: Note that this specific model has a maximum sequence length of 512 tokens .
: This model is optimized for speed and is a pragmatic choice for basic vector stores, though newer models may offer better context handling. nL6.rar
from sentence_transformers import SentenceTransformer # Load the model model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') # Define your text data sentences = ["Developing text processing tools is efficient.", "NLP models convert text into numerical vectors."] # Generate embeddings embeddings = model.encode(sentences) # The embeddings can now be used for semantic similarity or search print(embeddings) Use code with caution. Copied to clipboard Key Considerations : Note that this specific model has a