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  • SentenceTransformers Documentation — Sentence Transformers documentation
    Sentence Transformers (a k a SBERT) is the go-to Python module for accessing, using, and training state-of-the-art embedding and reranker models It can be used to compute embeddings using Sentence Transformer models or to calculate similarity scores using Cross-Encoder (a k a reranker) models
  • Quickstart — Sentence Transformers documentation - SBERT. net
    Sentence Transformer Characteristics of Sentence Transformer (a k a bi-encoder) models: Calculates a fixed-size vector representation (embedding) given texts or images Embedding calculation is often efficient, embedding similarity calculation is very fast
  • Pretrained Models — Sentence Transformers documentation - SBERT. net
    We provide various pre-trained Sentence Transformers models via our Sentence Transformers Hugging Face organization Additionally, over 6,000 community Sentence Transformers models have been publicly released on the Hugging Face Hub
  • Installation — Sentence Transformers documentation - SBERT. net
    There are 5 extra options to install Sentence Transformers: Default: This allows for loading, saving, and inference (i e , getting embeddings) of models ONNX: This allows for loading, saving, inference, optimizing, and quantizing of models using the ONNX backend
  • SentenceTransformer — Sentence Transformers documentation - SBERT. net
    Deprecated training method from before Sentence Transformers v3 0, it is recommended to use sentence_transformers trainer SentenceTransformerTrainer instead This method should only be used if you encounter issues with your existing training scripts after upgrading to v3 0+
  • Training Overview — Sentence Transformers documentation - SBERT. net
    Finetuning Sentence Transformer models often heavily improves the performance of the model on your use case, because each task requires a different notion of similarity For example, given news articles: “Apple launches the new iPad” “NVIDIA is gearing up for the next GPU generation”
  • Semantic Textual Similarity — Sentence Transformers documentation
    Sentence Transformers implements two methods to calculate the similarity between embeddings: SentenceTransformer similarity : Calculates the similarity between all pairs of embeddings SentenceTransformer similarity_pairwise : Calculates the similarity between embeddings in a pairwise fashion
  • Usage — Sentence Transformers documentation - SBERT. net
    Characteristics of Sentence Transformer (a k a bi-encoder) models: Calculates a fixed-size vector representation (embedding) given texts or images Embedding calculation is often efficient, embedding similarity calculation is very fast
  • Sentence Transformers: Multilingual Sentence, Paragraph, and Image . . .
    Sentence Transformers: Multilingual Sentence, Paragraph, and Image Embeddings using BERT Co ¶ This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images The models are based on transformer networks like BERT RoBERTa XLM-RoBERTa etc and achieve state-of-the-art performance in





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