This model is a T5-3B reranker fine-tuned on the MS MARCO passage dataset for 10k steps (or 1 epoch).

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/)

This model is also the state of the art on the BEIR Benchmark.
- Paper: [No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval](https://arxiv.org/abs/2206.02873)
- [Repository](https://github.com/guilhermemr04/scaling-zero-shot-retrieval)

{}
transformers pytorch t5 text2text-generation arxiv:2206.02873 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2206.02873 #autotrain_compatible #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).

For more details on how to use it, check URL

Paper describing the model: Document Ranking with a Pretrained Sequence-to-Sequence Model

This model is also the state of the art on the BEIR Benchmark.
- Paper: No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
- Repository

TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2206.02873 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

passage: TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2206.02873 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
