
This model uses two BERT models fine-tuned on a contrastive learning objective.
One is responsable for short queries, and the other for longer documents that contain the answer to the query.
After encoding many documents, one may perform a nearest neighbor search with the query encoding, to fetch
the most relevant document.

Take a look at inference.py to see how to perform inference.
{"language": ["en"], "datasets": ["BeIR/msmarco"]}
en dataset:BeIR/msmarco region:us
TAGS
#en #dataset-BeIR/msmarco #region-us 


This model uses two BERT models fine-tuned on a contrastive learning objective.
One is responsable for short queries, and the other for longer documents that contain the answer to the query.
After encoding many documents, one may perform a nearest neighbor search with the query encoding, to fetch
the most relevant document.

Take a look at URL to see how to perform inference.
TAGS
#en #dataset-BeIR/msmarco #region-us 

TAGS
#en #dataset-BeIR/msmarco #region-us 
