Transformers are a type of deep learning model designed for natural language processing (NLP) tasks. They were introduced in the paper "Attention Is All You Need" by Vaswani et al. in 2017 and have since become a foundational architecture in the field of NLP.
The term "Transformers" actually refers to the architecture itself, which relies heavily on a mechanism called "self-attention." Self-attention allows the model to weigh the importance of different words in a sentence relative to each other, enabling it to capture contextual relationships and dependencies between words.
The key components of a Transformer architecture include:
Self-Attention Mechanism: This mechanism enables the model to weigh the relevance of each word in a sequence with respect to all other words, capturing long-range dependencies.
Multi-Head Attention: In order to capture different aspects of context, the self-attention mechanism is often used in multiple "heads" (parallel instances), allowing the model to focus on different parts of the input sequence.
Positional Encodings: Since Transformers don't inherently understand the sequential order of words, positional encodings are added to the input embeddings to provide information about the position of each word in the sequence.
Feedforward Neural Networks: After applying self-attention and combining information from different heads, the model typically employs feedforward neural networks to perform further processing and transformation of the representations.
Encoder and Decoder Stacks: Transformers are often used in a two-part architecture known as the "encoder-decoder" framework. In tasks like machine translation, the encoder processes the input sentence, while the decoder generates the output sentence. The decoder's self-attention layer also incorporates an additional "masked" attention mechanism to prevent future positions from being attended to.
Transformers have achieved state-of-the-art results in a wide range of NLP tasks, including machine translation, text generation, sentiment analysis, question answering, and more. Variants and improvements on the original architecture, such as BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), and RoBERTa, have further advanced the capabilities of these models.