A pre-trained language model is an NLP model that has been trained on large corpora of text to learn general language patterns, such as grammar, syntax, and semantic relationships. These models serve as a foundation for building task-specific applications, reducing the need to train models from scratch. Examples include BERT, GPT, and RoBERTa.
Pre-training tasks typically include language modeling (predicting the next word in a sequence) or masked language modeling (predicting masked words in a sentence). For instance, a BERT model might learn to fill in the blank in "The cat ___ on the mat" by predicting "sat." This training enables the model to understand context, word relationships, and even some world knowledge.
Once pre-trained, these models can be fine-tuned on smaller datasets for tasks like sentiment analysis, question answering, or named entity recognition. Pre-trained models have become a cornerstone of NLP due to their efficiency, scalability, and performance, with libraries like Hugging Face Transformers making them accessible to developers.