Blogs

管理的本质是一种实践，在某些情形下，阅历比简历更重要，丰富的职业经历有助于企业高管形成多元化的思维结构、广阔的管理视野、丰富的社会资源和过人的胆识。因此，对于企业而言，了解高管的职业经历非常重要，这可以帮助企业更好地了解高管的背景和潜力，从而更好地为企业的发展提供支持。而研究高管的个人特质，已有的研究，主要从年龄、性别、学历等类别型变量开展研究，即使从从职业经历研究，也是作为离散变量，没有充分挖掘职业经历的信息。...

开源 LLMS 越来越受欢迎，Ollama 的 OpenAI 兼容性后来发布了，这使得使用 JSON 模式获取结构化输出成为可能。在本篇博文的结尾，您将了解如何有效地利用 Instructor 和 ollama。但在继续之前，让我们先探讨一下修补的概念。Open-source LLMS are gaining popularity, and the release of Ollama's OpenAI compatibility later it has made it possible to obtain structured outputs using JSON schema.By the end of this blog post, you will learn how to effectively utilize instructor with ollama. But before we proceed, let's first explore the concept of patching....

情感分析是分析文本以确定消息的情绪基调是积极、消极还是中性的过程。通过情感分析，我们可以了解文本是否表现出快乐、悲伤、愤怒等情绪。主要的计算方法有语义词典法、机器学习法、混合方法、其他方法。 随着chatGPT这类大语言模型的出现， 它们增强了文本理解能力，使我们能够更精准的把握文本中的语义和情绪，也因此大型语言模型 (LLM) 一出场就有实现情感分析功能。Sentiment analysis is the process of analyzing text to determine whether the emotional tone of a message is positive, negative, or neutral. Through sentiment analysis, we can understand whether the text expresses emotions such as happiness, sadness, anger, etc. The main computational methods are semantic dictionary method, machine learning method, hybrid method, and other methods. With the emergence of large language models such as chatGPT, they enhance text understanding capabilities, allowing us to more accurately grasp the semantics and emotions in the text. Therefore, large language models (LLMs) have implemented sentiment analysis functions as soon as they appeared....

大邓是一个技术博主，运营着公众号，每天要消耗大量的时间进行选题、创作、编辑。随着LLM的流行， 能否让LLM替我进行选题、创作、编辑，从此进入躺平式人生新阶段。 这不是做梦， 使用软件Ollama、Python的CrewAI库，设计好智能体(AI Agent)，就能实现大邓的白日梦。In technical terms an AI Agent is a software entity designed to perform tasks autonomously or semi-autonomously on behalf of a user or another program. These agents leverage artificial intelligence to make decisions, take actions, and interact with their environment or other systems....

数据科学家花费 80% 以上的时间来准备数据，这其中主要是数据清洗、数据标注。随着 GPT-4 等大型语言模型 (LLM)的兴起，现在我们可以更高效的准备工作。在本文中，我们将探讨如何使用 LLM 进行数据标注，以提高文本注释的准确性、效率和可扩展性，并最终为 ML 项目带来更好的结果。 Data scientists spend over 80% of their time preparing data, including data labeling. With the rise of Large Language Models (LLMs) like GPT-4, we now have the tools to streamline this process significantly.In this article, we’ll explore how to use LLM for data labeling to enhance the accuracy, efficiency, and scalability of text annotations and ultimately drive better outcomes for ML projects....