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
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...
Investigating the effectiveness of pre-trained BERT-based models for single-label, multi-class FoRC: This study examines the potential of BERT-based models in capturing the semantic relationship within scientific articles and their corresponding research fields. ...
每个WordPiece token输入被表示为三个向量,token/ segment/ position embedding,相加进入model主体。 每个transformer层堆叠很多编码器单元,每个编码器包含两个主要子单元:self-attention和前向反馈网络FFN,通过残差连接。每个self-attention包含全连接层、多头multi-head self-attention层、全连接层(前后都有),FFN只包含全...
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 goal in this work is to propose a BERT-based approach to automatically classify the BSV to make their data easily indexable. We sampled 200 BSV to finetune the pretrained BERT language models and classify them as pest or/and disease and we show preliminary results. 展开 ...
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...
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
manipulating FireBERT, compared to working on unhardened classif i ers. Weshow that it is possible to improve the accuracy of BERT-based models in the faceof adversarial attacks without signif i cantly reducing the accuracy for regular bench-mark samples. We present co-tuning with a synthetic...
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-...