Natural Language Processing (NLP)
Natural Language Processing (NLP) is a branch of artificial intelligence that enables machines to understand, interpret, and manipulate human language. NLP algorithms analyze natural language text or speech and generate structured data in the form of ontologies, taxonomies, topic models, and other semantic representations. With these capabilities, machines can interact naturally with humans by understanding commands as well as providing feedback about their progress and results. This technology has been widely applied to many different areas including search engines, question-answering systems, customer service applications, machine translation services, automated call centers, and more. By leveraging the power of NLP algorithms, businesses are able to increase operational efficiency while improving customer satisfaction. The potential for NLP is vast and its application will continue to revolutionize the way we interact with machines. With NLP, businesses can extract valuable insights from text-based data and automate tedious tasks for improved productivity and customer experience.
Natural language processing (NLP) models are an essential part of modern machine learning and artificial intelligence. Some of the most popular NLP models include BERT, GPT, and T5. BERT stands for Bidirectional Encoder Representations from Transformers and is a model used to understand natural language queries. GPT or Generative Pre-trained Transformer is a deep learning model that can generate text using predictive coding techniques. Finally, T5 or Text-To-Text Transfer Transformer is a large scale transformer model used for text generation tasks such as summarization and question answering. Each of these prominent NLP models serve different purposes in the field of machine learning and AI, making them vital tools for data scientists today.
Cerebras makes the benefits of massive-scale NLP available to everyone. Our solutions combine best-in-industry performance with push button scaling, to make running the largest models simple and straightforward. Avoid the pain of distributed compute, run long sequence lengths up to 50,000 tokens, harvest structured and unstructured sparsity, and do so on-premises or in the cloud.
