readme: Paper: [Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study](https://arxiv.org/abs/2212.10233)
  
  ```
  @article{https://doi.org/10.48550/arxiv.2212.10233,
    doi = {10.48550/ARXIV.2212.10233},
    url = {https://arxiv.org/abs/2212.10233},
    author = {Wu, Di and Ahmad, Wasi Uddin and Chang, Kai-Wei},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study},
    publisher = {arXiv},
    year = {2022}, 
    copyright = {Creative Commons Attribution 4.0 International}
  }
  ```
  
  Pre-training Corpus: [RealNews](https://github.com/rowanz/grover/tree/master/realnews)
  
  Pre-training Details:
  - Resume from facebook/bart-base
  - Batch size: 2048
  - Total steps: 250k
  - Learning rate: 3e-4
  - LR schedule: polynomial with 10k warmup steps
  - Masking ratio: 30%, Poisson lambda = 3.5