List of transfer learning NLP models
A list of prominent models in natural language processing that employ transfer learning techniques, each known for their unique contributions and enhancements in the field:
- BERT (Bidirectional Encoder Representations from Transformers): Developed by researchers at Google, BERT leverages a transformer-based architecture and enhances model understanding through tasks like masked language modeling and next sentence prediction.
- GPT (Generative Pre-trained Transformer): Introduced by OpenAI, GPT models excel in text generation by employing autoregressive language modeling during their training phase.
- RoBERTa (Robustly Optimized BERT Approach): This model refines the BERT architecture by eliminating the next-sentence prediction and optimizing training with larger batch sizes and higher learning rates.
- T5 (Text-To-Text Transfer Transformer): Another innovation from Google, T5 transforms all natural language processing tasks into a text-to-text framework, treating both inputs and outputs as text strings.
- XLNet: Jointly developed by Google and Carnegie Mellon University, XLNet integrates the best features of autoregressive and autoencoding models, offering a versatile approach to pre-training.
- ALBERT (A Lite BERT): Designed to be a more efficient variant of BERT, ALBERT reduces model size and enhances training speed by sharing parameters across layers and decomposing the embedding layer.
- DistilBERT: This model is a streamlined version of BERT, designed to be smaller and faster, yet it manages to preserve a majority of BERT’s original language understanding capabilities.
- ERNIE (Enhanced Representation through kNowledge Integration): By Baidu, ERNIE improves language models by integrating structured world knowledge from knowledge graphs into its training process, enhancing its contextual awareness.
- ELECTRA: It introduces an innovative training method called replaced token detection, which is different from the masked language modeling in BERT, where a discriminator learns to distinguish between authentic and artificially altered tokens.
- BART (Bidirectional and Auto-Regressive Transformers): BART merges the strengths of BERT’s bidirectional training and GPT’s autoregressive capabilities. It is trained by corrupting texts in various ways and learning to reconstruct the original text accurately.
Transfer Learning in NLP
Transfer learning is an important tool in natural language processing (NLP) that helps build powerful models without needing massive amounts of data. This article explains what transfer learning is, why it’s important in NLP, and how it works.
Table of Content
- Why Transfer Learning is important in NLP?
- Benefits of Transfer Learning in NLP tasks
- How Does Transfer Learning in NLP Work?
- List of transfer learning NLP models
- 1. BERT
- 2. GPT
- 3. RoBERTa
- 4. T5
- 5. XLNet
- 6. ALBERT (A Lite BERT)
- 7. DistilBERT
- 8. ERNIE
- 9. ELECTRA
- 10. BART
- Conclusion
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