Originally posted on Quantpedia.
In recent years, the Transformer architecture has experienced extensive adoption in the fields of Natural Language Processing (NLP) and Natural Language Understanding (NLU). Google AI Research’s introduction of Bidirectional Encoder Representations from Transformers (BERT) in 2018 set remarkable new standards in NLP. Since then, BERT has paved the way for even more advanced and improved models. 
We discussed the BERT model in our previous article. Here we would like to list alternatives for all of the readers that are considering running a project using some large language model (as we do), would like to avoid ChatGPT, and would like to see all of the alternatives in one place. So, presented here is a compilation of the most notable alternatives to the widely recognized language model BERT, specifically designed for Natural Language Understanding (NLU) projects.
Keep in mind that the ease of computing can still depend on factors like model size, hardware specifications, and the specific NLP task at hand. However, the models listed below are generally known for their improved efficiency compared to the original BERT model.
This is a distilled version of BERT, which retains much of BERT’s performance while being lighter and faster.
- ALBERT (A Lite BERT)
ALBERT introduces parameter-reduction techniques to reduce the model’s size while maintaining its performance.
Based on BERT, RoBERTa optimizes the training process and achieves better results with fewer training steps.
ELECTRA replaces the traditional masked language model pre-training objective with a more computationally efficient approach, making it faster than BERT.
- T5 (Text-to-Text Transfer Transformer)
T5 frames all NLP tasks as text-to-text problems, making it more straightforward and efficient for different tasks.
- GPT-2 and GPT-3
While larger than BERT, these models have shown impressive results and can be efficient for certain use cases due to their generative nature.
- DistillGPT-2 and DistillGPT-3
Like DistilBERT, these models are distilled versions of GPT-2 and GPT-3, offering a balance between efficiency and performance.
Visit Quantpedia for details on these models.
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