integrated BERT into its Search algorithm in 2019 — one of the biggest Search updates in years. BERT helps Google understand the intent behind search queries, especially long conversational ones. Searching 'can you get medicine for someone pharmacy' now returns results about picking up prescriptions for others, not just general pharmacy information.

Q: What is the difference between BERT and GPT?

BERT is an encoder — it reads and understands text, making it excellent at classification, search, and question answering. GPT is a decoder — it generates text word by word, making it excellent at writing, conversation, and completion. BERT understands. GPT generates. Most production AI systems use both types for different tasks.

⚡ BERT (Bidirectional Encoder Representations from Transformers) is Google’s 2018 language model that reads every word in the context of all surrounding words simultaneously — left and right at once. It transformed how machines understand language and powers Google Search’s ability to interpret the true intent behind your queries.

Category: NLP & Language · Difficulty: Intermediate · Last updated: 15 May 2026 · 5 min read

What is BERT?

Consider the word “bank.” In “I deposited money at the bank,” bank means financial institution. In “we sat on the river bank,” bank means a riverbank. Humans instantly know which meaning is right because we read the full sentence — context from both sides. Earlier AI language models read left to right and struggled with this. BERT fixed it.

BERT (Bidirectional Encoder Representations from Transformers) was developed by Google and published in 2018. It reads text bidirectionally — processing all words simultaneously, with every word attending to every other word in the sentence. This lets BERT understand “bank” correctly in both sentences, understand pronoun references, and capture the nuance that separates human-level reading comprehension from word-by-word processing.

How BERT works

BERT was pre-trained on the entire Wikipedia and BookCorpus datasets using two tasks: masked language modelling (predicting randomly hidden words) and next sentence prediction.
Masking forces BERT to understand context from both directions — if the middle word is hidden, it must use left and right context together to predict it.
This pre-training produces rich contextual representations — each word’s meaning encoded as a vector that captures its full context.
For a specific task (search ranking, sentiment analysis, question answering), BERT is fine-tuned on a smaller labelled dataset — a process that takes hours not weeks.
At inference, BERT reads the input and produces contextual embeddings used to classify, rank, or extract information.

Real-world examples

Not theory — what real teams actually shipped using this technique.

Google uses BERT in Search to interpret long conversational queries. Before BERT, “parking on a hill with no curb” returned generic parking results. After BERT, it correctly returns advice about turning wheels when parking on hills without curbs.
Bing, DuckDuckGo, and most major search engines now use BERT-style models for query understanding and result ranking.
Customer support teams fine-tune BERT on their ticket history to automatically classify and route incoming support requests — achieving 90%+ accuracy with a fraction of the labelled data that older approaches needed.

Common pitfalls

BERT only encodes — it cannot generate text. For tasks requiring text generation, GPT-style decoders are needed.
Context window limit — base BERT handles only 512 tokens. Long documents must be chunked, which can break context at important boundaries.
Computationally heavy for inference at scale — base BERT has 110 million parameters. DistilBERT (a distilled version) retains 97% performance at 40% the size, often a better production choice.
Fine-tuning requires labelled data — BERT needs task-specific labelled examples to specialise. Without them, performance on specific tasks can be mediocre.

Frequently asked questions

QUESTION 1 What is BERT in simple terms?

ANSWER 1 A language model that reads a sentence by looking at all words simultaneously — left and right context at once. Before BERT, models read left to right. BERT reads the way you re-read a sentence — knowing the end helps understand the beginning.

QUESTION 2 How does Google use BERT?

ANSWER 2 Google integrated BERT into Search in 2019 to understand the intent behind queries, especially long conversational ones — one of its biggest algorithm updates in years.

QUESTION 3 What is the difference between BERT and GPT?

ANSWER 3 BERT is an encoder — understands text, excellent for search and classification. GPT is a decoder — generates text, excellent for writing and conversation. BERT understands. GPT generates.

QUESTION 4 Is BERT still used in 2026?

ANSWER 4 Yes. BERT and its descendants (RoBERTa, DistilBERT, DeBERTa) remain the workhorse of enterprise NLP — search ranking, document classification, sentiment analysis, and named entity recognition.

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