Sentence Transformers
An open-source sentence embedding framework based on BERT and RoBERTa, trained using a siamese network structure. It is designed for semantic similarity computation and sentence-level embedding generation, and is widely used in semantic search and retrieval-augmented generation (RAG) applications.
Timeline (last 90 days)
When using the paraphrase-multilingual-MiniLM-L12-v2 model for semantic retrieval, for the query 'Where is the kitten?', 'The cat sat on the mat.' scores 0.560 and ranks first, the Spanish sentence 'El gato duerme en la alfombra.' scores 0.480 and ranks second, and the two sentences share no words at all.
paraphrase-multilingual-MiniLM-L12-v2 is a multilingual sentence embedding model provided by the Sentence Transformers library; it compresses the output of a 12-layer Transformer encoder into a 384-dimensional vector through mean pooling and supports about 50 languages.
Sentence Transformers is a framework that performs contrastive learning fine-tuning on top of pretrained models such as BERT and XLM-R, with the training goal of bringing the vectors of semantically similar sentences closer to each other.
The model's weights are automatically downloaded by encode() on first use.
Vectors generated by OpenAI, Google, and various open-source models (such as BERT and Sentence Transformers) differ in dimensionality, numerical distribution, and semantic arrangement.
All Facts (5)
When using the paraphrase-multilingual-MiniLM-L12-v2 model for semantic retrieval, for the query 'Where is the kitten?', 'The cat sat on the mat.' scores 0.560 and ranks first, the Spanish sentence 'El gato duerme en la alfombra.' scores 0.480 and ranks second, and the two sentences share no words at all.
50%Unverifiedparaphrase-multilingual-MiniLM-L12-v2 is a multilingual sentence embedding model provided by the Sentence Transformers library; it compresses the output of a 12-layer Transformer encoder into a 384-dimensional vector through mean pooling and supports about 50 languages.
50%UnverifiedSentence Transformers is a framework that performs contrastive learning fine-tuning on top of pretrained models such as BERT and XLM-R, with the training goal of bringing the vectors of semantically similar sentences closer to each other.
50%UnverifiedThe model's weights are automatically downloaded by encode() on first use.
50%UnverifiedVectors generated by OpenAI, Google, and various open-source models (such as BERT and Sentence Transformers) differ in dimensionality, numerical distribution, and semantic arrangement.
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