Vision transformer and BERT seem to be made of only encoder section of a transformer. So, are they very similar to each other? If not, please give a short gist where the differences lie. Even better, if you can throw me a material which explains what the BERT architecture is composed of (from what I have seen, videos on YT are confusing or are very shallow when it comes to explaining BERT).
Great question! While Vision Transformers (ViT) and BERT both use the encoder part of the transformer architecture, they serve different purposes and have some key differences:
Purpose: ViT is designed for computer vision tasks, while BERT is focused on natural language processing (NLP).
Input: ViT processes images as sequences of patches, whereas BERT processes text data.
Training: ViT requires large amounts of labeled image data, while BERT is pre-trained on vast amounts of text data and fine-tuned for specific tasks.
For a detailed explanation of BERT’s architecture, you can check out the comprehensive guide on Analytics Vidhya or the Hugging Face blog.
Hello,
Vision Transformers (ViT) and BERT are indeed both based on the encoder part of the transformer architecture, but they serve different purposes and have some key differences: DMV NY
Vision Transformers (ViT)
Purpose: Designed for image processing tasks.
Architecture: Uses self-attention mechanisms to process images as sequences of patches, similar to how BERT processes text.
Application: Used in tasks like image classification, object detection, and more.
BERT (Bidirectional Encoder Representations from Transformers)
Purpose: Designed for natural language processing (NLP) tasks.
Architecture: Uses bidirectional training to understand the context of words in a sentence.
Application: Used in tasks like text classification, sentiment analysis, question answering, and more.
For a detailed explanation of BERT’s architecture, you can refer to the Hugging Face BERT 101 guide. It provides a comprehensive overview of how BERT works and its applications.