This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.

This model was converted from the Tensorflow model gtr-xxl-1 to PyTorch. When using this model, have a look at the publication: Large Dual Encoders Are Generalizable Retrievers. The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.

The model uses only the encoder from a T5-11B model. The weights are stored in FP16.

Using this model becomes easy when you have sentence-transformers installed:

Then you can use the model like this:

The model requires sentence-transformers version 2.2.0 or newer.

If you find this model helpful, please cite the respective publication: Large Dual Encoders Are Generalizable Retrievers