Sentence Transformers

Sentence Transformers offers a powerful Python module for creating and using advanced embedding and reranker models. Compute embeddings with Sentence Transformer models, calculate similarity scores using Cross-Encoder models, or generate sparse embeddings with Sparse Encoder models. These capabilities support a wide range of applications, from semantic search to text classification.

With a variety of models available on Hugging Face, you can easily train or fine-tune models to suit your specific needs. Installation is simple with pip, and the library is compatible with Python 3.10+ and PyTorch 1.11.0+.

Here's a quick example:

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
sentences = ["The weather is lovely today.", "It's so sunny outside!"]
embeddings = model.encode(sentences)

Stay updated with the latest features, including multi-processing for CrossEncoder and multilingual evaluators. The project is maintained by Hugging Face, ensuring robust support and development. For more details, visit the official documentation.