People use search engines for various topics and items, from daily essentials to more aspirational and specialized objects. Therefore, search engines have taken over as peoples preferred resource. The How To prefix has become familiar and widely used in various search styles to find solutions to particular problems. This search allows people to find sequential instructions by providing detailed guidelines to accomplish specific tasks. Categorizing instructional text is also essential for task-oriented learning and creating knowledge bases. This study uses the How To articles to determine the multi-label instruction category. We have brought this work with a dataset comprising 11,121 observations from wikiHow, where each record has multiple categories. To find out the multi-label category meticulously, we employ some transformer-based deep neural architectures, such as Generalized Autoregressive Pretraining for Language Understanding (XLNet), Bidirectional Encoder Representation from Transformers (BERT), etc. In our multi-label instruction classification process, we have reckoned our proposed architectures using accuracy and macro f1-score as the performance metrics. This thorough evaluation showed us much about our strategys strengths and drawbacks. Specifically, our implementation of the XLNet architecture has demonstrated unprecedented performance, achieving an accuracy of 97.30% and micro and macro average scores of 89.02% and 93%, a noteworthy accomplishment in multi-label classification. This high level of accuracy and macro average score is a testament to the effectiveness of the XLNet architecture in our proposed InstructNet approach. By employing a multi-level strategy in our evaluation process, we have gained a more comprehensive knowledge of the effectiveness of our proposed architectures and identified areas for forthcoming improvement and refinement.

翻译：人们使用搜索引擎查询各类主题和物品，从日常必需品到更具抱负性的专业物品。因此，搜索引擎已成为人们的首选资源。"How To"前缀在各种搜索模式中已变得耳熟能详并被广泛使用，用以寻找特定问题的解决方案。此类搜索通过提供详细指导，使人们能够找到完成特定任务的顺序指令。对指令文本进行分类对于任务导向型学习和知识库构建也至关重要。本研究利用"How To"文章来确定多标签指令类别。我们构建了一个包含来自wikiHow的11,121条观测记录的数据集，其中每条记录都具有多个类别。为精确识别多标签类别，我们采用了若干基于Transformer的深度神经架构，例如广义自回归预训练语言理解模型（XLNet）、基于Transformer的双向编码器表示（BERT）等。在多标签指令分类过程中，我们使用准确率和宏平均F1分数作为性能指标评估了所提出的架构。这项全面评估充分揭示了我们策略的优势与不足。具体而言，我们实现的XLNet架构展现出前所未有的性能，取得了97.30%的准确率，以及89.02%的微平均分数和93%的宏平均分数，这在多标签分类领域是值得瞩目的成就。如此高的准确率和宏平均分数证明了XLNet架构在我们提出的InstructNet方法中的有效性。通过在评估过程中采用多层次策略，我们更全面地认识了所提出架构的效能，并确定了未来改进与优化的方向。