Sheshera Mysore
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Small changes.
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README.md
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**`aspire-biencoder-biomed-scib-full`**, can be used as follows: 1) Download the [`aspire-biencoder-biomed-scib-full.zip`](https://drive.google.com/file/d/1X6S5qwaKUlI3N3RDQSG-tJCzMBWAnqxP/view?usp=sharing), and 2) Use it per this example usage script: [`aspire/examples/ex_aspire_bienc.py`](https://github.com/allenai/aspire/blob/main/examples/ex_aspire_bienc.py)
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This model is evaluated on information retrieval datasets with document level queries. Here we report performance on RELISH, and TRECCOVID. These are detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). These datasets represent a abstract level retrieval task, where given a query scientific abstract the task requires the retrieval of relevant candidate abstracts.
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We rank documents by the L2 distance between the query and candidate documents.
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**`aspire-biencoder-biomed-scib-full`**, can be used as follows: 1) Download the [`aspire-biencoder-biomed-scib-full.zip`](https://drive.google.com/file/d/1X6S5qwaKUlI3N3RDQSG-tJCzMBWAnqxP/view?usp=sharing), and 2) Use it per this example usage script: [`aspire/examples/ex_aspire_bienc.py`](https://github.com/allenai/aspire/blob/main/examples/ex_aspire_bienc.py)
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### Variable and metrics
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This model is evaluated on information retrieval datasets with document level queries. Here we report performance on RELISH, and TRECCOVID. These are detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). These datasets represent a abstract level retrieval task, where given a query scientific abstract the task requires the retrieval of relevant candidate abstracts.
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We rank documents by the L2 distance between the query and candidate documents.
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