Densifying Sparse Representations for Passage Retrieval by Representational Slicing
Paper • 2112.04666 • Published
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Check out the documentation for more information.
This model, (DeLADE+[CLS])+, is trained by fusing neural lexical and semantic components in single transformer using DistilBERT as a backbone using hard negative mining and knowledge distillation with ColBERT teacher, which is detailed in the below paper.
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.
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# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jacklin/DeLADE-CLS-P", dtype="auto")