{"id":"src_m9bq0zi8111b","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","authorName":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","abstract":"We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.","tags":["natural language processing","transformers","pre-training","language representation"],"priceUSDC":"0.0001"}