Span selection models希望能够用它的boundary tokens来构建关于span的定长表示。因而span两端的tokens的repre...
Span selection models希望能够用它的boundary tokens来构建关于span的定长表示。因而span两端的tokens的repre...
BERT-based models can be readily fine-tuned to normalize any kind of named entities.doi:10.2196/14830Li, FeiJin, YonghaoLiu, WeisongRawat, Bhanu Pratap SinghCai, PengshanYu, HongJournal of Medical Internet Research
BERT-based models had already been successfully applied to the fake news detection task. For example, the work presented by Jwa et al.30had used it to a significant effect. The proposed model, exBAKE, applied BERT for the first time in fake news detection using a headline-body dataset. BE...
Transefomer-based 的预处理模型往往很消耗资源,对运算性能要求极高,还有严格的延迟需求。 潜在补救方法:模型压缩。 这篇文章主要讲如何压缩Transformers,重点关注BERT。使用不同的方法对attention层 全连接层等不同部分的压缩会有不同的效果,来看看作者后面怎么说。
PyTorch impelementations of BERT-based Spelling Error Correction Models. 基于BERT的文本纠错模型,使用PyTorch实现。 - gitabtion/BertBasedCorrectionModels
Fast-Bert supports XLNet, RoBERTa and BERT based classification models. Set model type parameter value to'bert',robertaor'xlnet'in order to initiate an appropriate databunch object. 2. Create a Learner Object BertLearner is the ‘learner’ object that holds everything together. It encapsulates th...
The model performance was compared with two state-of-the-art models, and it outperformed both models with 85% F1-score. 展开 关键词: the need for information extraction systems significantly DOI: 10.1155/2021/6633213 年份: 2021 收藏 引用 批量引用 报错 分享 ...
These transfer learning-based approaches have opened an opportunity to fine tune custom datasets for specific domains with pre-trained models on larger datasets. TB models have a deeper understanding of language context than traditional single-direction language models [4]. In this study, we ...
The major motivation behind BERT is to handle the limitation of the existing language models which are unidirectional in nature. This means that they only consider text left to right for sentence-level inference. BERT on the other hand, allows tokens to attend to both sides in the self-...