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Here the models described in the publication "PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval" are uploaded. The models are the DPR models denoted as LegalBERT_doc and LegalBERT_para in the paper. The dense retriever models can be loaded with the DPR libary from Facebook Research (https://github.com/facebookresearch/DPR). LegalBERT_para is the dense passage retrieval encoder based on LegalBERT and trained on the paragraph-level labels of COLIEE Task 2 data. LegalBERT_doc is the dense passage retrieval encoder based on LegalBERT and trained on the paragraph-level and document-level labels of COLIEE Task 1&2 data.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
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| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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