Sentence Transformers
A Python framework for computing and training state-of-the-art text embeddings, rerankers, and sparse encoders.
What is Sentence Transformers?
Sentence Transformers is a Python framework for generating and working with text embeddings. It provides tools to compute dense and sparse vector representations of sentences, train custom embedding models, and perform tasks like semantic search and text similarity. The framework simplifies access to state-of-the-art models, enabling developers to build advanced NLP applications without deep expertise in model training.
Data scientists, ML engineers, and developers building NLP applications such as search systems, recommendation engines, or content analysis tools. It's particularly useful for those needing efficient sentence-level representations.
Developers choose Sentence Transformers for its comprehensive model library, ease of use, and flexibility. It offers a unified interface for embedding, reranking, and sparse encoding tasks, along with robust training capabilities, making it a one-stop solution for sentence embedding needs.
Overview
State-of-the-Art Text Embeddings
Use Cases
Best For
- Building semantic search engines over large text corpora
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