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---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
**Reconstruction** of tsAspire 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 tsAspire and represents the papers proposed multi-vector model for fine-grained scientific document similarity.
Refer to https://huggingface.co/allenai/aspire-contextualsentence-singlem-biomed for more details and usage.
## 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.)
Below results for the tsAspire fine-tuned on Specter2-base which outperform both.
| Model | TRECCOVID-MAP | TRECCOVID-NDCG%20 | RELISH-MAP | RELISH-NDCG%20 |
|--------------------------------|----------|----------|----------|----------|
| specter | 28.24 | 59.28 | 60.62 | 77.2 |
| aspire-contextualsentence-singlem-biomed* | 26.24 | 56.55 | 61.29 | 77.89 |
| aspire-contextualsentence-singlem-biomed | 26.68 | 57.21 | 61.06 | 77.70 |
| **ts-aspire-biomed-recon** | 29.26 | 60.45 | 62.2 | 78.7 |
| ts-aspire-biomed-specter2 | 31.16 | 62.43 | 63.24 | 79.89 |
|