Integrating Text and Knowledge Graph Embeddings to Enhance RAG Performance

Integrating Text and Knowledge Graph Embeddings to Enhance RAG Performance

Source: DeepHub IMBA This article is approximately 4600 words long and is recommended to be read in 10 minutes. In this article, we will combine text and knowledge graphs to enhance the performance of our RAG. In our previous articles, we introduced examples of combining knowledge graphs with RAG. In this article, we will combine … Read more

Understanding Transformer and Its Variants

Understanding Transformer and Its Variants

Follow the public account "ML_NLP" Set as “Starred“, heavy content will be delivered to you first! Author: Jiang Runyu, Harbin Institute of Technology SCIR Introduction In recent years, one of the most impressive achievements in the field of NLP is undoubtedly the pre-trained models represented by BERT proposed by Google. They continuously refresh records (both … Read more

Overlooked Details of BERT and Transformers

Overlooked Details of BERT and Transformers

MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university professors, and corporate researchers. The community’s vision is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. Reprinted from | … Read more

Understanding Attention, Transformer, and BERT Principles

Understanding Attention, Transformer, and BERT Principles

Follow the public account “ML_NLP“ Set as “Starred“, delivering heavy content promptly! Original · Author | TheHonestBob School | Hebei University of Science and Technology Research Direction | Natural Language Processing 1. Introduction There are countless good articles online about this topic, all of which are very detailed. The reason I am writing this blog … Read more

Detailed Explanation of Transformer Structure and Applications

Detailed Explanation of Transformer Structure and Applications

Follow the public account “ML_NLP“ Set as “Starred“, heavy content delivered to you first! Source | Zhihu Address | https://zhuanlan.zhihu.com/p/69290203 Author | Ph0en1x Editor | WeChat public account on Machine Learning Algorithms and Natural Language Processing This article is for academic sharing only. If there is any infringement, please contact us to delete it. This … Read more

Understanding Transformer Algorithms in Neural Networks

Understanding Transformer Algorithms in Neural Networks

This article will cover theessence of Transformer, the principles of Transformer, and improvements in Transformer architecture in three aspects to help you understand Transformer. 1. Essence of Transformer Transformer Architecture: It mainly consists of four parts: input section (input-output embeddings and position encoding), multi-layer encoder, multi-layer decoder, and output section (output linear layer and Softmax). … Read more

How BERT Tokenizes Text

How BERT Tokenizes Text

Follow the official account “ML_NLP“ Set as “Starred“, delivering heavy content promptly! Source | Zhihu Link | https://zhuanlan.zhihu.com/p/132361501 Author | Alan Lee Editor | Machine Learning Algorithms and Natural Language Processing Public Account This article is authorized and reposting is prohibited This article was first published on my personal blog on 2019/10/16 and cannot be … Read more

Pre-training BERT: How TensorFlow Solved It Before Official Release

Pre-training BERT: How TensorFlow Solved It Before Official Release

Edited by Machine Heart Contributors: Siyuan, Wang Shuting This month, Google’s BERT has received a lot of attention, as the research has refreshed the state-of-the-art performance records in 11 NLP tasks with its pre-trained model. The authors of the paper stated that they would release the code and pre-trained model by the end of this … Read more

Beginner’s Guide to Using BERT: Principles and Hands-On Examples

Beginner's Guide to Using BERT: Principles and Hands-On Examples

Author Jay Alammar, Translated by QbitAI | WeChat Official Account QbitAI BERT, as a key player in the field of natural language processing, is an unavoidable topic for NLPer. However, for those with little experience and a weak foundation, mastering BERT can be a bit challenging. Now, tech blogger Jay Alammar has created a “Visual … Read more

Google Automatically Generates Text from Knowledge Graphs

Google Automatically Generates Text from Knowledge Graphs

New Intelligence Report Source: Google AI Editor: LRS [New Intelligence Guide] Based on pre-training experience, more data leads to better performance! Google recently published a paper at NAACL 2021 that can automatically generate text data from knowledge graphs, so there’s no need to worry about insufficient corpora anymore! Large pre-trained natural language processing (NLP) models, … Read more