huggingface/sentence-transformers
State-of-the-Art Embeddings, Retrieval, and Reranking
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- Loss OverviewLoss functions play a critical role in the performance of your fine-tuned Cross Encoder model. Sadly, there is no "one size fits all" loss function. Ideally, this table should help narrow down your c…docs/cross_encoder/loss_overview.md
- Pretrained ModelsCross-Encoders require pairs as inputs and output a score (0 to 1 if the Sigmoid activation function is used). Most models work with text pairs, but some also support non-text inputs such as images (…docs/cross_encoder/pretrained_models.md
- Training OverviewCross Encoder models are very often used as 2nd stage rerankers in a Retrieve and Rerank search stack. In such a situation, the Cross Encoder reranks the top X candidates from the retriever (which ca…docs/cross_encoder/training_overview.md