PELlaMs

PELlaMs, short for "Pretrained Embedding Language Models," are a class of machine learning models designed to understand and generate human language. They are built on the transformer architecture, which allows them to process and generate text in a way that captures the context and meaning of words in a sentence. PELlaMs are trained on large datasets of text, which enables them to learn the statistical patterns and structures of language. This training process involves unsupervised learning, where the model learns to predict the next word in a sentence or to fill in missing words in a sentence. Once trained, PELlaMs can be fine-tuned on specific tasks, such as translation, summarization, or question answering, to improve their performance on those tasks. PELlaMs have been shown to be highly effective in a wide range of natural language processing tasks, and they are widely used in research and industry. Some of the most well-known PELlaMs include BERT, RoBERTa, and T5.