Training Process of Generative Pre-trained Transformer
Large-scale text data corpora are used for unsupervised learning to train GPT algorithms. There are two primary stages to the training:
- Pre-training: Known as language modeling, this stage teaches the model to anticipate the word that will come next in a sentence. In order to make that the model can produce writing that is human-like in a variety of settings and domains, this phase makes use of a wide variety of internet material.
- Fine-tuning: While GPT models perform well in zero-shot and few-shot learning, fine-tuning is occasionally necessary for particular applications. In order to improve the model’s performance, this entails training it on data specific to a given domain or task.
Introduction to Generative Pre-trained Transformer (GPT)
The Generative Pre-trained Transformer (GPT) is a model, developed by Open AI to understand and generate human-like text. GPT has revolutionized how machines interact with human language, enabling more intuitive and meaningful communication between humans and computers. In this article, we are going to explore more about Generative Pre-trained Transformer.
Table of Content
- What is a Generative Pre-trained Transformer?
- Background and Development of GPT
- Architecture of Generative Pre-trained Transformer
- Training Process of Generative Pre-trained Transformer
- Applications of Generative Pre-trained Transformer
- Advantages of GPT
- Ethical Considerations
- Conclusion
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