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{"summary": "This post provides a catalog and classification of the most popular Transformer models.", "bulk": "- Transformer: The original Transformer model introduced in the \"Attention is All You Need\" paper.\n- BERT: Bidirectional Encoder Representations from Transformers, a pre-trained model for natural language processing tasks.\n- GPT: Generative Pre-trained Transformer, a model that uses unsupervised learning to generate text.\n- T5: Text-to-Text Transfer Transformer, a model that can perform a wide range of language tasks.\n- XLNet: A generalized autoregressive pretraining method that outperforms BERT on several benchmarks.\n- RoBERTa: A robustly optimized version of BERT that achieves state-of-the-art performance on multiple tasks.\n- ALBERT: A Lite BERT model that reduces memory consumption and training time while maintaining performance.\n- ELECTRA: A model that uses a generator and discriminator to efficiently pre-train and fine-tune.\n- GPT-3: A highly advanced model with 175 billion parameters, capable of performing various language tasks."}