New National and Regional Bryophyte Records, 69
Journal of Bryology(2022)SCI 3区
Nat Hist Museum | Russian Acad Sci | Univ Zagreb | Kherson State Univ | Univ Las Amer | Munzur Univ | Adnan Menderes Univ | Karadeniz Tech Univ | Nat Hist Museum Montenegro | Canakkale Onsekiz Mart Univ | Univ Estadual Feira de Santana | Nigde Omer Halisdemir Univ | Lomonosov Moscow State Univ | Univ Complutense Madrid | Bulgarian Acad Sci | Matlock | Univ Zurich | Weisskirchen | Gantz Family Collect Ctr | Univ Ljubljana | Univ South Bohemia | Masaryk Univ Brno | Tech Univ | CSIC | Polish Acad Sci | Inst Pesquisas Ambientais | Univ Lyon 1 | CSIR | Univ Murcia | Herdwangen Schonach | Univ Lisbon | Norwegian Univ Sci & Technol | Ctr Estadual Educ Profiss Saude Adelia Teixeira | Corso Lavoro & Sci | Romanian Acad | Univ South Pacific | Senckenberg Res Inst
- 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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