- Python Libraries
- Open Source
- Embeddings, Semantic Search, NLP, Reranker, Sparse Embeddings
- Added: Oct 04, 2025
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Sentence Transformers
Sentence Transformers is a powerful Python library for generating text embeddings and performing semantic search, enabling efficient natural language understanding.
Sentence Transformers is a Python module for accessing, using, and training state-of-the-art embedding and reranker models. It simplifies computing embeddings for semantic textual similarity, semantic search, and paraphrase mining. The library offers over 10,000 pre-trained models from Hugging Face, including top models from the MTEB leaderboard, and allows easy training of custom models for specific use cases.
This tool unlocks a wide range of applications by providing efficient neural lexical search and hybrid retrieval capabilities. Benefits include faster inference, dimensionality reduction, and model distillation for speed-performance trade-offs. It's ideal for developers, researchers, and data scientists working with natural language processing, semantic search, and information retrieval systems.
This tool unlocks a wide range of applications by providing efficient neural lexical search and hybrid retrieval capabilities. Benefits include faster inference, dimensionality reduction, and model distillation for speed-performance trade-offs. It's ideal for developers, researchers, and data scientists working with natural language processing, semantic search, and information retrieval systems.
- Compute text embeddings using pre-trained Sentence Transformer models.
- Calculate similarity scores with Cross-Encoder reranker models.
- Generate sparse embeddings using Sparse Encoder models for efficient search.
- Train or finetune custom embedding, reranker, or sparse encoder models.
- Integrate with vector databases like Elasticsearch and OpenSearch.
- Hugging Face Transformers
- Gensim
- spaCy
