This model is to reproduce the TCT-ColBERT dense retrieval described in the following paper:

> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. [Distilling Dense Representations for Ranking using Tightly-Coupled Teachers.](https://arxiv.org/abs/2010.11386) arXiv:2010.11386, October 2020. 

For more details on how to use it, check our experiments in [Pyserini](https://github.com/castorini/pyserini/blob/master/docs/experiments-tct_colbert.md)

{}
transformers pytorch arxiv:2010.11386 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #arxiv-2010.11386 #endpoints_compatible #has_space #region-us 

This model is to reproduce the TCT-ColBERT dense retrieval described in the following paper:

> Sheng-Chieh Lin, Jheng-Hong Yang, and Jimmy Lin. Distilling Dense Representations for Ranking using Tightly-Coupled Teachers. arXiv:2010.11386, October 2020. 

For more details on how to use it, check our experiments in Pyserini

TAGS
#transformers #pytorch #arxiv-2010.11386 #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #arxiv-2010.11386 #endpoints_compatible #has_space #region-us 
