Pre-trained language model

A pre-trained language model is a type of language model that has been trained on a large dataset of text in order to learn the structure and rules of language. The pre-training step typically involves training the language model on a large corpus of text using a task such as predicting the next word in a sentence. Once the language model is trained, it can be fine-tuned for a specific task, such as sentiment analysis or text classification, using a smaller dataset.

History

Pre-trained language models have become increasingly popular in natural language processing (NLP) thanks to advances in deep learning techniques and the availability of large amounts of text data. They have been shown to achieve state-of-the-art performance on a wide range of NLP tasks, including text classification, question-answering, machine translation, and conversational agents.

Examples

Some examples of pre-trained language models include BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), and RoBERTa (Robustly Optimized BERT Pretraining Approach). These models have been pre-trained on massive amounts of text and have achieved impressive results on a range of NLP tasks.