msmarco bert base dot v5 This is a sentence transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search . It has been trained on 500K (query, answer) pairs from the MS MARCO dataset. For an introduction to semantic search, have a look at: SBERT.net Semantic Search Usage (Sentence Transformers) Using this model becomes easy when you have sentence transformers installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without sentence transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling operation on top of the contextualized word embeddings. Technical Details In the following some technical details how this model must be used: Setting Value : : Dimensions 768 Max Sequence Length 512 Produces normalized embeddings No Pooling Method Mean pooling Suitable score functions dot product (e.g. util.dot score ) Training See train script.py in this repository for the used training script. The model was trained with the parameters: DataLoader : torch.utils.data.dataloader.DataLoader of length 7858 with parameter…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy