Pretraining

The driving force behind modern transformer models stems to a large extent from its pertaining data, allowing for strong in-context…Continue reading on Towards Data Science » The driving force behind modern transformer models stems to a large extent from its pertaining data, allowing for strong in-context…Continue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In…

Large Language Models, StructBERT — Incorporating Language Structures into PretrainingMaking models smarter by incorporating better learning objectivesIntroductionAfter its first appearance, BERT has shown phenomenal results in a variety of NLP tasks including sentiment analysis, text similarity, question answering, etc. Since then, researchers notoriously tried to make BERT even more performant by either modifying its architecture, augmenting training data, increasing vocabulary size or changing the hidden size of …

What are the pitfalls, benefits, and steps of pre-training your own model, and the limitations of existing PLMs?Image generated by DALL-E with prompt from author.Who is this blog post for and what to expect from this blog post?The goal of this blog post is to talk about how Pre-trained Language Models (PLM) can be used in creating Natural Language Processing (NLP) products and what the upsides and downsides are of using them. Training your own Transformer model from scratch will be discussed. High-level benefits and…

Most artificial intelligence models aren’t open-source, which means we, regular people like us, cannot use them freely. This is what we will dive into in this video... The most well-known, Dall-e 2, can be used to generate images from random prompts. The data used to train such models as well coming from random images on the internet pretty pretty. We will look into what they are trying to mitigate risks and how they are filtering out violent and sexual images from the internet.Louis BouchardI explain Artificial…

Boost your model performance via multimodal masked auto-encodersPhoto by Pablo Arenas on UnsplashSelf-supervised pre-training is a main approach to improve the performance to traditional supervised learning, in which large amount of labeled data is necessary and costly. Among self-supervised learning methods, contrastive learning is popular for its simplicity and efficacy. However, most contrastive learning methods use global vectors in which the details of pixel-level information is lost, which leaves room of improvement…

Fully leveraging supervision signals in contrastive perspectivesPhoto by Waranont (Joe) on UnsplashContrastive pre-training has been widely applied in deep learning. One reason for this is that contrastive pre-training can improve the efficiency of labeled data. During unsupervised contrastive pre-training, the unlabeled images are clustered in the latent space, forming fairly good decision boundaries between different classes. Based on this clustering, the subsequent supervised fine-tuning will achieve better performance…