ERNIE-GEN: Bridging the Gap Between Training and Inference
If you're interested in natural language processing, you may have heard of ERNIE-GEN. ERNIE-GEN is a framework used for multi-flow sequence to sequence pre-training and fine-tuning. It was designed to bridge the gap between model training and inference by introducing an infilling generation mechanism and a noise-aware generation method while training the model to generate semantically-complete spans. In this article, we'll explore ERNIE
K3M: A Powerful Pretraining Method for E-commerce Product Data
K3M is a cutting-edge pretraining method for e-commerce product data that integrates knowledge modality to address missing or noisy image and text data. It boasts of modal-encoding and modal-interaction layers that extract features and model interactions between modalities. The initial-interactive feature fusion model maintains the independence of image and text modalities, while a structure aggregation module fuses information from
What is MPNet and How Does it Work?
MPNet is a pre-training method for language models that combines two approaches, masked language modeling (MLM) and permuted language modeling (PLM), to create a more efficient and effective model. It was designed to address the issues of two other popular pre-training models, BERT and XLNet. MPNet takes into consideration the dependency among predicted tokens and alleviates the position discrepancy of XLNet by utilizing the position information of all tokens
What is ReasonBERT?
ReasonBERT is a pre-training method that enhances language models with the ability to reason over long-range relations and multiple, possibly hybrid, contexts. It is a deep learning model that uses distant supervision to connect multiple pieces of text and tables to create pre-training examples that require long-range reasoning. This pre-training method is an improvement to existing language models like BERT and RoBERTa.
How does ReasonBERT work?
Imagine you have a query