The Transformer is a neural network architecture that has revolutionized natural language processing (NLP) tasks. Introduced in the "Attention is All You Need" paper in 2017, Transformers excel at capturing long-range dependencies in sequences, enabling them to outperform traditional models.
Transformers are composed of two primary components: an encoder and a decoder.
Both the encoder and decoder employ self-attention mechanisms, but with key differences.
Attention is the core mechanism that allows transformers to effectively capture relationships between tokens in a sequence. It works by assigning weights to different tokens, indicating their importance or relevance to the current token being processed.
This attention mechanism allows transformers to focus on relevant parts of the input sequence, leading to more accurate and meaningful representations.
Word embedding is a fundamental concept in NLP, enabling words or phrases to be represented as dense vectors in a high-dimensional space.
Transformers utilize word embeddings as the input to the encoder, allowing the model to benefit from the semantic and contextual information encoded in these vectors.
Transformers emerged as a powerful alternative to recurrent neural networks (RNNs), overcoming several challenges faced by RNNs, particularly in handling long sequences.
Positional encoding is crucial for transformers to understand the order of tokens in a sequence. It's calculated using sinusoidal functions and added to the input embeddings.
This encoding ensures that the model can differentiate between tokens based on their positions, enhancing its ability to capture sequential relationships.
The encoder-decoder architecture is a common framework in NLP for sequence-to-sequence tasks, where an input sequence is transformed into an output sequence.
Transformers have effectively leveraged the encoder-decoder architecture, achieving state-of-the-art results in various NLP tasks such as machine translation, text summarization, and speech recognition.
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