Popular frameworks for creating embeddings include TensorFlow, PyTorch, and Hugging Face Transformers. These libraries provide tools for building and training neural networks that generate embeddings for various data types, such as text, images, and audio.
Specialized libraries like FastText and Gensim focus on word embeddings, offering pre-trained models and easy-to-use interfaces for tasks like text similarity and sentiment analysis. Frameworks like Faiss and Milvus are designed for efficient indexing and retrieval of embeddings in vector search applications.
For multimodal embeddings, libraries like OpenAI’s CLIP and DeepMind’s Perceiver provide models that can handle multiple data types simultaneously. These frameworks are widely adopted in applications like recommendation systems, semantic search, and cross-modal retrieval.