Fine-tuning applications

Fine-tuning can be applied to a wide range of natural language processing tasks, including the following:

• Text classification: This involves classifying text into predefined categories by examining its content or context. For example, in sentiment analysis of customer reviews, we can classify text as positive, negative, or neutral.
• Token classification: This involves labeling words in a piece of text, often to spot names or specific entities. For example, when applying named entity recognition to text, we can identify people, cities, and more.
• Question-answering: This involves providing effective answers to questions in natural language.
• Summarization: This involves providing concise summaries of long texts – for example, summarizing a news article.
• Language translation: This involves converting text from one language into another. An example of this is translating a document from English into Spanish.

The aforementioned...