• Turing Talks
  • Posts
  • Issue #17: Understanding BERT: The Key to Advanced Language Models

Issue #17: Understanding BERT: The Key to Advanced Language Models

Learn the fundamentals of BERT, a revolutionary model in natural language processing, and how to work with it.

Have you ever wondered how Google seems to understand exactly what you mean, even when your search terms are a bit off?

Or how your favorite voice assistant can comprehend complex questions?

The secret behind much of this smart technology is a powerful tool called BERT.

In this article, I’ll break down what BERT is, why it’s a game-changer in the world of natural language processing (NLP), and how you can get started with a simple code example.

What is BERT?

BERT stands for Bidirectional Encoder Representations from Transformers. It is an advanced method developed by Google for natural language processing (NLP).

It represents a shift in how computers understand human language.

Imagine you’re trying to understand a sentence with a word that has multiple meanings. For example, the word “bank” could refer to the side of a river or a financial institution. This is where BERT shines.

Instead of just looking at the words before it, like we usually read, BERT looks at the words before and after that word at the same time.

This way, the model gets a fuller picture of what the word means based on the entire sentence, not just part of it. It’s like having a conversation with someone who listens to everything you say before and after a question before answering it.

This bidirectional approach allows BERT to grasp the nuanced meanings of words within their specific context, leading to more accurate interpretations of text.

BERT supports many of the recent improvements in search engines, language translation services, and conversational AI.

Why BERT Matters

BERT excels at understanding the context, helping computers grasp the meaning of ambiguous language.

This has huge implications for improving search engines, translation services, and even generating text that feels more natural to humans.

  • Understanding Context: BERT’s ability to understand the context of words in a sentence from both directions leads to more accurate interpretations of the meaning of text, which is crucial for understanding human language.

  • Improving Search Engines: BERT has been used to enhance search engine algorithms, allowing them to better understand the intent behind users’ queries. This means that search results are more relevant and useful to what people are looking for.

  • Enhancing Language-Based Applications: Applications like language translation, question-answering systems, and virtual assistants benefit significantly from BERT. They become more accurate and conversational, improving user experience and making technology more accessible.

  • Handling Ambiguity in Language: BERT’s deep understanding of context helps it deal with ambiguity in language, distinguishing between different meanings of the same word based on context. This is crucial for accurate language interpretation and translation.

  • Advancing AI Research: BERT represents a significant step forward in machine learning and AI research, pushing the boundaries of what’s possible in understanding and generating human-like text. It opens up new possibilities for AI applications and has set a new standard in the field of NLP.

Overall, BERT matters because it represents a leap forward in how machines understand and interact with human language, making technology more intuitive and effective in processing and generating text.

How BERT Works

BERT makes use of a transformer, an attention mechanism that learns contextual relations between words (or sub-words) in a text.

In its base form, a transformer includes two separate mechanisms — an encoder that reads the text input and a decoder that produces a prediction for the task. However, BERT only uses the encoder mechanism.

By adopting this approach, BERT models can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial modifications to the underlying model.

Working with BERT

Let’s build a simple sentiment analyzer using BERT. We will be using the Huggingface Transformer’s library to use a pre-trained model of BERT and use it to build a sentiment analyzer. 

from transformers import pipeline

# Load BERT model for text classification
classifier = pipeline("sentiment-analysis", model="bert-base-uncased")

# Define input text
text = "It was a fantastic movie and I loved it!"

# Perform sentiment analysis
result = classifier(text)

# Map output label to human-readable sentiment
if result[0]['label'] == 'LABEL_1':
    sentiment_label = 'Positive'
    sentiment_label = 'Negative'

# Print result
print("Sentiment:", sentiment_label)
print("Score:", result[0]['score'])

Let’s look at what this code does:

  • First, we import the required modules from the transformers library.

  • Next, we load the pre-trained model and tokenizer. We specify a model name (bert-base-uncased) that represents a BERT model. The tokenizer is loaded to preprocess text in the way BERT expects (e.g. converting text to lowercase).

  • Next, we create a sentiment analysis pipeline from the transformers library. The pipeline function of Hugging Face Transformers abstracts away much of the manual work of preprocessing and applying the model. We specify sentiment analysis as the task to automatically handle tokenization, model inference, and output interpretation.

  • We then give it an input, which in our case is a sentence to analyze the sentiment of. You can replace this with any text you want to analyze.

  • Next, the example text is sent to the pipeline to get the sentiment analysis results.

  • Finally, we print the sentiment of along with the confidence score (how confident the model is about the result). 

Here is the output for the above code. 

Sentiment: Negative
Score: 0.5871706604957581

The pipeline function makes it very straightforward to apply pre-trained models to specific tasks, including sentiment analysis. The label and score give you a quick understanding of the model's sentiment prediction and its confidence in that prediction, respectively.

This example provides a basic understanding of how you can use BERT for a sentiment analysis task. The model takes in a sentence, processes it to understand its context, and predicts its sentiment as either positive or negative.


BERT represents a significant leap forward in the ability of machines to understand and interact with human language. Its bidirectional training and context-aware capabilities enable a wide range of applications, from enhancing search engine results to creating more powerful chatbots.

By experimenting with BERT and other NLP models, you can begin to explore the vast potential of language understanding technologies. Whether you’re a seasoned developer or just starting, the world of NLP offers endless opportunities for innovation and improvement.

Remember, this example is just the beginning. As you dive deeper into BERT and NLP, you’ll discover more complex and powerful ways to use these tools. Happy coding!

Hope you enjoyed this article. If you have any questions, let me know in the comments. See you soon with a new topic.

Join the conversation

or to participate.