This demonstration shows how traditional models (like RNNs) process text one word at a time, while transformers process all words simultaneously.
What's happening: The first button demonstrates how traditional models like RNNs process text sequentially (one word at a time), causing delays in processing. The second button shows how transformers process the entire sentence simultaneously, allowing for much faster processing and parallel computation.
This demonstration shows how traditional models struggle with long-distance relationships in text, while transformers can directly connect related words.
What's happening: This demo illustrates how traditional models must process information sequentially, causing them to "forget" earlier context in long sentences. In contrast, transformers can make direct connections between related words regardless of distance, allowing them to better understand complex relationships in text.
This interactive demo shows how attention works in transformer models, allowing direct connections between related words regardless of their position in the sentence.
What's happening: This visualization demonstrates how the attention mechanism in transformers allows words to directly "attend to" other words in a sentence. When you click on a word, you can see which other words it pays attention to, with line thickness indicating the strength of attention. This direct connection allows transformers to understand relationships between words regardless of their distance from each other.