High Resolution Spectroscopic Study of Be-10(Lambda)
Physical Review C(2016)SCI 2区SCI 1区
Kyoto Univ | Hampton Univ | Tohoku Univ | Johannes Gutenberg Univ Mainz | N Carolina Agr & Tech State Univ | Univ Zagreb | AI Alikhanyan Natl Sci Lab | Florida Int Univ | Christopher Newport Univ | Ist Nazl Fis Nucl | Southern Univ New Orleans | Thomas Jefferson Natl Accelerator Facil JLab | Univ N Carolina | Lanzhou Univ | Univ Houston | Yamagata Univ | Mississippi State Univ | James Madison Univ | Univ Metropolitana | Virginia Mil Inst | Xavier Univ
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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