| 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 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* | |