Understanding the needs of NLP applications
NLP applications are designed to enable machines to understand and interpret human language in a valuable way. Let’s look at some of the core needs that these applications typically aim to address.
Computational efficiency
Computational efficiency is a critical factor in the development and deployment of NLP applications due to the following reasons:
- Large datasets: NLP models are typically trained on vast amounts of data. Efficiently handling and processing these datasets is essential for training models within a reasonable timeframe and without prohibitive costs.
- Complex models: State-of-the-art NLP models, such as Transformers, involve millions or even billions of parameters. Managing such complexity requires substantial computational power and efficient algorithms.
- Real-time processing: Many NLP applications, such as virtual assistants, translation services, and chatbots, need to process language data in real...
