Research Paper Recommendation System using Transformer ModelNayse, Snehal S.Deshmukh, Pratiksha R.Grenze International Journal of Engineering & Technology (GIJET)
The transformer [1] is a well-known deep neural network (DNN) model, which has revolutionized the artificial intelligence (AI) field. The architecture of the transformer builds the backbone of large language models (LLM), enabling them to harness the power of vast amounts of data to gain a...
Originating from a 2017 research paper by Google, transformer models are one of the most recent and influential developments in the Machine Learning field. The first Transformer model was explained in the influential paper"Attention is All You Need. ...
论文链接：https://arxiv.org/pdf/2102.11174.pdf5. Universal Language Model Fine-tuning for Text Classification (2018)这篇论文虽然发表于2018年，但并没有研究Transformer，而主要关注循环神经网络，但提出了有效的预训练语言模型和对下游任务的迁移学习。论文链接：https://arxiv.org/abs/1801.06146虽然迁移学...
designing a highly performant and efficient inference system is extremely challenging. In this paper, we present DeepSpeed Inference, a comprehensive system solution for transformer model inference to address the above-mentioned challenges. DeepSpeed Inference consists...
Besides evaluating our model with NLP metrics presented in theresearch paper, we also did extensive offline evaluation based on the location, length, and log-likelihood of the completion suggestions. The extensive offline evaluation and online metrics collected through ...
all different sizes of the same model ViT-B/16 - ViT-Base with image patch size 16x16 layers = no. of transformers encoder layers hidden sizeD - the embedding size throughout the architecture - if we have embedding size of 768 means ...
In the paper for the 2017 NeurIPS conference, the Google team described their transformer and the accuracy records it set for machine translation. Thanks to a basket of techniques, they trained their model in just 3.5 days on eight NVIDIA GPUs, a small fraction of the time and cost of train...
The goal of this research is to develop a self-attention-based transformer model for assessing CVD risk utilizing the Cleveland dataset. This dataset contains a variety of medical and non-medical components that can be used to identify whether a patient has cardiac disease. The dataset comprises ...
Deep learning model The Hugging Face open-source version of Google Research’s ELECTRA94 deep learning transformer was used to train a general chemistry model from scratch and subsequently to fine-tune the model to enable performing downstream tasks such as binary classification. The rational design ...