Harnessing GPT-2: A Deep Dive into Inference
Introduction
OpenAI’s GPT-2 has been a significant breakthrough in the field of Natural Language Processing (NLP), enabling the development of applications that can understand and generate human-like text. This model has been a subject of fascination due to its ability to produce text that is not only coherent but also contextually relevant, making it a valuable tool for developers and researchers alike. This article aims to provide a deeper understanding and practical guide on leveraging GPT-2 for making inferences, allowing users to explore its vast potential in various applications.
Understanding Inference
Inference is the phase where the trained model is utilised to make predictions or generate new, unseen data. In the context of GPT-2, a generative language model, inference involves providing a seed text or prompt and having the model generate coherent and contextually relevant continuations or completions of the given text. This process leverages the patterns and relationships the model has learned during training to produce plausible outputs.
How Inference Works in GPT-2:
- Tokenization: The input text or prompt is tokenized into a sequence of tokens (words or subwords) that the model can understand.
