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所在平台: Udemy |
课程主页: https://www.udemy.com/course/question-generation-using-natural-language-processing/
课程评论：没有评论
课程名称：使用自然语言处理进行问题生成 课程概述：本课程旨在利用最先进的自然语言处理技术解决教育科技中的问题生成问题。我们常见的中学教科书每章末尾都会有评估问题，如选择题、判断题、填空题、配对题等。本课程将教您如何使用自然语言处理(NLP)技术，从任何文本内容中生成这些评估问题。课程将展示NLP的实际应用，包括基础算法（如词向量技术word2vec、GloVe等）以及最近的进展（如BERT、OpenAI GPT-2和T5变换器）在真实世界中的应用。我们将使用Spacy、NLTK、AllenNLP、HuggingFace等NLP库，所有部分均附有易于使用的Google Colab笔记本，您可以在云端免费运行，并利用Google提供的免费GPU进行模型训练。 课程前提：课程将专注于算法的实际用例，虽然会介绍所用算法的高层次概念，但重点不在算法背后的数学推导。学员需对深度学习概念（如前向传播、反向传播、优化器、损失函数）有基本了解，同时需具备扎实的Python编程技能，并对自然语言处理和Pytorch有基本知识。 课程大纲： ➤ 为选择题选项生成干扰项：学员将使用Wordnet、ConceptNet和Sense2vec等多种方法为选择题生成干扰项。 ➤ 使用预训练模型生成判断题：学员将学习如何使用来自AllenNLP的成分解析器分割句子，利用GPT-2生成替代句子，并通过Sentence BERT进行筛选。 ➤ 从任何内容生成选择题：学员将理解T5变换器算法，并使用SQUAD数据集，通过HuggingFace Transformers库和Pytorch Lightning训练问题生成模型。 ➤ 生成填空题：学员将学习使用Python关键词提取库提取关键词，使用flashtext库进行快速关键词匹配，并在Colab中使用HTML ElementTree可视化填空题。 ➤ 生成配对题：学员将学习使用Python关键词提取库，flashtext库进行关键词匹配，并利用BERT进行词义消歧(WSD)。 ➤ 将问题生成模型部署到生产环境：学员将学习将T5等变换器模型转换为ONNX格式，进行量化，以无服务器的方式部署，同时使用FastAPI创建轻量级docker容器并在Google Cloud Run上进行部署。 此课程使学员能够熟练应用自然语言处理技术，在教育科技领域生成各种评估问题。
This course focuses on using state-of-the-art Natural Language processing techniques to solve the problem of question generation in edtech.If we pick up any middle school textbook, at the end of every chapter we see assessment questions like MCQs, True/False questions, Fill-in-the-blanks, Match the following, etc. In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques. This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT-2, and T5 transformers to real-world use.We will use NLP libraries like Spacy, NLTK, AllenNLP, HuggingFace transformers, etc.All the sections will be accompanied by easy to use Google Colab notebooks. You can run Google Colab notebooks for free on the cloud and also train models using free GPUs provided by Google.Prerequisites:This course will focus on the practical use cases of algorithms. A high-level introduction to the algorithms used will be introduced but the focus is not on the mathematics behind the algorithms.A high-level understanding of deep learning concepts like forward pass, backpropagation, optimizers, loss functions is expected. Strong Python programming skills with basic knowledge of Natural Language processing and Pytorch is assumed.The course outline:➤ Generate distractors (wrong choices) for MCQ optionsStudents will use several approaches like Wordnet, ConceptNet, and Sense2vec to generate distractors for MCQ options.➤ Generate True or False questions using pre-trained models like sentence BERT, constituency parser, and OpenAI GPT-2Students will learn to use constituency parser from AllenNLP to split any sentence. They will learn to use GPT-2 to generate sentences with alternate endings and filter them with Sentence BERT.➤ Generate MCQs from any content by training a T5 transformer model using the HuggingFace library.Students will understand the T5 transformer algorithm and use SQUAD dataset to train a question generation model using HuggingFace Transformers library and Pytorch Lightning.➤ Generate Fill in the blanks questionsStudents will learn to use Python Keyword extraction library to extract keywords, use flashtext library to do fast keyword matching, and visualize fill-in-the-blanks using HTML ElementTree in Colab➤ Generate Match the following questions.Students will learn to use Python Keyword extraction library to extract keywords, use flashtext library to do fast keyword matching, and use BERT to do word sense disambiguation (WSD).➤ Deploy question generation models to production.Deploy transformer models like T5 to production in a serverless fashion by converting them to ONNX format and performing quantization. Create lightweight docker containers using FastAPI for transformer model and deploy on Google Cloud Run.
