This article is approximately 9200 words long and is recommended for a reading time of over 10 minutes. This article mainly shares typical cases in the financial sector and further reflects on common issues in the implementation of large models in vertical domains. Introduction The large model from Hang Seng Electronics has been implemented in … Read more
Source: Fresh Dates Classroom Original Author: Little Date Jun This article provides a simple and accessible introduction to what large models are, how they are trained, and their applications. What Are Large Models Large models, known in English as Large Model, were also referred to as Foundation Model in the early days. Large model is … Read more
Follow the public account “ML_NLP“ Set as “Starred“, heavy content delivered first! Written by | Lao Tao (Researcher from a certain company, hereditary parameter tuning) Translated by | Beautiful person with meticulous thoughts Introduction As the hottest topic in NLP over the past two years, the language pre-training technologies represented by ELMo/BERT are already familiar … Read more
Follow the WeChat public account “ML_NLP” Set as “Starred” to receive heavy content promptly! Reprinted from | PaperWeekly ©PaperWeekly Original · Author｜Hai Chenwei School｜Master’s student at Tongji University Research Direction｜Natural Language Processing In the current NLP field, Transformer/BERT has become a fundamental application, and Self-Attention is the core part of both. Below, we attempt to … Read more
Source: Python Data Science This article is about 7200 words, recommended reading time 14 minutes. In this article, we will explore the Transformer model and understand how it works. 1. Introduction The BERT model launched by Google achieved state-of-the-art results in 11 NLP tasks, triggering a revolution in the NLP field. One key factor for … Read more
Every day we bring you NLP technology insights! Introduction Entity Relation Extraction is a core task of text mining and information extraction. It mainly models text information to automatically extract semantic relationships between entity pairs, thereby extracting valid semantic knowledge. The research results are mainly applied in text summarization, automatic question answering, machine translation, semantic … Read more
Delivering NLP technical insights to you every day! © Author | Zeng Weihao Institution | Beijing University of Posts and Telecommunications Research Direction | Dialogue Summarization Typesetting | PaperWeekly Paper Title: Adversarial Self-Attention For Language Understanding Paper Source: ICLR 2022 Paper Link: https://arxiv.org/pdf/2206.12608.pdf Introduction This paper proposes the Adversarial Self-Attention mechanism (ASA), which reconstructs the … Read more
Source: AI Technology Review This article contains over 10,000 words, and it is recommended to read it in about 20 minutes. In this article, author Zhang Junlin uses vivid language to compare the features of the three major feature extractors in natural language processing (CNN/RNN/TF). At the turn of the year, everyone is busy reviewing … Read more
Author: Sienna Reviewed by: Los Abstract: Large Language Models (LLMs) have demonstrated exceptional capabilities in numerous language processing tasks; however, the computational intensity and memory consumption required for their deployment have become significant challenges to improving service efficiency. Industry estimates suggest that the processing cost of a single LLM request can be as much as … Read more
Author丨Fat Cat, Yi Zhen Zhihu Column丨Machine Learning Algorithms and Natural Language Processing Address丨https://zhuanlan.zhihu.com/p/82477941 Natural Language Processing (NLP) is generally divided into two categories: Natural Language Understanding (NLU) and Natural Language Generation (NLG). The former extracts or analyzes concise logical information from a piece of text, such as Named Entity Recognition (NER) which identifies keywords in … Read more