This model is a T5-base 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 jax t5 feature-extraction endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #feature-extraction #endpoints_compatible #has_space #text-generation-inference #region-us 

This model is a T5-base 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 #jax #t5 #feature-extraction #endpoints_compatible #has_space #text-generation-inference #region-us 

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