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# Evaluation
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We perform an evaluation on 9 datasets from the [BEIR benchmark](https://github.com/beir-cellar/beir) that none of the evaluated models have been trained upon (to our knowledge).
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We evaluate on a subset of all queries (the first 250) to save evaluation time.
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We find that our model performs similarly or better than many of the state-of-the-art reranker models in our evaluation, without compromising on inference speed.
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We make our evaluation code and results available [on our Github](https://github.com/lightblue-tech/lb-reranker/blob/main/run_bier.ipynb).
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 that none of the evaluated models have been trained upon (to our knowledge).
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* Arguana
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* Dbpedia-entity
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* Fiqa
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* NFcorpus
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* Scidocs
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* Scifact
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* Trec-covid-v2
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* Vihealthqa
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* Webis-touche2020
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We evaluate on a subset of all queries (the first 250) to save evaluation time.
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We find that our model performs similarly or better than many of the state-of-the-art reranker models in our evaluation, without compromising on inference speed.
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We make our evaluation code and results available [on our Github](https://github.com/lightblue-tech/lb-reranker/blob/main/run_bier.ipynb).
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# License
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