Christopher Berner, Christopher M. Hesse, Sam McCandlish, Alec Radford, Ilya Sutskever. This paper explores methods for creating and using controlled datasets for MLM, and provides a practical guide for implementing the task.
在自然语言处理领域，基于掩码语言模型(Masked Language Model)的代理任务，BERT成功引领了自监督预训练的...
Although several deep learning paradigms have been studied for this task, the power of the recently emerging language models has not been fully explored. In this paper, we propose MaskSR, a masked language model capable of restoring full-band 44.1 kHz speech jointly considering noise, reverb, ...
给定节点,,它们的重叠子图大小为,设重叠子图中最大规模为;此外,图中节点的特征都是独立同分布地随机采样自一个的高斯分布,则我们可以给出 task irrelevant 信息的一个下界: 这个下界说明 task irrelevant 的信息和给定两点之间的 k-hop 邻域的重叠程度是正相关的,因此当我们采取基于边的掩码策略时,可以有效地去除 ...
The proposed method involves two learning tasks: valence–arousal intensity estimation, which is the major task, and random masked sentiment word prediction, which is the auxiliary task modify from mask language modeling, used to enhance the model performance. The experimental results indicate that ...
Unified vision-language frameworks have greatly advanced in recent years, most of which adopt an encoder-decoder architecture to unify image-text tasks as sequence-to-sequence generation. However, existing video-language (VidL) models still require task-specific designs in model architecture and ...
Say I want to train a model for sequence classification. And so I define my model to be: model = DistilBertForSequenceClassification.from_pretrained("bert-base-uncased") My question is - what would be the optimal way if I want to pre-train this model with masked langu...
MPNet can see full position information and know there are two tokens to predict, and thus it can model the dependency among predicted tokens and predict the tokens better. ObjectiveFactorization MLM (BERT) log P (sentence | the task is [M] [M]) + log P (clas...
BERT得益于Transformer强大的计算效率，构造一种类似完形填空的proxy task，可以将不同NLP任务的语料一起...
contrastive learning and MLM, where the former trains the model to discretize input continuous speech signals into a finite set of discriminative speech tokens, and the latter trains the model to learn contextualized speech representations via solving a masked prediction task consuming the discretized ...