What are transformers in AI? Explain Encoder only (BERT), Decoder only (GPT) and Encoder and Decoder Based (T5) transformers.

Understanding Transformers in AI: Encoder-Only, Decoder-Only, and Encoder-Decoder Architectures

Transformers have revolutionized the field of artificial intelligence (AI), particularly in natural language processing (NLP). Introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017, transformers have become the backbone of modern AI models like BERT, GPT, and T5. In this blog post, we’ll explore what transformers are, how they work, and the differences between encoder-only, decoder-only, and encoder-decoder architectures.

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What Are Transformers?

Transformers are a type of neural network architecture designed to handle sequential data, such as text, without relying on recurrent layers (e.g., RNNs or LSTMs). Instead, they use a mechanism called self-attention to process input data in parallel, making them faster and more efficient for large-scale tasks.

Key Components of Transformers:

1. Self-Attention Mechanism: Weighs the importance of different words in a sentence relative to each other.

2. Positional Encoding: Adds information about the position of words in a sequence since transformers don’t process data sequentially.

3. Feed-Forward Neural Networks: Used to transform the output of the attention layers.

4. Encoder and Decoder Blocks: The building blocks of transformer architectures.

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Types of Transformer Architectures

Transformers can be categorized into three main types based on their architecture:

1. Encoder-Only Models (e.g., BERT)

2. Decoder-Only Models (e.g., GPT)

3. Encoder-Decoder Models (e.g., T5)

Let’s dive into each of these architectures.

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1. Encoder-Only Models (e.g., BERT)

Encoder-only models focus on understanding and representing input data. They are primarily used for tasks that require understanding context, such as text classification, named entity recognition, and question answering.

Key Features:

• Bidirectional Context: Encoder-only models process input data in both directions (left-to-right and right-to-left), allowing them to capture context from the entire sentence.

• No Generation: These models are not designed to generate text but rather to extract meaningful representations of the input.

Example: BERT (Bidirectional Encoder Representations from Transformers)

BERT is a popular encoder-only model that uses masked language modeling (MLM) to predict missing words in a sentence.

How BERT Works:

1. Input Representation: BERT takes a sequence of tokens as input and adds positional embeddings.

2. Masked Language Modeling (MLM): Randomly masks some tokens in the input and predicts them based on the surrounding context.

3. Output: Produces contextualized embeddings for each token.

Code Example:

python

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from transformers import BertTokenizer, BertForMaskedLM
import torch

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

# Input sentence with a masked token
input_text = "The quick brown [MASK] jumps over the lazy dog."

# Tokenize input
inputs = tokenizer(input_text, return_tensors='pt')

# Predict the masked token
with torch.no_grad():
    outputs = model(**inputs).logits

# Decode the predicted token
predicted_token = torch.argmax(outputs[0, inputs['input_ids'][0] == tokenizer.mask_token_id])
print("Predicted Token:", tokenizer.decode(predicted_token))

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2. Decoder-Only Models (e.g., GPT)

Decoder-only models are designed for text generation tasks. They process input data in a unidirectional manner (left-to-right) and are trained to predict the next word in a sequence.

Key Features:

• Unidirectional Context: These models only consider previous tokens when generating the next token.

• Autoregressive Generation: They generate text one token at a time, using previously generated tokens as input.

Example: GPT (Generative Pre-trained Transformer)

GPT is a decoder-only model that uses autoregressive language modeling to generate coherent and contextually relevant text.

How GPT Works:

1. Input Representation: GPT takes a sequence of tokens as input and adds positional embeddings.

2. Autoregressive Modeling: Predicts the next token in the sequence based on the previous tokens.

3. Output: Generates text one token at a time.

Code Example:

python

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from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Load pre-trained GPT-2 model and tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Input prompt
input_text = "Once upon a time"

# Tokenize input
inputs = tokenizer(input_text, return_tensors='pt')

# Generate text
output = model.generate(inputs['input_ids'], max_length=50, num_return_sequences=1)

# Decode the generated text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print("Generated Text:", generated_text)

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3. Encoder-Decoder Models (e.g., T5)

Encoder-decoder models combine both encoder and decoder components, making them suitable for sequence-to-sequence tasks like translation, summarization, and text generation.

Key Features:

• Bidirectional Encoding: The encoder processes the input sequence bidirectionally.

• Autoregressive Decoding: The decoder generates the output sequence one token at a time.

• Versatility: Can handle a wide range of tasks by framing them as text-to-text problems.

Example: T5 (Text-to-Text Transfer Transformer)

T5 is an encoder-decoder model that treats every NLP task as a text-to-text problem, where both the input and output are text strings.

How T5 Works:

1. Input Representation: The encoder processes the input text bidirectionally.

2. Task-Specific Prefix: A task-specific prefix (e.g., "translate English to French:") is added to the input.

3. Output Generation: The decoder generates the output text autoregressively.

Code Example:

python

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from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load pre-trained T5 model and tokenizer
tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = T5ForConditionalGeneration.from_pretrained('t5-small')

# Input text and task prefix
input_text = "translate English to French: The house is wonderful."

# Tokenize input
inputs = tokenizer(input_text, return_tensors='pt')

# Generate translation
output = model.generate(inputs['input_ids'], max_length=50)

# Decode the output
translated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print("Translated Text:", translated_text)

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Comparison of Transformer Architectures

Feature	Encoder-Only (BERT)	Decoder-Only (GPT)	Encoder-Decoder (T5)
Context	Bidirectional	Unidirectional	Bidirectional (Encoder)
Primary Use Case	Understanding context	Text generation	Sequence-to-sequence tasks
Example Tasks	Text classification, Q&A	Text generation, completion	Translation, summarization
Training Objective	Masked language modeling	Autoregressive modeling	Text-to-text generation

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Conclusion

Transformers have transformed the field of AI, enabling state-of-the-art performance across a wide range of NLP tasks. Whether you’re working with encoder-only models like BERT for understanding context, decoder-only models like GPT for text generation, or encoder-decoder models like T5 for sequence-to-sequence tasks, transformers provide a powerful and flexible framework for building AI systems.

By understanding the differences between these architectures, you can choose the right model for your specific use case and leverage the full potential of transformers in your projects.

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References:

• Attention is All You Need (Paper)

• BERT: Pre-training of Deep Bidirectional Transformers

• GPT-3: Language Models are Few-Shot Learners

• T5: Exploring the Limits of Transfer Learning