BERT
| swMATH ID: | 30756 |
| Software Authors: | Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina |
| Description: | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. 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. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 |
| Homepage: | https://arxiv.org/abs/1810.04805 |
| Source Code: |
 https://github.com/google-research/bert
|
| Related Software: | Tensor2Tensor; Adam; RoBERTa; word2vec; ImageNet; PyTorch; GloVe; AlexNet; GPT-3; Python; GitHub; Transformers; XLNet; TensorFlow; SQuAD; ViT; ALBERT; BLEU; Sentence-BERT; Scikit |
| Cited in: | 221 Documents |
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