Understanding Child-Tuning: Fine-Tuning Technique for Pretrained Models
If you're interested in the world of machine learning, chances are you have heard of child-tuning. It is a fine-tuning technique that is used to update a subset of parameters of large pre-trained models in order to effectively adapt them to a range of tasks while maintaining their generalization ability. In simple terms, child-tuning allows you to take an already-existing deep learning model and make it better suited for yo
Discriminative Fine-Tuning: An Overview
Discriminative Fine-Tuning is a strategy used for ULMFiT type models. This strategy allows us to tune each layer of our model with different learning rates to improve its accuracy. Fine-tuning is a popular technique where pre-trained models are adapted to new tasks by updating their parameters with new data. But fine-tuning all layers with the same learning rate may not be the best option when dealing with complex models. That's where Discriminative Fine-
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
Virtual Data Augmentation, or VDA, is an advanced technique used in machine learning to improve the quality of language models. It works by fine-tuning pre-trained models using a mixture of virtual data and Gaussian noise. The result is a more robust and accurate language model that is better able to understand and respond to natural language queries.
What is Virtual Data Augmentation?
Virtual Data Augmentation is a technique used in machine learning to improve the performance and accuracy of