How to Convert Text into Numbers Using BERT: A Beginner's Guide

How to Turn Text into Numbers Using BERT

When we want a computer to understand language, we need to convert text into something it can work with—numbers. One of the best ways to do this is by using a special tool called BERT. BERT is a model that helps the computer understand the meaning of words in a sentence by looking at all the words around them.

Here’s how you can turn your sentences into numbers using BERT.

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What is BERT?

BERT stands for Bidirectional Encoder Representations from Transformers. It’s a tool that helps computers understand text better. Instead of just looking at a sentence from left to right or right to left, BERT looks at the entire sentence at once, so it can understand the full meaning.

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Why Do We Need to Turn Text Into Numbers?

Computers can't understand words the way humans do. They need numbers to work with. So, when we give a sentence to a computer, we convert it into a series of numbers (called vectors). These vectors represent the meaning of the words in the sentence.

For example:

• The word "bank" can mean a place where money is kept or the edge of a river.
  BERT understands the difference because it looks at the words around it.

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How to Use BERT to Convert Text into Numbers

We will use the Hugging Face library to work with BERT. Follow these steps:

Step 1: Install the Required Tools

You need to install some libraries first. You can do that by running these commands in your computer’s terminal or command prompt:

pip install transformers
pip install torch

Step 2: Load BERT

Now, we will use BERT to process our text. Here’s how you do it in Python:

from transformers import BertTokenizer, BertModel
import torch

# Load BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

Step 3: Turn Your Text into Tokens

Next, we need to break down our sentence into parts (called tokens) that BERT can understand. Let’s try with a simple sentence:

text = "I love learning new things!"
inputs = tokenizer(text, return_tensors="pt")

This will convert the sentence into a format that BERT can process.

Step 4: Get the Numbers (Vectors)

Now, we run the text through BERT to get the output. The output is a set of numbers (or vectors) that represent the meaning of the sentence.

with torch.no_grad():  # We don't need to train BERT right now
    outputs = model(**inputs)

# Get the vector for the whole sentence
sentence_embedding = outputs.last_hidden_state[:, 0, :]
print(sentence_embedding.shape)

This gives you a vector that represents the entire sentence. This vector is a list of numbers that the computer can use to understand the meaning of the sentence.

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What Can You Do With These Numbers?

Now that you have the vector (the list of numbers), you can use it for different things:

• Finding Similar Sentences: You can compare vectors to see how similar two sentences are.

• Classifying Text: For example, you can use these vectors to decide if an email is spam or not.

• Searching: You can build search engines that find the most relevant results based on these vectors.

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Conclusion

BERT is a powerful tool that helps computers understand text in a more human-like way. By converting sentences into vectors, BERT makes it easier to work with text for tasks like classification, similarity, and search. All you need is a few lines of code, and you can start using BERT for your own projects!