For the purpose of training and evaluating our intent classification model for electric automation, we curated a dataset consisting of intent-based user instructions. The dataset comprises a total of 14 intents, each associated with approximately 10 user instructions, resulting in a total of 140 instructions for electric automation. The intents were carefully selected to cover a diverse range of control commands and actions commonly encountered in electric automation scenarios. These intents include commands for turning on/off electrical appliances. Each user instruction in the dataset is labeled with its corresponding intent, allowing the model to learn the mapping between input instructions and their intended actions.
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