UKPLab/sentence-transformers
Sentence Transformers
This project is a framework for training and deploying transformer-based models that map text, images, audio, and video into dense or sparse vector representations. It functions as a multimodal embedding library and semantic search engine used to retrieve relevant documents by calculating vector similarity between meanings.
The framework provides specialized tools for both cross-encoder reranking, which calculates precise similarity scores to refine search results, and vector quantization to compress embedding vectors for reduced memory usage and increased retrieval speed.
The project covers broad capability areas including neural embedding training, semantic retrieval, and the generation of dense and sparse embeddings. It also supports information retrieval optimization through hybrid search implementations and the fine-tuning of transformer networks.
Features
- Embedding Generators - Provides a comprehensive framework for generating dense and sparse vector embeddings from text using transformer models.
- Sentence Embeddings - Maps sentences and paragraphs to dense vector spaces for high-performance semantic search and clustering.
- Contrastive Learning Models - Provides contrastive loss functions to optimize the distance between similar and dissimilar sentence pairs in vector space.
- Embedding Frameworks - Provides a complete framework for training and deploying transformer-based embedding models for text and multimodal data.
- Cross-Encoders - Includes cross-encoder models for calculating precise semantic similarity scores between queries and documents.
- Embedding Model Fine-Tuning - Enables fine-tuning of transformer-based embedding models to create specialized representations for specific domains.
- Multimodal Embedding Models - Maps text, images, audio, and video into a shared vector space for cross-modal similarity analysis.
- Siamese Networks - Uses siamese-network architectures to train identical transformers for similarity comparison.
- Semantic Search Engines - Functions as a semantic search engine that retrieves documents based on conceptual meaning and vector similarity.
- Dual-Encoder Architectures - Implements dual-encoder architectures to map queries and documents into a shared vector space for fast similarity search.
- Dense Embeddings - Generates dense vector representations for text, images, audio, and video to enable semantic similarity analysis.
- Sparse Embeddings - Produces sparse vector representations of text to support efficient keyword-based and hybrid information retrieval.
- Semantic Search Engines - Implements a semantic search engine for retrieving documents by calculating the vector similarity of their meanings.
- Semantic Information Retrieval - Performs semantic retrieval of the most relevant passages from large corpora using vector-based similarity.
- Document Rerankers - Ships specialized document rerankers that use cross-encoder architectures to improve search relevance.
- Result Reranking - Provides result reranking capabilities using cross-encoders to refine the precision of retrieved documents.
- Hybrid Search Engines - Implements hybrid search by combining dense vector embeddings with sparse keyword-based indexing.
- Vector Quantization - Offers vector quantization methods to compress high-dimensional embeddings and reduce memory usage during retrieval.
- Embedding Models - State-of-the-art library for generating text embeddings.
- Language Model Development - Library for computing dense vector representations of text.
- Language Modeling - Siamese BERT networks for generating sentence embeddings.
- Natural Language Processing - Framework for sentence and text embeddings.
- Semantic Text Matching - Siamese BERT networks for efficient semantic similarity and clustering.
- Deep learning models and transformers - Listed in the “Deep learning models and transformers” section of the Awesome Linguistics awesome list.
Star history
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