This model, DeLADE+[CLS], is trained by fusing neural lexical and semantic components in single transformer using DistilBERT as a backbone. 
*[A Dense Representation Framework for Lexical and Semantic Matching](https://arxiv.org/pdf/2112.04666.pdf)* Sheng-Chieh Lin and Jimmy Lin. 

You can find the usage of the model in our [DHR repo](https://github.com/jacklin64/DHR): (1) [Inference on MSMARCO Passage Ranking](https://github.com/castorini/DHR/blob/main/docs/msmarco-passage-train-eval.md); (2) [Inference on BEIR datasets](https://github.com/castorini/DHR/blob/main/docs/beir-eval.md).

{}
transformers pytorch arxiv:2112.04666 endpoints_compatible region:us
TAGS
#transformers #pytorch #arxiv-2112.04666 #endpoints_compatible #region-us 

This model, DeLADE+[CLS], is trained by fusing neural lexical and semantic components in single transformer using DistilBERT as a backbone. 
*A Dense Representation Framework for Lexical and Semantic Matching* Sheng-Chieh Lin and Jimmy Lin. 

You can find the usage of the model in our DHR repo: (1) Inference on MSMARCO Passage Ranking; (2) Inference on BEIR datasets.

TAGS
#transformers #pytorch #arxiv-2112.04666 #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #arxiv-2112.04666 #endpoints_compatible #region-us 
