Understanding the Transformer Architecture in NLP
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Chapter 1: An Overview of Transformers
The landscape of Natural Language Processing (NLP) has undergone a transformation with the introduction of Transformers, which have outperformed traditional models like Recurrent Neural Networks (RNNs).
Transformers, introduced by Vaswani et al. in their landmark 2017 paper "Attention is All You Need," have changed the game by utilizing self-attention mechanisms. This innovation enables the model to comprehend the context and significance of each word within a sentence. Unlike RNNs, which process data sequentially, Transformers analyze all words in a sentence simultaneously. This parallel processing ability enhances the model's understanding of the relationships between words, effectively addressing challenges related to long-term dependencies and computational efficiency that plagued RNNs.
Let's delve deeper into the architecture step by step.
Section 1.1: Components of Transformer Architecture
The Transformer Architecture comprises two main components: the Encoder and the Decoder. These elements work collaboratively and share several characteristics.
- Encoder: This component transforms an input sequence of tokens into a detailed, continuous representation that encapsulates the context of each token. The output is a series of embedding vectors, referred to as the hidden state or context.
- Decoder: The Decoder utilizes the encoder's hidden state to sequentially generate an output token by token.
While both the Encoder and the Decoder are integral to the Transformer Architecture, there are three variations of Transformers based on their configuration: encoder-only, decoder-only, and encoder-decoder models.
Subsection 1.1.1: Encoder-Only Transformers
These models function as adept analysts, capable of comprehending and interpreting textual data deeply. They convert an input text sequence into a sophisticated numerical representation, making them ideal for tasks such as text classification and named entity recognition (NER). BERT and its derivatives, including RoBERTa and DistilBERT, fall under this category. These models employ bidirectional attention, allowing them to consider the entire context surrounding a word, both preceding and following it.
Subsection 1.1.2: Decoder-Only Transformers
Consider these models as imaginative storytellers who continue a narrative based on a given prompt. For instance, if prompted with "Learning transformers is…," these models will predict the next word iteratively, ideally completing the sentence with "fun." The GPT family of models exemplifies this category, where the representation of each token depends solely on the left context, following an autoregressive attention mechanism.
Subsection 1.1.3: Encoder-Decoder Transformers
These models are versatile multitaskers, adept at transforming text from one format to another. They first interpret the input text through the encoder, capturing its essence, and then the decoder generates a new text piece in response. This design is particularly suited for tasks like machine translation and summarization, with models like T5 and BART fitting into this classification.
Chapter 2: The Process of Tokenization
In this section, we will focus on the encoder-decoder transformer, using a machine translation task from Greek to English as our example.
- Tokenizer: The initial step in processing text with a model is tokenization, which converts words into numerical representations. Each unique token is assigned a specific number. It’s essential to use the same tokenizer for both training and text generation.
- Embedding Layer: The embedding layer converts the tokenized numerical representations into dense vector embeddings. Each token is represented in a high-dimensional space, allowing the model to capture semantic meanings. Positional embeddings are also added to convey the order of words, which is crucial for understanding text.
Section 2.1: Exploring the Encoder
It's crucial to note that the transformer consists of multiple identical encoders stacked together. For instance, BERT utilizes a stack of 24 encoders. The encoder receives the sequence of embeddings from the embedding layer, which are processed through a multi-head self-attention layer followed by a fully connected feed-forward layer.
- Multi-Head Self-Attention: This layer's role is to analyze each word in the context of other words in the sentence. The term "multi-head" indicates that the model examines relationships from various perspectives, enhancing its understanding of the text.
- Feed-Forward Layer: This consists of a two-layer fully connected neural network, processing each embedding individually and outputting transformed embeddings. An activation function, specifically GELU, is employed to introduce non-linearity, which is beneficial for natural language data distributions.
Section 2.2: Delving into the Decoder
Similar to the encoder, the decoder comprises a stack of identical layers. A notable distinction is that the decoder includes two attention sublayers: masked multi-head self-attention and encoder-decoder attention.
- Masked Multi-Head Self-Attention: This ensures the tokens generated at each step rely solely on past outputs and the current token being predicted, preventing the model from "cheating" during training.
- Encoder-Decoder Attention: This layer allows the decoder to focus on relevant parts of the input sequence while generating output tokens, considering both the current context and previously generated tokens.
The output from the decoder is a probability score for each token in the tokenizer's dictionary, with the highest probability token being selected as the output.
The first video titled "Introduction to Transformer Architecture and Discussion" provides an insightful overview of the fundamental concepts behind transformers in NLP.
The second video, "Illustrated Guide to Transformers Neural Network: A Step-by-Step Explanation," offers a detailed visual guide to understanding the transformer architecture.
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