Research on the Application of Tibetan Sentence Vector Pre-trained Model in Embedded Systems
This paper investigates the problem of deploying Tibetan sentence vector pre-trained models to embedded systems for infer-ence and testing.In machine learning,encoding and representation of text pose challenges,leading to the prominence of sentence vector techniques in the field of natural language processing(NLP).However,research on sentence vectors in the Tibetan NLP domain is rel-atively limited.Therefore,this paper analyzes the existing pre-trained models and sentence vector representation methods in the Tibetan language field,and designs an improved simple contrastive learning of unsupervised sentence embeddings(I-SimCSE)method.The experimental results indicate that the performance of Tibetan sentence vectors model trained using the I-SimCSE method outperforms other methods.Furthermore,this paper explores the integration of edge computing with pre-trained models and discusses the potential application scenarios of pre-trained language models on embedded systems.Finally,the I-SimCSE sentence vector model is deployed on the Jetson TX1 embedded device,and its average single inference time is tested,demonstrating the feasibility of deploying pre-trained language models for inference on embedded systems.In conclusion,this research provides a useful reference for the application of Tibetan sentence vector pre-trained models on embedded systems,and offer guidance and insights for the future development of Ti-betan large model on embedded systems.
Tibetansentence vector representationembedded systempre-trained model
NETL
NSTL
万方数据
