Language and NLP Section 044
The Embedding Family
The models that turn a word, a sentence or a whole document into a list of numbers you can compare.
10 of 10 lessons published Three reading levels on every lesson
Lessons in order
Work top to bottom. Each lesson assumes the one above it.
- GloVe
- FastText and subword vectors
- King minus man plus woman, and why it works less often than you think
- Static vs contextual embeddings
- Sentence-transformers
- CLS, mean and max pooling
- Fine-tuning an embedding model on your own data
- Hard negative mining
- Embedding size and Matryoshka truncation
- Storing vectors as int8 and binary