Transformers Enable Large-Scale Pretrained Language Models

The architecture allows models like GPT, BERT, and T5 to scale to billions of parameters efficiently.

Home › Artificial Intelligence Breakthroughs › Transformer Model › Transformers Enable Large-Scale Pretrained Language Models
← Back to Artificial Intelligence Breakthroughs ← Back to Transformer Model
Top Ad Slot
🤯 Did You Know (click to read)

GPT-3, based on a Transformer decoder, contains 175 billion parameters, making it one of the largest NLP models.

Transformers’ parallelizable design supports pretraining on massive text corpora. Attention mechanisms capture global dependencies, enabling learning of contextualized embeddings. Pretrained models can be fine-tuned for downstream tasks, facilitating transfer learning and reducing task-specific data requirements.

Mid-Content Ad Slot
💥 Impact (click to read)

Large-scale pretraining with Transformers accelerates NLP research and enables high-performance models across translation, summarization, and dialogue.

For developers, pretrained Transformer models reduce compute cost and training time, allowing application to specialized domains.

Source

Brown et al., 2020 - GPT-3

LinkedIn Reddit
Copy Link

⚡ Ready for another mind-blower?

‹ Previous Next ›

💬 Comments