How to Use BERT and GPT for Text Processing
How to Use BERT and GPT for Text Processing
In the world of Natural Language Processing (NLP), two models have revolutionized how machines understand language: BERT and GPT.
Both models are part of the transformer architecture family and are widely used for tasks like sentiment analysis, text generation, question answering, and more.
But what’s the difference between them, and how do you actually use them for text processing?
Let’s break it down.
🔍 What is BERT?
BERT stands for Bidirectional Encoder Representations from Transformers.
Developed by Google
Pre-trained on massive datasets like Wikipedia
Reads text in both directions (left to right and right to left)
Best suited for understanding tasks like:
Text classification
Named Entity Recognition (NER)
Question answering
Semantic search
📘 Example use case:
Given the question: “Where is the Eiffel Tower?”, BERT can find the correct answer from a paragraph.
🤖 What is GPT?
GPT stands for Generative Pre-trained Transformer.
Developed by OpenAI
Pre-trained to predict the next word in a sentence (left to right)
Best suited for text generation tasks, such as:
Chatbots
Story writing
Summarization
Code generation
📘 Example use case:
You type “Once upon a time,” and GPT continues the story.
⚖️ BERT vs GPT at a Glance
Feature BERT GPT
Architecture Encoder-only Decoder-only
Direction Bidirectional Unidirectional (left to right)
Best for Understanding tasks Generative tasks
Pre-training task Masked Language Modeling (MLM) Next-Token Prediction
🛠️ How to Use BERT and GPT for Text Processing
Both models are available through the Hugging Face Transformers library, which makes it easy to get started.
✅ Setup
First, install the required libraries:
bash
Copy
Edit
pip install transformers
pip install torch
🧠 Using BERT for Text Classification
python
Copy
Edit
from transformers import BertTokenizer, BertForSequenceClassification
import torch
# Load pre-trained BERT
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
# Example input
text = "This movie was amazing!"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
# Get predictions
outputs = model(**inputs)
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1)
print("Predicted class:", predicted_class.item())
🤖 Using GPT for Text Generation
python
Copy
Edit
from transformers import GPT2LMHeadModel, GPT2Tokenizer
# Load pre-trained GPT-2
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")
# Example prompt
prompt = "The future of artificial intelligence is"
inputs = tokenizer(prompt, return_tensors="pt")
# Generate text
outputs = model.generate(inputs['input_ids'], max_length=50, do_sample=True)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
🎯 Common Use Cases
Task Recommended Model
Sentiment Analysis BERT
Text Classification BERT
Question Answering BERT
Named Entity Recognition BERT
Text Summarization GPT / BART / T5
Email Writing GPT
Story Generation GPT
Chatbots GPT
⚠️ Tips for Better Results
Use fine-tuning on your specific dataset for better accuracy.
Be mindful of token limits (BERT usually supports 512 tokens, GPT varies by model size).
For production, consider using distilled versions like DistilBERT or GPT-Neo for faster performance.
🚀 Conclusion
BERT and GPT have become essential tools in modern NLP workflows.
Use BERT when your goal is to understand text.
Use GPT when your goal is to generate text.
Thanks to libraries like Hugging Face, integrating these models into your own projects is easier than ever — no PhD required.
Learn Data Science Course in Hyderabad
Read More
Named Entity Recognition (NER) Explained
Sentiment Analysis with NLP: How It Works
How Chatbots Work: The Power of NLP
Introduction to NLP: How Machines Understand Language
Visit Our Quality Thought Training Institute in Hyderabad
Comments
Post a Comment