Seonglae ChoEmbeddings are arrays of floating point numbers that represent the semantic meaning of a piece of content
Text encoding is more broader concept which means does not requires to reserve original semantics like One Hot encoding
The advantage of vectorization is that it enables operations such as addition, subtraction, and multiplication.
We can separate text embeddings using below parameters
- Multi-lingual
- Context window size - max passage size
- Model size - computing resource
- embedding vector length - storage resource
Embedding-based search is much more powerful than traditional keyword search, promising for high-quality content discovery and LLM augmentation.
Text embedding Notion
Text Embedding Models
Sentence embedding Methods
The 1950-2024 Text Embeddings Evolution Poster
Take part in celebrating the achievements of text embeddings and carry a piece of its legacy with you.
https://jina.ai/news/the-1950-2024-text-embeddings-evolution-poster/
What is embedding
Embeddings: What they are and why they matter
Embeddings are a really neat trick that often come wrapped in a pile of intimidating jargon. If you can make it through that jargon, they unlock powerful and exciting techniques …
https://simonwillison.net/2023/Oct/23/embeddings/
Sentence embedding
In natural language processing, a sentence embedding refers to a numeric representation of a sentence in the form of a vector of real numbers which encodes meaningful semantic information.
https://en.wikipedia.org/wiki/Sentence_embedding
[딥러닝] 언어모델, RNN, GRU, LSTM, Attention, Transformer, GPT, BERT 개념 정리
언어모델에 대한 기초적인 정리
![[딥러닝] 언어모델, RNN, GRU, LSTM, Attention, Transformer, GPT, BERT 개념 정리](https://static.velog.io/favicons/apple-icon-152x152.png){kind=icon}
https://velog.io/@rsj9987/딥러닝-용어정리
![[딥러닝] 언어모델, RNN, GRU, LSTM, Attention, Transformer, GPT, BERT 개념 정리](https://images.velog.io/velog.png)
Embedding vs Encoding
Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models · Explosion Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. The new approach can be summarised as a simple four-step formula: embed, encode, attend, predict. This post explains the components of this explosion.ai Embedding과 Encoding의 개념..
https://beausty23.tistory.com/223
