# Large-scale parameter model

NLLB-200 is a neural machine translation model supporting 200 languages, focusing on translation research for low-resource languages.

NLLB-MoE is a Mixture of Experts machine translation model developed by Meta, supporting 200 languages, and is one of the most advanced open-access machine translation models available.

ERNIE 3.0 is a large-scale knowledge-enhanced pre-trained model for language understanding and generation, developed by Baidu.

CodeT5 is a large-scale encoder-decoder model pre-trained with NTP objectives for Python language, focusing on code understanding and generation tasks

This model is a high-performance open-domain chatbot capable of integrating multiple dialogue skills such as questioning, answering, knowledge demonstration, and empathy.

This model achieves multi-skill integrated open-domain dialogue capabilities through large-scale neural models and carefully designed training strategies.

This is an open-domain dialogue system based on large-scale neural networks, capable of conducting multi-turn natural conversations and integrating various dialogue skills.

T0pp is an 11-billion-parameter encoder-decoder model based on the T5 architecture, excelling in zero-shot task generalization with English natural language prompts, outperforming GPT-3 while being more compact.

DeBERTaV3 improves upon DeBERTa with ELECTRA-style pre-training and gradient-disentangled embedding sharing techniques, excelling in natural language understanding tasks