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--- |
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language: "`en`" |
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thumbnail: "https://camo.githubusercontent.com/7d080b7a769f7fdf64ac0ebeb47b039cb50be35287e3071f9d633f0fe33e7596/68747470733a2f2f692e6962622e636f2f33544331576d472f737065637465722d6c6f676f2d63726f707065642e706e67" |
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license: "apache 2.0" |
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datasets: |
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- SciDocs |
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metrics: |
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- F1 |
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- accuracy |
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- map |
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- ndcg |
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--- |
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## SPECTER |
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SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on a a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. |
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Paper: [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://arxiv.org/pdf/2004.07180.pdf) |
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Original Repo: [Github](https://github.com/allenai/specter) |
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Evaluation Benchmark: [SciDocs](https://github.com/allenai/scidocs) |
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Authors: *Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld* |
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