Language model

A language model machine learning model is designed to understand and generate human language. It is trained on large amounts of text data and learns to classify the text into different categories or generate new text that is similar in style and content to the original data.

Various tasks can be performed using language models, including:

• Language translation: translating text from one language to another.
• Text generation: generating new text that is similar in style and content to the original data.
• Sentiment analysis: analyzing the sentiment or emotion conveyed in a piece of text.
• Question answering: responding to questions based on a given piece of text.

Language models can be used in a variety of applications, such as chatbots, virtual assistants, customer service, document summarization, and language translation. For example, language models can be used to generate natural-sounding responses in chatbots, or to translate text from one language to another in real-time.

In recent years, large pre-trained language models such as GPT-3 and GPT-4 have emerged as a powerful tool for natural language processing and generation. These models have been used to generate news articles, poetry, and even code.

Language models can also be monitored on VIANOPS. For example, you can extract embedded features from a neural network and send them to VIANOPS together with other raw features like word count, source, and model output to monitor feature drift and prediction drift. If the task of the language model is classification, then you can also monitor the model performance over time.

Go to the Monitor your model How-To guide to learn more details about how to set up monitoring jobs with policies and view monitoring dashboards.