Transformers in NLP (BERT, GPT)

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Certainly! Here’s a comprehensive breakdown of Transformers in NLP, focusing on two of the most well-known models that revolutionized natural language processing: BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer).

🧠 Transformers in NLP: BERT & GPT

🎯 What is a Transformer?

The Transformer model is a deep learning architecture introduced in the paper “Attention is All You Need” (Vaswani et al., 2017) that revolutionized natural language processing (NLP). Unlike previous architectures like RNNs and LSTMs, transformers rely solely on self-attention mechanisms to process sequences of data, making them highly parallelizable and effective at capturing long-range dependencies.

Transformers are the backbone of many state-of-the-art NLP models, including BERT and GPT.

🔑 Key Concepts in Transformer Models:

• Self-Attention: The model processes all words (or tokens) in a sentence simultaneously, with each word attending to every other word, allowing it to capture long-range dependencies.
• Multi-Head Attention: The attention mechanism is computed multiple times in parallel with different learned projections, allowing the model to focus on different parts of the sentence or different aspects of the relationship between words.
• Positional Encoding: Since transformers don’t process data sequentially like RNNs, they require positional encodings to understand the order of words in a sequence.
• Feedforward Neural Networks: After the attention layers, transformers use fully connected feedforward layers to refine the representations.
• Layer Normalization & Residual Connections: These help with model stability and gradient flow during training.

🧩 BERT (Bidirectional Encoder Representations from Transformers)

BERT is a transformer-based model designed by Google, introduced in 2018. It significantly improved performance on many NLP tasks by capturing bidirectional context, as opposed to unidirectional models.

🎯 Key Features of BERT:

1. Bidirectional Attention:
   • Traditional language models process text in one direction (either left-to-right or right-to-left). BERT utilizes bidirectional transformers, meaning it considers both the left and right context of a word simultaneously. This allows it to understand context more effectively.
2. Pre-training + Fine-tuning:
   • Pre-training: BERT is first pre-trained on a large corpus of text (e.g., Wikipedia) using two unsupervised objectives:
     • Masked Language Modeling (MLM): Randomly masks some of the words in a sentence and trains the model to predict those words based on the context of the surrounding words.
     • Next Sentence Prediction (NSP): Trains the model to understand the relationship between two sentences (e.g., whether sentence B logically follows sentence A).
   • Fine-tuning: After pre-training, BERT is fine-tuned on specific tasks (like classification, question answering, etc.) using labeled data.
3. Transformer Encoder Architecture:
   • BERT uses only the encoder part of the transformer architecture (as opposed to the full transformer which also includes a decoder). This makes BERT well-suited for tasks that require understanding the context and meaning of a sentence (like classification and extraction tasks).

🧩 BERT Architecture:

1. Input Representation:
   • BERT uses a combination of WordPiece embeddings for words, segment embeddings for distinguishing sentences, and positional embeddings to represent word positions in the sequence.
2. Masked Language Modeling (MLM):
   • During pre-training, BERT randomly masks 15% of the words in the input sequence and trains the model to predict those masked words based on the context from both directions.
3. Next Sentence Prediction (NSP):
   • For the NSP task, BERT is given pairs of sentences, and the model is trained to predict if the second sentence follows the first one logically.
4. Layers:
   • BERT comes in different sizes: BERT-Base (12 layers) and BERT-Large (24 layers). These layers consist of self-attention and feedforward networks to process the input sequence.

🚀 Applications of BERT:

• Question Answering: BERT can be fine-tuned for QA tasks like SQuAD (Stanford Question Answering Dataset), where the model reads a passage and answers questions based on the content.
• Sentiment Analysis: Fine-tuned BERT can classify the sentiment of text (positive, negative, neutral).
• Named Entity Recognition (NER): BERT is used for extracting information like names, organizations, dates, etc.
• Text Classification: BERT can be used for various classification tasks, including spam detection or topic classification.

🧩 GPT (Generative Pre-trained Transformer)

GPT, developed by OpenAI, is another transformer-based model but with a different focus. Unlike BERT, GPT is a unidirectional model, meaning it processes text from left to right. This makes it well-suited for generative tasks, where the goal is to predict the next word in a sequence, such as text generation, completion, or summarization.

🎯 Key Features of GPT:

1. Autoregressive (Unidirectional) Model:
   • GPT generates text by predicting the next word in the sequence, conditioned on the words that came before it (left-to-right).
2. Pre-training + Fine-tuning:
   • Similar to BERT, GPT is pre-trained on large text corpora, but it uses the causal language modeling objective (predicting the next word given previous words) instead of MLM.
   • After pre-training, GPT is fine-tuned on specific downstream tasks (e.g., text generation, summarization).
3. Transformer Decoder Architecture:
   • GPT uses only the decoder part of the transformer architecture. This makes it well-suited for generative tasks, where the model needs to generate a sequence based on some initial input.

🧩 GPT Architecture:

1. Input Representation:
   • The input to GPT is tokenized text, and each token is represented by a token embedding. The model also uses positional encodings to capture the order of words in the sequence.
2. Autoregressive Pre-training:
   • GPT is trained to predict the next word in a sequence, given the context of all previous words. The training objective is to maximize the likelihood of the next word in the sequence, conditioned on the previous words.
3. Decoding Process:
   • During inference, GPT generates text by autoregressively predicting the next word, appending it to the sequence, and then predicting the next word based on the updated sequence.

🚀 Applications of GPT:

• Text Generation: GPT is famous for generating coherent and contextually relevant text, which can be used for creative writing, dialogue generation, and more.
• Text Completion: Given a prompt, GPT can predict and complete the rest of the text, making it useful for writing assistants and content creation tools.
• Summarization: GPT can be fine-tuned for summarization tasks, condensing long documents into concise summaries.
• Translation: GPT can be fine-tuned for machine translation tasks.
• Conversational Agents: GPT powers many chatbots and virtual assistants by generating human-like responses in conversations.

🧩 BERT vs. GPT

🧠 Transformer Variants (GPT-2, GPT-3, T5, RoBERTa, etc.)

1. GPT-2: An improved version of GPT that significantly scaled up the model size and showed even more powerful text generation capabilities.
2. GPT-3: One of the largest language models to date, with 175 billion parameters, capable of performing a wide range of tasks with little to no fine-tuning (zero-shot learning).
3. RoBERTa: A variant of BERT that removes the NSP objective and is trained with more data and larger batches.
4. T5 (Text-to-Text Transfer Transformer): A transformer model that frames all NLP tasks as a text-to-text problem, where both the input and output are sequences of text.

🚀 Next Steps:

• Code Examples: Want to see how BERT or GPT can be implemented and fine-tuned for specific NLP tasks?
• Advanced Topics: Interested in understanding the scaling laws behind GPT-3 or learning more about model optimization for these transformers?
• Applications: Let me know if you’d like to explore specific applications, such as chatbot creation or creative text generation.

Feel free to ask if you’d like further details or examples!