
Sentence Transformers
Empowering Semantic Understanding

Sentence Transformers is a Python module that provides access to state-of-the-art embedding and reranker models. It allows users to compute embeddings, calculate similarity scores, and generate sparse embeddings for various applications such as semantic search, semantic textual similarity, and paraphrase mining. The module offers a wide selection of pre-trained models and enables users to train or finetune their own models for specific use cases.
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Features
- Compute embeddings using Sentence Transformer models
- Calculate similarity scores using Cross-Encoder models
- Generate sparse embeddings using Sparse Encoder models
- Access over 10,000 pre-trained models from Hugging Face
- Train or finetune custom models for specific applications
Advantages
- Wide range of applications including semantic search and textual similarity
- Access to a variety of pre-trained models for immediate use
- Ability to train or finetune custom models for specific use cases
- Efficient computation of embeddings and similarity scores
- Supported by UKPLab and maintained by Hugging Face
Disadvantages
- Requires Python 3.9+ and PyTorch 1.11.0+ for installation
- Complexity in training custom models for beginners
- Limited documentation for advanced usage scenarios
Frequently Asked Questions
-
Q:What can I do with Sentence Transformers?
A:You can compute embeddings, calculate similarity scores, and generate sparse embeddings for various applications. -
Q:How can I access pre-trained models?
A:You can access over 10,000 pre-trained models from Hugging Face for immediate use. -
Q:Can I train my own custom models?
A:Yes, you can train or finetune custom models for specific use cases.
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