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This is the dataset for COVID-19 document type screening. It is composed of: - Epistemonikos train dataset - CORD-19 test dataset adapted for Evidence Based Medicine domain - XLNET model fine-tuned on Epistemonikos dataset. - BioBERT model fine-tuned on Epistemonikos dataset. Epistemonikos XLNET models fine-tuned on Cord-19: - Episte-XLNET fine-tuned with random sampling strategy. - Episte-XLNET fine-tuned with data augmentation strategy. - Episte-XLNET fine-tuned with uncertainty sampling strategy (iteration 1 and 2). Scripts to run experiments can be found at: https://github.com/afcarvallo/covid_19_document_type_screening
user evaluation, evidence based medicine, neural language models, natural language processing
user evaluation, evidence based medicine, neural language models, natural language processing
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 46 | |
| downloads | 17 |

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