huggingface/sentence-transformers
State-of-the-Art Embeddings, Retrieval, and Reranking
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문서
- Loss OverviewThe <a href="../package_reference/sparse_encoder/losses.html#spladeloss"><code>SpladeLoss</code></a> implements a specialized loss function for SPLADE (Sparse Lexical and Expansion) models. It combin…docs/sparse_encoder/loss_overview.md
- Pretrained ModelsMS MARCO Passage Retrieval serves as the gold standard dataset, featuring authentic user queries from Bing search engine paired with expertly annotated relevant text passages. Models trained on this…docs/sparse_encoder/pretrained_models.md
- Training OverviewFinetuning Sparse Encoder 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:docs/sparse_encoder/training_overview.md