This model is a T5-base pairwise reranker fine-tuned on MS MARCO passage dataset for 50k steps (or 5 epochs).

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

Paper describing the model: [The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models](https://arxiv.org/pdf/2101.05667.pdf)
{}
transformers pytorch jax t5 text2text-generation arxiv:2101.05667 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #arxiv-2101.05667 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

This model is a T5-base pairwise reranker fine-tuned on MS MARCO passage dataset for 50k steps (or 5 epochs).

For more details on how to use it, check URL

Paper describing the model: The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #arxiv-2101.05667 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

passage: TAGS
#transformers #pytorch #jax #t5 #text2text-generation #arxiv-2101.05667 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
