Perplexity is a measure used in natural language processing and machine learning to evaluate the performance of language models. It measures how well the model predicts the next word or character based on the context provided by the previous words or characters. The lower the perplexity score, the better the model’s ability to predict the next word or character.

• Language Modeling
• Speech Recognition
• Chatbot Development
• Information Retrieval
• Machine Translation
• Text Summarization
• Text Generation
• Text Classification

For instance, a language model trained on a large corpus of text data may have a perplexity score of 10 when asked to predict the next word in a sentence. If the model is able to accurately predict the next word with high probability, its perplexity score will be lower than if it struggled to make accurate predictions.

• Designing the semantic data model