In the context of transformer models, especially in natural language processing (NLP), the three main types of transformers are:
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Encoder-only Transformers:
- These transformers are designed to process input sequences and generate context-aware representations of the input tokens.
- They are commonly used for tasks such as text classification, named entity recognition, and other tasks that require understanding of the input sequence as a whole.
- Example models: BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (A Robustly Optimized BERT Pretraining Approach).
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Decoder-only Transformers:
- These transformers are used for tasks that involve generating sequences, such as text generation and auto-regressive language modeling.
- They process input tokens and generate output tokens one by one, predicting the next token in the sequence based on the previously generated tokens.
- Example models: GPT (Generative Pre-trained Transformer), GPT-2, GPT-3.
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Encoder-Decoder (Sequence-to-Sequence) Transformers:
- These transformers consist of both an encoder and a decoder, making them suitable for tasks that involve transforming an input sequence into an output sequence, such as machine translation, text summarization, and other sequence-to-sequence tasks.
- The encoder processes the input sequence to create a context-aware representation, and the decoder generates the output sequence based on this representation.
- Example models: T5 (Text-to-Text Transfer Transformer), BART (Bidirectional and Auto-Regressive Transformers).
These types leverage the transformer architecture in different ways to address various NLP tasks effectively.
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