readme: ---
  language: en
  thumbnail: "https://camo.githubusercontent.com/7d080b7a769f7fdf64ac0ebeb47b039cb50be35287e3071f9d633f0fe33e7596/68747470733a2f2f692e6962622e636f2f33544331576d472f737065637465722d6c6f676f2d63726f707065642e706e67"
  license: apache-2.0
  datasets:
  - SciDocs
  metrics:
  - F1
  - accuracy
  - map
  - ndcg
  ---
  
  ## SPECTER
  
  SPECTER is a pre-trained language model to generate document-level embedding of documents. It is pre-trained on 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. 
  
  Paper: [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://arxiv.org/pdf/2004.07180.pdf)
  
  Original Repo: [Github](https://github.com/allenai/specter)
  
  Evaluation Benchmark: [SciDocs](https://github.com/allenai/scidocs)
  
  Authors: *Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel S. Weld*
  
  