BART
BART (Bidirectional and Auto-Regressive Transformers) is a sequence-to-sequence model introduced by Facebook AI. It is based on the Transformer architecture and is designed for various natural language processing tasks, including text generation, summarization, and translation.
- Bidirectionality:One of the key features of BART is its bidirectionality, which allows it to effectively handle tasks that require understanding of context from both directions in a sequence. This bidirectionality is achieved through a combination of auto-regressive and bidirectional training objectives.
- BART has shown strong performance on a range of NLP tasks, particularly in text generation and summarization. Its ability to generate coherent and contextually relevant text makes it a valuable tool for tasks such as text summarization, where it can produce concise summaries of longer texts.
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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