This model is a T5-3B reranker fine-tuned on the MS MARCO passage dataset for 10K steps (or 1 epoch) and then fine-tuned again on MedMARCO (from [Sledge-Z paper](https://www.aclweb.org/anthology/2020.emnlp-main.341.pdf)) for 1K steps.

For more details on how to use it, check [pygaggle.ai](pygaggle.ai)!

Paper describing the model: [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://www.aclweb.org/anthology/2020.findings-emnlp.63/)
{}
transformers pytorch t5 feature-extraction endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #feature-extraction #endpoints_compatible #has_space #text-generation-inference #region-us 

This model is a T5-3B reranker fine-tuned on the MS MARCO passage dataset for 10K steps (or 1 epoch) and then fine-tuned again on MedMARCO (from Sledge-Z paper) for 1K steps.

For more details on how to use it, check URL!

Paper describing the model: Document Ranking with a Pretrained Sequence-to-Sequence Model
TAGS
#transformers #pytorch #t5 #feature-extraction #endpoints_compatible #has_space #text-generation-inference #region-us 

passage: TAGS
#transformers #pytorch #t5 #feature-extraction #endpoints_compatible #has_space #text-generation-inference #region-us 
