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SPECTER-CoCite model included in a paper for modeling fine-grained similarity between documents in the biomedical domain.
Title: "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
Authors: Sheshera Mysore, Arman Cohan, Tom Hope
Paper: https://arxiv.org/abs/2111.08366
Github: https://github.com/allenai/aspire
Note: In the context of the paper, this model is referred to as Specter-CoCite_Spec and represents a baseline bi-encoder for scientific document similarity. This model is similar in architecture to the allenai/specter model but is trained on co-citation data instead of citation data.
Refer to https://huggingface.co/allenai/aspire-biencoder-biomed-spec for more details and usage. Base Model: https://huggingface.co/allenai/specter2_base
Evaluation Results
Differences might be in co-citations training data which constructed from scratch from different release of S2ORC (originaly, 2019-09-28, which I didn't have access to.)
Model | TRECCOVID-MAP | TRECCOVID-NDCG%20 | RELISH-MAP | RELISH-NDCG%20 |
---|---|---|---|---|
specter | 28.24 | 59.28 | 60.62 | 77.20 |
aspire-biencoder-biomed-spec | 26.07 | 54.89 | 61.47 | 78.34 |
aspire-biencoder-biomed-spec-full | 28.87 | 60.47 | 61.69 | 78.22 |
aspire-biencoder-biomed-spec-recon | 27.73 | 59.18 | 60.36 | 77.09 |
aspire-biencoder-biomed-spec2 | 29.38 | 61.45 | 60.26 | 77.25 |
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