mteb/SciDocsRR
Possible licence conflict
The declared licence (cc-by-4.0) may conflict with a source dataset's reported share-alike licence (cc-by-sa-4.0).
Licence facts reported by the dataset and its declared sources. Not legal advice, and not a grant of permission to use the dataset.
Evidence
- Declared licence: cc-by-4.0 (from the tag)
- Licence files: no licence file at the top level of the repository
- Access gated on Hugging Face: no
Declared sources
- mteb/scidocs: cc-by-sa-4.0
Signal
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Tasks
Citation text from the card
@inproceedings{cohan-etal-2020-specter,
abstract = {Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, accurate embeddings of documents are a necessity. We propose SPECTER, a new method to generate document-level embedding of scientific papers based on pretraining a Transformer language model 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. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that Specter outperforms a variety of competitive baselines on the benchmark.},
address = {Online},
author = {Cohan, Arman and
Feldman, Sergey and
Beltagy, Iz and
Downey, Doug and
Weld, Daniel},
booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
doi = {10.18653/v1/2020.a
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