ALBERT (A Lite BERT)
ALBERT, which stands for “A Lite BERT,” is a variant of BERT (Bidirectional Encoder Representations from Transformers) that aims to reduce model size and increase training speed without significantly sacrificing performance. Developed by Google Research, ALBERT addresses the issues related to scalability and memory consumption that arise with large models like BERT. Here are four key aspects of ALBERT:
- Parameter Reduction Techniques: ALBERT incorporates two main strategies to reduce the number of parameters compared to BERT. The first is factorized embedding parameterization, which separates the size of the hidden layers from the size of vocabulary embeddings. This approach reduces the parameter count by allowing the model to project word embeddings into smaller-dimensional embeddings before feeding them into the deeper network layers. The second strategy involves cross-layer parameter sharing, which ensures that all layers share the same set of parameters, drastically reducing memory usage and improving the training speed.
- Inter-sentence Coherence Loss: ALBERT modifies the next sentence prediction (NSP) task used in BERT with a sentence-order prediction (SOP) task. SOP is designed to focus more directly on modeling inter-sentence coherence, rather than just predicting whether two segments follow each other, which improves the model’s understanding of sentence relationships and text structure.
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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