“A transformer model is a neural network architecture that advances natural language processing (NLP) and other sequential data tasks.”
Unlike traditional recurrent neural networks (RNNs) or convolutional neural networks (CNNs), which process sequential data sequentially, transformers use a self-attention mechanism to capture relationships between different elements of the input sequence simultaneously. This self-attention mechanism allows the model to weigh the importance of each element based on its relevance to other elements in the sequence, enabling it to process long sentences or documents efficiently.
Transformers have 4 main parts:
- Tokenization: takes words, punctuation, prefixes, and suffixes and sends them to a known token from the library.
- Embedding: sends texts to a vector of numbers to group similar pieces of text together with corresponding vectors.
- Positional encoding: adding coordinates and sequences of predefined vectors to embedded vectors of words.
- Transformer block: predicting the next word in a sentence using neural networks with an attention component.
- Softmax: turns scores into probabilities for the next word.
Transformer models have shown remarkable success in various NLP tasks, such as machine translation, sentiment analysis, and text generation. They have enabled the development of state-of-the-art language models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer).
Their ability to handle long-range dependencies and parallelize computations has made them instrumental in achieving state-of-the-art performance in various NLP tasks. As technology advances and evolves, transformer models will continue driving innovation in AI and expanding their applications across different domains.