RichJackson/pytorch-transformers: supporting ablation paper v3

@inproceedings{Wolf2020RichJacksonpytorchtransformersSA,
  title={RichJackson/pytorch-transformers: supporting ablation paper v3},
  author={Thomas Wolf and Lysandre Debut and Victor Sanh and Denis and erenup and Matt and Julien Chaumond and Gr{\'e}gory Ch{\^a}tel and Tim Rault and Catalin Voss and Fei Wang and D. Fiocco and Malte Pietsch and A. Jha and Stefan Schweter and Dhanajit Brahma and Guillem Garc{\'i}a Subies and Shijie Wu and Yongbo Wang and yzy and Jade Abbott and Joel Grus and Nikolay Korolev and Liu Chi-liang and Zeyao Du and Weixin Wang and Abhishek Rao and Ailing and Clement and matej-svejda},
  year={2020}
}
1 Citations

Ablations over transformer models for biomedical relationship extraction

TLDR
A recent general domain pretrained model performs approximately the same as a biomedical specific one, suggesting that domain specific models may be of limited use given the tendency of recent model pretraining regimes to incorporate ever broader sets of data.