Embeddings are numerical vector representations of data (such as words, sentences, or documents) that capture their semantic meaning. In machine learning and natural language processing, embeddings enable algorithms to understand and compare data based on context and similarity, rather than relying on raw text or symbols.

Features of Embeddings:

• Transform text or other inputs into fixed-size vectors.
• Enable semantic similarity search, clustering, and classification.
• Power many NLP applications like search engines, chatbots, and recommendation systems.
• Commonly generated using models like BERT, RoBERTa, Word2Vec, or FastText.
• Used with machine learning frameworks like PyTorch, TensorFlow, and libraries like sentence-transformers.
• Essential in building modern AI systems for language understanding and representation.

To check the installed version of an embedding library like sentence-transformers, run these commands in your environment:

$ pip show transformers
 $ pip show sentence-transformers 
 $ pip show torch

Disclaimer: Embedding models and libraries are open-source and developed by the AI community. While widely adopted, they should be used with an understanding of their limitations, biases, and resource requirements. Refer to official documentation for guidance. The developers are not liable for any issues resulting from misuse. Use responsibly.