给我推荐20个比较流行的NLU 深度学习模型

### 回答1：
好的，这是比较流行的 20 个 NLU 深度学习模型:

1. BERT
2. GPT
3. Transformer
4. RoBERTa
5. XLNet
6. ERNIE
7. Albert
8. T5
9. Pegasus
10. XLM
11. DeBERTa
12. Text-GPT
13. MT-DNN
14. ELECTRA
15. FlauBERT
16. Text-XLNet
17. CTRL
18. Funnel-Transformer
19. UniLM
20. BART

希望这些模型能够帮助你。如果你想了解这些模型的更多信息，我可以为你提供一些资源。

### 回答2：
以下是20个比较流行的NLU深度学习模型：

1. BERT (Bidirectional Encoder Representations from Transformers)
2. GPT (Generative Pre-trained Transformer)
3. XLNet (eXtreme Multi-task Learning)
4. RoBERTa (Robustly Optimized BERT Approach)
5. ALBERT (A Lite BERT)
6. ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately)
7. DistilBERT (Distilled BERT)
8. TinyBERT (Smaller BERT for Fine-tuning)
9. CamemBERT (BERT for French Language)
10. Multilingual BERT (BERT for Multiple Languages)
11. SpanBERT (BERT for Span-based Question Answering)
12. MT-DNN (Multi-Task Dual Encoder with Transformers)
13. T5 (Text-to-Text Transfer Transformer)
14. GPT-3 (Generative Pre-trained Transformer 3)
15. BART (Bidirectional and Auto-Regressive Transformers)
16. XLM (Cross-lingual Language Model)
17. Transformer-XL (Transformer with Long-term Dependency)
18. ULMFiT (Universal Language Model Fine-tuning)
19. ELMo (Embeddings from Language Models)
20. LASER (Language-Agnostic SEntence Representations)

这些深度学习模型在NLU领域中得到广泛应用和研究，并结合自然语言处理任务，如文本分类、命名实体识别、情感分析等，能够帮助处理和理解自然语言文本。