 Evaluation results  The released model `aspire-contextualsentence-multim-biomed` is compared against `allenai/specter`. `aspire-contextualsentence-multim-biomed`<sup>*</sup> is the performance reported in our paper by averaging over 3 re-runs of the model. The released models `aspire-contextualsentence-multim-biomed` is the single best run among the 3 re-runs.  |                                            | TRECCOVID  |    TRECCOVID    | RELISH  |     RELISH   | |-------------------------------------------:|:---------:|:-------:|:------:|:-------:| |                                            |    MAP    | NDCG%20 |   MAP  | NDCG%20 | |                               `specter`    |  28.24    |  59.28  |  60.62 |  77.20  | | `aspire-contextualsentence-multim-biomed`<sup>*</sup> |   30.92  |  62.23   |  62.57  |  78.95  | | `aspire-contextualsentence-multim-biomed` |   31.25  |    62.99 |   62.24 |  78.65 |     **Alternative models:**  Besides the above models consider these alternative models also released in the Aspire paper:  [`aspire-contextualsentence-multim-compsci`](https://huggingface.co/allenai/aspire-contextualsentence-multim-compsci): If you wanted to run on computer science papers and want to use a model trained to match _multiple_ sentences between documents.  [`aspire-contextualsentence-singlem-biomed`](https://huggingface.co/allenai/aspire-contextualsentence-singlem-biomed): If you wanted to run on biomedical papers and want to use a model trained to match _single_ sentences between documents.  [`aspire-contextualsentence-singlem-compsci`](https://huggingface.co/allenai/aspire-contextualsentence-singlem-compsci): If you wanted to run on computer science papers and want to use a model trained to match _single_ sentences between documents.