04.6.11 AI Text Embeddings
Embeddings are widely used in modern language models and natural language processing systems. Here are some of the main applications of text embeddings:
- Text analysis and classification: representing words and text as vectors allows efficient application of machine learning algorithms for text classification tasks, topic modeling, tone detection, etc.
- Information retrieval and recommendation: text embeddings are used to compute semantic proximity between documents, queries and content, which is important for search engines, recommendation services and other applications.
- Improving deep learning models: using textual embeddings as inputs to deep learning models can often improve their performance, especially in the presence of limited labeled data.
The bge-base-en-v1.5, bge-large-en-v1. 5 and bge-small-en-v1.5 models are part of the Generalized Embedding or BGE family of models from BAAI (Beijing Academy of Artificial Intelligence). The main differences between them are model size and performance:
- bge-base-en-v1.5 is the basic version of the Generalized Embedding model. It has a smaller size and lower performance than bge-large-en-en-v1.5. It is suitable for applications where less memory or faster execution time is required.
- bge-large-en-v1.5 is a larger version of the generic embedding model. It has a larger size and higher performance than bge-base-en-v1.5. Suitable for applications where higher accuracy or complex tasks are required.
- The bge-small-en-v1.5 is a smaller version of the general embedding model. It has a smaller size and lower performance than bge-base-en-v1.5. It is suitable for applications where minimal memory is required, such as on mobile devices or limited computing systems.
The AI: Text Embeddings node group includes the nodes: