Check our SIGIR2021 short paper: https://dl.acm.org/doi/10.1145/3404835.3463048

This checkpoint is a variant of monot5 (T5 pointwise re-ranking model). 
Specifically, we fuse the "P2Q (i.e. doc2query)" and "Rank (i.e. passage ranking)" to learn the **discriminative** view (Rank) and **geneartive** view (P2Q). 

We found that under the specific **mixing ratio** of these two task, the effectiveness of passage re-ranking improves on par with monot5-3B models.
Hence, you can try to do both the task with this checkpoint by the following input format:
- P2Q: Document: *\<here is a document or a passage\>* Translate Document to Query:
- Rank: Query: *\<here is a query\>* Document: *\<here is a document or a passage\>* Relevant: 

which the outputs will be like:
- P2Q: *\<relevant query of the given text\>*
- Rank: *true* or *false*

```
Note that we usually use the logit values of *true*/ *false* token from T5 reranker as our query-passage relevant scores
Note the above tokens are all case-sensitive.
```
{}
transformers pytorch t5 text2text-generation autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Check our SIGIR2021 short paper: URL

This checkpoint is a variant of monot5 (T5 pointwise re-ranking model). 
Specifically, we fuse the "P2Q (i.e. doc2query)" and "Rank (i.e. passage ranking)" to learn the discriminative view (Rank) and geneartive view (P2Q). 

We found that under the specific mixing ratio of these two task, the effectiveness of passage re-ranking improves on par with monot5-3B models.
Hence, you can try to do both the task with this checkpoint by the following input format:
- P2Q: Document: *\<here is a document or a passage\>* Translate Document to Query:
- Rank: Query: *\<here is a query\>* Document: *\<here is a document or a passage\>* Relevant: 

which the outputs will be like:
- P2Q: *\<relevant query of the given text\>*
- Rank: *true* or *false*


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

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