Language models are machine learning models designed to represent the language domain, serving as a basis for various language-based tasks such as question answering and sentiment analysis. They learn through training on large datasets and can be fine-tuned for specific use cases, adapting to different domains like medicine or law by undergoing additional training steps. These models operate similarly to human agents reading through documents to extract information from them, but are much faster and can store a vast amount of information picked up during training. Language models can be used in various applications such as enhancing language understanding, generating answers, summarizing text, and extracting named entities, with some models being trained to memorize information and others focusing on learning the language's regularities. The Hugging Face model hub provides access to a wide range of pre-trained models that can be fine-tuned and adapted to specific use cases, making it easier for users to integrate language models into their NLP pipelines.