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.
3
9 commits
1 linked in READMEs
updated May 28, 2021
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 pygaggle.ai!
Paper describing the model: Document Ranking with a Pretrained Sequence-to-Sequence Model
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.
3
9 commits
1 linked in READMEs
updated May 28, 2021
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 pygaggle.ai!
Paper describing the model: Document Ranking with a Pretrained Sequence-to-Sequence Model
