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This chapter introduces bidirectional transformer encoders and masked language modeling, which allow language models to see the entire context of text when performing tasks like named entity recognition and question answering. It discusses how these models are pretrained using techniques like masked language modeling and then fine-tuned on downstream tasks via transfer learning. Contextual embeddings are also introduced, where each word is represented differently depending on its context in the text.

Author

cagla.cengiz

Language

EN