Explore Natural Language Processing and modern language models interactively
Word2Vec, GloVe, and semantic spaces
Self-attention and multi-head attention
Encoder-decoder and BERT/GPT models
BPE, WordPiece, and subword tokenization
Translation and text generation
Text classification and emotion detection
Word embeddings represent words as dense vectors in a continuous space where semantically similar words are closer together.
Famous Word Analogy:
Vector arithmetic captures semantic relationships!
Red = Selected word | Blue = Other words
Skip-gram & CBOW models
Predicts context from word or vice versa
Global Vectors
Uses word co-occurrence statistics
Subword embeddings
Handles out-of-vocabulary words