transformer models
Transformer models are a type of deep learning architecture that use self-attention mechanisms to process sequential data without relying on predefined positions, making them effective in understanding and generating natural language, as well as other sequential data, by capturing long-range dependencies.
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Related Concepts (22)
- albert (a lite bert)
- attention layers
- attention mechanism
- bert (bidirectional encoder representations from transformers)
- bidirectional transformers
- computational linguistics with transformer models
- electra (efficiently learning an encoder that classifies token replacements accurately)
- encoder-decoder architecture
- gpt (generative pre-trained transformers)
- gpt-3 (generative pre-trained transformer 3)
- graph transformer networks
- image captioning using transformers
- large language model
- masked language modeling
- named entity recognition using transformers
- recommender systems using transformers
- roberta (robustly optimized bert approach)
- self-attention
- speech recognition using transformer models
- t5 (text-to-text transfer transformer)
- transformer-xl
- xlnet (extreme learning network)
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