def forward(self, input_ids): embeddings = self.embedding(input_ids) outputs = self.transformer(embeddings) outputs = self.fc(outputs) return outputs
Large language models are a type of neural network designed to process and understand human language. They are trained on vast amounts of text data, which enables them to learn patterns, relationships, and structures within language. This training allows LLMs to generate coherent and context-specific text, making them useful for a wide range of applications. Build A Large Language Model -from Scratch- Pdf -2021
The most notable examples of LLMs include BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERT Pretraining Approach), and XLNet (Extreme Language Modeling). These models have achieved state-of-the-art results in various NLP tasks, such as language translation, sentiment analysis, and question-answering. def forward(self, input_ids): embeddings = self