Intelligent Interaction of Fluorine containing materials Based on NL2SQL

Abstract

The data on the production and development of fluorine containing materials are characterized by a large amount of data and a high degree of dimensionality of physical and chemical property characterization indicators. The manual way of analyzing the data item by item not only has high interaction cost, but also is difficult to analyze and explore the data intuitively. In order to efficiently utilize the data, this paper firstly constructs a dataset of fluorine-containing materials and proposes the Mengzi-ITPT model based on it, which takes Mengzi as the encoder and uses the attention mechanism to enhance the representation of the listed information. Meanwhile, for the data characteristics of fluorine containing materials, the training strategy of ITPT is adopted to improve the accuracy of the model. The experimental results show that the accuracy of the Mengzi-ITPT model query reaches 86.9% when the model is trained under the fluorine-containing material dataset.