🤯 Did You Know (click to read)
GPT and BERT models, based on Transformers, leverage billions of parameters to encode contextualized word embeddings learned from large corpora.
Due to parallelization in attention layers, Transformers can be trained on massive datasets without sequential constraints. Pretrained models capture rich language representations, which can be fine-tuned for downstream tasks like sentiment analysis, summarization, or text generation. This approach revolutionized NLP by enabling transfer learning at scale.
💥 Impact (click to read)
Large-scale pretraining with Transformers improves accuracy across NLP benchmarks and reduces the need for large task-specific datasets.
Practitioners benefit from pretrained Transformer models as they can fine-tune for specialized tasks quickly, lowering computational cost and accelerating research.
Source
Devlin et al., 2018 - BERT: Pre-training of Deep Bidirectional Transformers
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