ELECTRA模型认为bert的预训练任务过于简单,预训练任务应该能够动态选择语料中较难的部分进行mask。所以作者把生成式的Masked language model(MLM)预训练任务改成了判别式的Replaced token detection(RTD)任务,判断当前token是否被语言模型替换过。作者结合gan的思想使用一个MLM的G-BERT来对输入句子进行更改,然后丢给D-BERT...
*BERT预训练过程包含两个不同的预训练任务,分别是Masked Language Model和Next Sentence Prediction任务。 Masked Language Model(MLM) 通过随机掩盖一些词(替换为统一标记符[MASK]),然后预测这些被遮盖的词来训练双向语言模型,并且使每个词的表征参考上下文信息。 这样做会产生两个缺点:( 1)会造成预训练和微调时的不...
BANG is a new pretraining model to Bridge the gap between Autoregressive (AR) and Non-autoregressive (NAR) Generation. AR and NAR generation can be uniformly regarded as to what extent previous tokens can be attended, and BANG bridges AR and NAR generati
(self, ~\Documents\anaconda\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, ...
Language model pre-training and the derived general-purpose methods have reshaped machine learning research. However, there remains considerable uncertainty regarding why pre-training improves the performance of downstream tasks. This challenge is pronou
我们的Point-BERT采用纯转化器架构和BERT式的预训练技术,在ModelNet40上达到了93.8%的准确率,在ScanObjectNN的复杂设置上达到了83.1%的准确率,超过了精心设计的点云模型,而人类的先验因素要少得多。我们还表明,通过Point-BERT学到的表征可以很好地转移到新的任务和领域,我们的模型在很大程度上推进了少样本点云分类...
As part of MicrosoftAI at Scale(opens in new tab), the Turing family of NLP models are being used at scale across Microsoft to enable the next generation of AI experiences. Today,we are happy to announce that the latest Microsoft Turing...
of tasks by varying the number and lengths of blanks. On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1.25x parameters ...
作者要求模型预测输入的真实lable的同时,还需要尽可能地和teacher model的logits接近,由此提出了损失函数: 损失函数的第一项即是预测真实label的交叉熵,第二项则为teacher model和student model的logits的KL散度。α作为超参数用来调控比例。 处理利用BERT和ViT做知识蒸馏外,作者还提出了梯度掩码策略来防止CV任务和NLP任...
「BertModel」, The bare Bert Model transformer outputting「raw hidden-states」without any specific head on top。这个类的目标主要就是利用「Transformer」获取序列的编码向量。抽象出来的目标是为了适配不同的预训练任务。例如:MLM预训练任务对应的类为BertForMaskedLM,其中有个成员实例为BertModel,就是为了编码序...